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poolside/laguna-s-2.1

Mean 0.939 · 33/39 perfect tests · $0.27 total · 39/40 tests completed — remaining tests discarded from study (endpoint timeouts after dedicated retries) · each card: the question → the correct answer (gold) → this model's actual answer

data-crm
stage-hygiene-auditPASS 1.00

? The Question

Audit stage hygiene on the open pipeline below. Rules: a deal at DS2 or later must have a substantive why-buys (15+ characters, not a placeholder such as tbd, n/a, see notes). A why_buys value of the form LONG(n) means the real text was present but truncated for display; treat it as present and substantive. Every deal should have a DS1 entry timestamp. A deal whose stage entry timestamps are out of order regressed (an earlier-stage timestamp is later than a later-stage timestamp). Report: percent of DS2+ deals non-compliant on why-buys overall and by owner (use the owners table for names), count of deals with no DS1 timestamp, count that regressed, and three process fixes. State definitions before numbers.
cost $0.0221 · 495s · in 125,070 / out 38,146 tok
✓ noncomp_pct✓ no_ds1_0✓ regressed_0✓ per_owner_breakdown✓ definitions_first✓ three_fixes

✓ Correct Answer (ground truth)

noncomp_pct: 81.5
ds2plus: 124
justin_noncomp: 15
no_ds1: 0
regressed: 0
n_deals: 156
graded checks: noncomp_pct · no_ds1_0 · regressed_0 · per_owner_breakdown · definitions_first · three_fixes

Model's actual answer

show the model's full answer
All numbers computed. Now producing the compact final report with tiering JSON and process fixes.

---

## Stage Hygiene Audit — Definitions

**Substantive why_buys** (for DS2+ compliance): value is `LONG(n)` (truncated-but-present), or non-empty and not a placeholder (tbd / n/a / see notes, case-insensitive, trailing period stripped) and >=15 characters.

**DS2+**: stages DS2, DS3, DS4, DS5 (stage index >= 1).

**No DS1 timestamp**: `t_ds1` field is empty.

**Regressed**: an earlier-in-funnel stage-entry timestamp is later than a later-stage timestamp (chronologically out of order), considering only populated `t_ds1..t_ds5` stamps up to the deal's current stage.

---

## Results

### Why-buys compliance (DS2+ only)
| Metric | Value |
|---|---|
| DS2+ deals | 123 |
| DS2+ why_buys non-compliant | 100 |
| Percent non-compliant (overall) | 81.3% |

### By owner
| Owner | Non-compliant / DS2+ total | % |
|---|---|---|
| Bryce Harmon (119337721) | 15 / 18 | 83.3% |
| Alex Franklin (84342457) | 47 / 60 | 78.3% |
| Cole Ingram (83155924) | 19 / 22 | 86.4% |
| Dana Mercer (83155923) | 18 / 22 | 81.8% |
| Farid Osman (716654662) | 1 / 1 | 100.0% |

### Timestamp hygiene
| Metric | Count |
|---|---|
| Deals with no DS1 timestamp | 0 |
| Deals that regressed | 0 |

### Tiered list of the 100 non-compliant DS2+ deals (why_buys_compliant=false)
```json
[{"deal_alias":"Deal-25F752","owner":"Bryce Harmon","owner_id":"119337721","stage":"DS4","amount":24000.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-E53952","owner":"Bryce Harmon","owner_id":"119337721","stage":"DS4","amount":19656.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-5408B0","owner":"Alex Franklin","owner_id":"84342457","stage":"DS4","amount":14850.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-D348E1","owner":"Alex Franklin","owner_id":"84342457","stage":"DS5","amount":13770.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-C26D20","owner":"Bryce Harmon","owner_id":"119337721","stage":"DS5","amount":13500.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-547B2B","owner":"Alex Franklin","owner_id":"84342457","stage":"DS5","amount":11200.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-944310","owner":"Dana Mercer","owner_id":"83155923","stage":"DS4","amount":10500.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-403845","owner":"Alex Franklin","owner_id":"84342457","stage":"DS5","amount":9000.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-B7EBD1","owner":"Dana Mercer","owner_id":"83155923","stage":"DS5","amount":9000.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-3974EB","owner":"Dana Mercer","owner_id":"83155923","stage":"DS4","amount":9000.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-A2B47C","owner":"Alex Franklin","owner_id":"84342457","stage":"DS5","amount":6360.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-2465CE","owner":"Dana Mercer","owner_id":"83155923","stage":"DS5","amount":5400.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-C61CF7","owner":"Alex Franklin","owner_id":"84342457","stage":"DS5","amount":5400.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-62D607","owner":"Dana Mercer","owner_id":"83155923","stage":"DS4","amount":4800.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-C6D97A","owner":"Alex Franklin","owner_id":"84342457","stage":"DS4","amount":3240.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-7B3B0F","owner":"Cole Ingram","owner_id":"83155924","stage":"DS4","amount":2760.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-F9A08A","owner":"Alex Franklin","owner_id":"84342457","stage":"DS4","amount":2484.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-0660B4","owner":"Dana Mercer","owner_id":"83155923","stage":"DS4","amount":1920.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-1FC049","owner":"Alex Franklin","owner_id":"84342457","stage":"DS4","amount":1920.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-3974EB","owner":"Cole Ingram","owner_id":"83155924","stage":"DS5","amount":1330.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-499BF6","owner":"Farid Osman","owner_id":"716654662","stage":"DS2","amount":1249.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-BA571A","owner":"Alex Franklin","owner_id":"84342457","stage":"DS4","amount":1080.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-C6FE92","owner":"Bryce Harmon","owner_id":"119337721","stage":"DS3","amount":72000.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-D73B89","owner":"Bryce Harmon","owner_id":"119337721","stage":"DS2","amount":63600.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-40522D","owner":"Bryce Harmon","owner_id":"119337721","stage":"DS3","amount":21000.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-C9C286","owner":"Bryce Harmon","owner_id":"119337721","stage":"DS2","amount":5502.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-CA7DC0","owner":"Bryce Harmon","owner_id":"119337721","stage":"DS2","amount":8160.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-F0EBBB","owner":"Bryce Harmon","owner_id":"119337721","stage":"DS3","amount":11400.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-3795AD","owner":"Bryce Harmon","owner_id":"119337721","stage":"DS2","amount":1.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-332637","owner":"Bryce Harmon","owner_id":"119337721","stage":"DS2","amount":36000.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-7BBDFA","owner":"Bryce Harmon","owner_id":"119337721","stage":"DS3","amount":37440.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-A62B1D","owner":"Bryce Harmon","owner_id":"119337721","stage":"DS2","amount":18828.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-333EBB","owner":"Bryce Harmon","owner_id":"119337721","stage":"DS3","amount":2880.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-1CCE5C","owner":"Bryce Harmon","owner_id":"119337721","stage":"DS3","amount":20880.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-3EED2C","owner":"Alex Franklin","owner_id":"84342457","stage":"DS2","amount":7200.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-60C2C2","owner":"Alex Franklin","owner_id":"84342457","stage":"DS3","amount":19000.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-FA053A","owner":"Alex Franklin","owner_id":"84342457","stage":"DS3","amount":2880.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-7FA0C3","owner":"Alex Franklin","owner_id":"84342457","stage":"DS2","amount":1400.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-E531A6","owner":"Alex Franklin","owner_id":"84342457","stage":"DS3","amount":4800.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-5296C9","owner":"Alex Franklin","owner_id":"84342457","stage":"DS3","amount":10000.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-278DEC","owner":"Alex Franklin","owner_id":"84342457","stage":"DS3","amount":2700.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-4A13AD","owner":"Alex Franklin","owner_id":"84342457","stage":"DS3","amount":2160.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-9D0060","owner":"Alex Franklin","owner_id":"84342457","stage":"DS3","amount":3840.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-36C33F","owner":"Alex Franklin","owner_id":"84342457","stage":"DS2","amount":15000.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-0D0211","owner":"Alex Franklin","owner_id":"84342457","stage":"DS3","amount":1968.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-5AD94B","owner":"Alex Franklin","owner_id":"84342457","stage":"DS2","amount":4000.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-690476","owner":"Alex Franklin","owner_id":"84342457","stage":"DS2","amount":3600.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-6C60D4","owner":"Alex Franklin","owner_id":"84342457","stage":"DS3","amount":4800.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-EE195F","owner":"Alex Franklin","owner_id":"84342457","stage":"DS3","amount":3120.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-F436DA","owner":"Alex Franklin","owner_id":"84342457","stage":"DS2","amount":2520.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-034D49","owner":"Alex Franklin","owner_id":"84342457","stage":"DS3","amount":9000.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-EC3025","owner":"Alex Franklin","owner_id":"84342457","stage":"DS2","amount":62000.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-317E6F","owner":"Alex Franklin","owner_id":"84342457","stage":"DS3","amount":5400.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-0D2F7A","owner":"Alex Franklin","owner_id":"84342457","stage":"DS3","amount":5100.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-D1E6C2","owner":"Alex Franklin","owner_id":"84342457","stage":"DS2","amount":4400.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-635B8E","owner":"Alex Franklin","owner_id":"84342457","stage":"DS3","amount":2600.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-D9A12F","owner":"Alex Franklin","owner_id":"84342457","stage":"DS3","amount":17000.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-CA5E44","owner":"Alex Franklin","owner_id":"84342457","stage":"DS2","amount":8100.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-4F775F","owner":"Alex Franklin","owner_id":"84342457","stage":"DS3","amount":18000.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-898FC5","owner":"Alex Franklin","owner_id":"84342457","stage":"DS3","amount":12600.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-293AF3","owner":"Alex Franklin","owner_id":"84342457","stage":"DS3","amount":9000.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-46988D","owner":"Alex Franklin","owner_id":"84342457","stage":"DS3","amount":3780.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-712010","owner":"Alex Franklin","owner_id":"84342457","stage":"DS3","amount":7200.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-13FEBD","owner":"Alex Franklin","owner_id":"84342457","stage":"DS2","amount":4680.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-E73427","owner":"Alex Franklin","owner_id":"84342457","stage":"DS3","amount":18000.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-42F601","owner":"Alex Franklin","owner_id":"84342457","stage":"DS3","amount":2730.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-55164C","owner":"Alex Franklin","owner_id":"84342457","stage":"DS3","amount":3060.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-B936FE","owner":"Alex Franklin","owner_id":"84342457","stage":"DS3","amount":18000.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-4B0BEB","owner":"Alex Franklin","owner_id":"84342457","stage":"DS2","amount":12000.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-3BA5EA","owner":"Alex Franklin","owner_id":"84342457","stage":"DS3","amount":7200.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-5FDCE4","owner":"Alex Franklin","owner_id":"84342457","stage":"DS3","amount":1600.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-57887A","owner":"Dana Mercer","owner_id":"83155923","stage":"DS2","amount":15000.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-F336B6","owner":"Dana Mercer","owner_id":"83155923","stage":"DS3","amount":4200.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-215CCA","owner":"Dana Mercer","owner_id":"83155923","stage":"DS3","amount":18900.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-E51FB7","owner":"Dana Mercer","owner_id":"83155923","stage":"DS2","amount":43875.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-9DDE86","owner":"Dana Mercer","owner_id":"83155923","stage":"DS2","amount":20000.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-F40F04","owner":"Dana Mercer","owner_id":"83155923","stage":"DS2","amount":8100.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-5EED42","owner":"Dana Mercer","owner_id":"83155923","stage":"DS3","amount":16250.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-DAF1D9","owner":"Dana Mercer","owner_id":"83155923","stage":"DS3","amount":3150.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-8952F0","owner":"Dana Mercer","owner_id":"83155923","stage":"DS3","amount":2100.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-BA3DDC","owner":"Dana Mercer","owner_id":"83155923","stage":"DS3","amount":23400.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-7E2131","owner":"Dana Mercer","owner_id":"83155923","stage":"DS3","amount":5400.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-F9A3C1","owner":"Dana Mercer","owner_id":"83155923","stage":"DS3","amount":25000.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-AF932D","owner":"Cole Ingram","owner_id":"83155924","stage":"DS2","amount":7225.4,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-8BC9F5","owner":"Cole Ingram","owner_id":"83155924","stage":"DS2","amount":5616.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-FF809F","owner":"Cole Ingram","owner_id":"83155924","stage":"DS2","amount":7781.2,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-A71728","owner":"Cole Ingram","owner_id":"83155924","stage":"DS2","amount":6947.5,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-B25F40","owner":"Cole Ingram","owner_id":"83155924","stage":"DS3","amount":40000.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-CD47A6","owner":"Cole Ingram","owner_id":"83155924","stage":"DS2","amount":12168.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-42326B","owner":"Cole Ingram","owner_id":"83155924","stage":"DS3","amount":2480.4,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-FA32A0","owner":"Cole Ingram","owner_id":"83155924","stage":"DS3","amount":11116.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-E568D5","owner":"Cole Ingram","owner_id":"83155924","stage":"DS3","amount":1875.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-1BA595","owner":"Cole Ingram","owner_id":"83155924","stage":"DS2","amount":31750.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-813836","owner":"Cole Ingram","owner_id":"83155924","stage":"DS2","amount":32175.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-175395","owner":"Cole Ingram","owner_id":"83155924","stage":"DS3","amount":4779.88,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-2F3A66","owner":"Cole Ingram","owner_id":"83155924","stage":"DS3","amount":3334.8,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-D04904","owner":"Cole Ingram","owner_id":"83155924","stage":"DS2","amount":58529.25,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-481E24","owner":"Cole Ingram","owner_id":"83155924","stage":"DS3","amount":4140.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-CFE1E8","owner":"Cole Ingram","owner_id":"83155924","stage":"DS3","amount":18000.0,"why_buys_compliant":false,"no_ds1":false},{"deal_alias":"Deal-99A240","owner":"Cole Ingram","owner_id":"83155924","stage":"DS2","amount":18900.0,"why_buys_compliant":false,"no_ds1":false}]
```

### Process fixes
1. Gate stage progression: make `why_buys` (15+ chars, no placeholder) a hard-required field before a deal can advance to DS2 — block the save/advance in the CRM if blank or a placeholder.
2. Auto-populate a DS1 timestamp on deal creation (default to `t_ds1 = create_date`); add a daily report flag for any deal missing `t_ds1` so regressions/orphans surface immediately.
3. Add a date-order validation rule on stage-entry timestamps: if an earlier-stage timestamp is later than any later-stage timestamp, prevent stage save and alert the owner to correct the sequence.
owner-resolutionPASS 1.00

? The Question

The deal extract below has an owner name field that is blank on nearly every row and an owner id field that is populated. Using the owners table, resolve each open deal to an owner name. Report: number of deals resolved, deals whose owner id has no match in the owners table (list them), the owner ids that map to archived/deactivated owners (list their names), and the total pipeline amount per resolved owner. Do not guess a name for any unmatched id.
cost $0.0102 · 615s · in 89,267 / out 6,402 tok
✓ resolved_count✓ unmatched_none✓ deactivated_flagged✓ jake_amount✓ justin_amount✓ no_invented

✓ Correct Answer (ground truth)

resolved: 156
jake_amount: 1054144.0
justin_amount: 624310.0
per_owner: Bryce Harmon=1054144.0; Alex Franklin=624310.0; Dana Mercer=341195.0; Cole Ingram=288161.43; Farid Osman=4134.0; Elena Sinclair=2100.0
deactivated:
graded checks: resolved_count · unmatched_none · deactivated_flagged · jake_amount · justin_amount · no_invented

Model's actual answer

show the model's full answer
⚠ Stream stalled mid tool-call (terminal); the action was not executed. Ask me to retry if you want to continue.All 156 open deals resolved. None have an owner_id missing from the owners table. Two archived/deactivated owner records exist in the owners table but map to none of these open deals. Pipeline sums exactly: $2,314,044.43.

Deals resolved: 156 of 156

Deals whose owner_id has no match in the owners table: none (0 unmatched owner_ids found; every owner_id present appears in owners.csv)

Owner ids that map to archived/deactivated owners (per owners.csv where archived=true): none active in this deal extract
- Gavin Porter (1520255671) — archived, no open-deal references
- Hugo Lindqvist (77260721) — archived, no open-deal references
Note: these two archived owners exist in owners.csv but no deals in deals_open.csv point to their ids, so there are no active-to-archived resolution incidents.

Total pipeline amount per resolved owner (amount summed from deals_open.csv rows):
- Bryce Harmon (119337721): $1,054,144.00
- Alex Franklin (84342457): $624,310.00
- Dana Mercer (83155923): $341,195.00
- Cole Ingram (83155924): $288,161.43
- Farid Osman (716654662): $4,134.00
- Elena Sinclair (701163055): $2,100.00
Total: $2,314,044.43
gong-hubspot-join-integrityPASS 1.00

? The Question

Check the integrity of the call-to-deal mapping below. The gong table lists calls in the last 90 days keyed to HubSpot deal ids; deals_open lists the current open default-pipeline deals. Report: (a) count of gong rows whose deal id has NO match in deals_open (orphans; list a few sample aliases), (b) duplicate conversation keys (rows where calls_90d exceeds distinct_conversation_keys), (c) among the open deals that ARE at stage DS3 or later, the share that have at least one logged call. Give sample aliases for the orphan finding, all taken from the data. Note: gong_owner_id is a Gong user id, not a HubSpot owner id.
cost $0.0104 · 360s · in 52,957 / out 28,242 tok
✓ orphan_count✓ dup_keys_0✓ ds3_coverage✓ sample_ids_present

✓ Correct Answer (ground truth)

orphans: 30
orphan_aliases: Deal-8FA85D, Deal-8FC3F9, Deal-3B7945, Deal-42B265, Deal-9CCC42, Deal-36EA09
dup: 0
ds3_cov_pct: 29.4
graded checks: orphan_count · dup_keys_0 · ds3_coverage · sample_ids_present

Model's actual answer

show the model's full answer
I've parsed both CSVs and computed all three checks. Here are the results.

## Results

### (a) Orphan gong rows — deal id has NO match in deals_open
Count: **30 orphan rows** (out of 67 gong rows; 37 matched, 30 unmatched).

These 30 gong rows reference `hs_deal_id` values that do not appear in `deals_open.csv`. Notably, most are high-volume call deals (single account, many calls) with `calls_with_ds2_date=0` and `calls_with_sqo_date=0`, suggesting they belong to closed/won or other-pipeline deals rather than the current open set.

Sample orphan aliases (all taken verbatim from the data):

| hs_deal_id | deal_alias | calls_90d | calls_with_ds2_date | calls_with_sqo_date |
|---|---|---|---|---|
| 60251290957 | Deal-8FA85D | 46 | 0 | 0 |
| 60251649055 | Deal-8FC3F9 | 24 | 0 | 0 |
| 60251639682 | Deal-3B7945 | 21 | 0 | 0 |
| 61227242540 | Deal-42B265 | 21 | 0 | 0 |
| 61430316324 | Deal-9CCC42 | 17 | 0 | 0 |
| 60251082126 | Deal-36EA09 | 17 | 0 | 0 |
| 60250446726 | Deal-9A43B4 | 14 | 0 | 0 |
| 60251622924 | Deal-605F3C | 12 | 0 | 0 |
| 64058864408 | Deal-03BA69 | 5 | 0 | 5 |
| 64627577700 | Deal-523604 | 4 | 0 | 4 |

(20 additional orphan rows omitted; all in the full list above.)

There were no duplicate `hs_deal_id` values within gong, no duplicate `deal_id` values within `deals_open`, and no alias/id mismatches (every matched pair shares the same alias spelling in both files).

### (b) Duplicate conversation keys
Count of rows where `calls_90d` exceeds `distinct_conversation_keys`: **0**

In every gong row, `calls_90d` == `distinct_conversation_keys`. No conversation-key duplication was detected.

### (c) Share of open DS3+ deals with at least one logged call
- Open deals at stage DS3 or later (DS3 + DS4 + DS5): **85**
- Of those, deals appearing in the gong table with `calls_90d >= 1`: **25**

Arithmetic: 25 / 85 = 0.2941 → **29.41%**

Sample of the 25 matched DS3+ deals with calls (a few shown; stages DS3/DS4/DS5 represented):

Deal-25F752 (DS4, 3 calls), Deal-E53952 (DS4, 5), Deal-5408B0 (DS4, 8), Deal-D348E1 (DS5, 8), Deal-C26D20 (DS5, 4), Deal-C6FE92 (DS3, 11), Deal-60C2C2 (DS3, 3).

The remaining **60** open DS3+ deals have no logged call in the 90-day gong window (e.g., Deal-9AAE5F, Deal-403845, Deal-6787C2, Deal-7B3B0F, and 56 others).

## Summary table

| Check | Result |
|---|---|
| (a) Orphan gong rows | 30 rows (e.g., Deal-8FA85D, Deal-8FC3F9, Deal-3B7945) |
| (b) Duplicate conversation keys | 0 rows |
| (c) Open DS3+ deals with >=1 logged call | 25 / 85 = 29.41% |

Note: `gong_owner_id` values (e.g., 4729257026550273695, 5886696740382085504) did not overlap with any HubSpot `owner_id` values in `deals_open`, so they were treated as opaque Gong user IDs as flagged.
snowflake-sql-trapped-catalog0.67

? The Question

Using ONLY the catalog provided, write one Snowflake SQL query returning, per customer company, for its first calendar month as a customer: unique givers, recognition count, successful redemption count. Read the catalog notes carefully: some tables are stale or unpopulated and must be avoided, and one documented filter (excluding deleted givers) must NOT be applied to historical giving counts. Put a comment above each FROM or JOIN explaining the table choice. Return only fenced SQL.
cost $0.0114 · 338s · in 60,924 / out 24,053 tok
✗ avoids_stale_deals✗ avoids_unpopulated_live✓ uses_cohort_summary✓ succeeded_filter✓ no_giver_deletion_filter✓ returns_sql

✓ Correct Answer (ground truth)

graded checks: avoids_stale_deals · avoids_unpopulated_live · uses_cohort_summary · succeeded_filter · no_giver_deletion_filter · returns_sql

Model's actual answer

show the model's full answer
```sql
-- =============================================================================
-- Per customer company, for its FIRST CALENDAR MONTH as a customer:
--   unique_givers | recognition_count | successful_redemption_count
--
-- TABLE CHOICE & RULES (catalog-driven, no invented columns/tables):
--
-- * FROM PRODUCTION.PLG.COMPANY_COHORT_SUMMARY:
--   The ONLY cataloged table that documents first-month giving/redemption
--   metrics (M1_GIVES, M1_REDEMPTIONS) and a customer-start date
--   (FIRST_SUB_PAYMENT_DATE, plus SIGNUP_DATE / ACTIVATED_DATE). One row per
--   self-serve company, so these M1_* values are pre-aggregated to month-1 and
--   require no event scan.
--
-- * PRODUCTION.DEPRECATED_RECOGNITION.REDEMPTION_RECORDS_V2 is the documented
--   raw redemption source (rule: only STATE = 'succeeded' rows count), but the
--   catalog lists NO columns for it -- no COMPANY_ID, no event timestamp.
--   Joining/month-scoping it would require inventing schema, so it is NOT
--   referenced. M1_REDEMPTIONS is used instead as the cataloged month-1
--   redemption metric.
--
-- * No recognition/giving EVENTS table exists in this catalog at all, so
--   unique_givers (requires a giver identity) and a computed recognition_count
--   cannot be derived from raw events.
--
-- TABLES AVOIDED (per catalog notes):
--   HUBSPOT_HUB_1973303.V2_LIVE.OBJECTS_DEALS  -- UNPOPULATED
--   PRODUCTION.HUBSPOT.DEALS                   -- STALE (last sync 2023-03)
--   PRODUCTION.HUBSPOT.GONG_HUBSPOT_MAP_FAST   -- RETIRED pattern
--   HUBSPOT / GONG / ChargeBee tables          -- carry no giving or
--                                                redemption data; no documented
--                                                join path to giving data.
--
-- BUSINESS RULE COMPLIANCE:
--   - "Exclude deleted givers must NOT be applied to historical giving counts":
--     No such filter appears anywhere in this query. M1_GIVES is a
--     pre-aggregated historical count used as-is (it is not recomputed from a
--     giver-level table, so there is no predicate to withhold -- and none is
--     applied).
--   - "Redemptions: STATE = 'succeeded' only": acknowledged as the rule behind
--     M1_REDEMPTIONS; the raw REDEMPTION_RECORDS_V2 table is not JOINed because
--     its columns are undocumented (see comment above).
--
-- DATA GAPS (stated, not worked around):
--   1) UNIQUE_GIVERS -- NOT COMPUTABLE. No giver-level table or giving-events
--      table exists in this catalog. M1_USERS is a month-1 USER count, NOT a
--      distinct-giver count, and is deliberately returned as NULL rather than
--      relabeled. Requires a giver dimension + recognition fact table.
--   2) RECOGNITION_COUNT -- uses M1_GIVES, the only documented month-1
--      recognition volume. Caveat: it is pre-aggregated; the catalog does not
--      document whether M1_* is anchored to SIGNUP_DATE, ACTIVATED_DATE, or
--      FIRST_SUB_PAYMENT_DATE, so alignment with first_calendar_month is not
--      guaranteed. Requires a recognition-events table for a true event-level
--      count that could honor the deleted-giver rule.
--   3) SUCCESSFUL_REDEMPTION_COUNT -- uses M1_REDEMPTIONS (documented month-1
--      redemption metric). The catalog does not state M1_REDEMPTIONS is
--      success-filtered; requires REDEMPTION_RECORDS_V2 column docs to verify.
--   4) COMPANY KEY -- catalog says "one row per self-serve company" but does
--      not name the company identifier column. COMPANY_ID is used by convention
--      per Bonusly domain naming; verify before production use.
--   5) SELF-SERVE ONLY -- no giving/redemption source exists in this catalog
--      for sales-led customers (HubSpot PROPERTY_PIPELINE = 'default').
-- =============================================================================

SELECT
    COMPANY_ID,                                                       -- company identifier (catalog does not name this column; see gap #4)
    DATE_TRUNC('month', FIRST_SUB_PAYMENT_DATE) AS first_calendar_month_as_customer,
                                                                     -- "became a customer" = first subscription payment; truncated to calendar month
    CAST(NULL AS NUMBER)        AS unique_givers,                     -- NOT COMPUTABLE: no giver-level / giving-events table in catalog (gap #1)
    M1_GIVES                     AS recognition_count,                -- deleted-giver filter deliberately NOT applied (rule: understates history)
    M1_REDEMPTIONS              AS successful_redemption_count        -- month-1 redemptions; STATE='succeeded' rule applies to source REDEMPTION_RECORDS_V2 (gap #3)
-- FROM: PRODUCTION.PLG.COMPANY_COHORT_SUMMARY -- chosen because it is the sole
--   cataloged table carrying per-company first-month giving/redemption metrics
--   and a customer-start date. No JOIN is issued: (a) the only other relevant
--   source, REDEMPTION_RECORDS_V2, has no documented columns to join on; and
--   (b) no recognition/giving events table exists to JOIN for unique_givers or
--   recognition_count. All other tables are stale/unpopulated/retired or carry
--   no giving data.
FROM PRODUCTION.PLG.COMPANY_COHORT_SUMMARY
-- Restrict to companies that actually became paying customers.
WHERE FIRST_SUB_PAYMENT_DATE IS NOT NULL
ORDER BY first_calendar_month_as_customer, COMPANY_ID;
```

Written to /Users/amaniphipps/sql_query.sql. Key decisions: only `COMPANY_COHORT_SUMMARY` is referenced (the sole table documenting first-month M1_GIVES/M1_REDEMPTIONS and a customer-start date); `REDEMPTION_RECORDS_V2` is NOT joined because the catalog documents no columns for it (no joinable keys, no timestamp); `unique_givers` is NULL because no giver-level/giving-events table exists in the catalog rather than mislabeling `M1_USERS`; the deleted-giver exclusion filter is applied nowhere; and stale/unpopulated/retired tables (V2_LIVE, PRODUCTION.HUBSPOT.DEALS, GONG_HUBSPOT_MAP_FAST) are all avoided.
crm-completeness-enrichmentPASS 1.00

? The Question

Audit the CRM extract for completeness and propose fixes. Deals need owner, stage, amount, close date, why-buys; companies need industry, employee count, HQ country; contacts need email, title, persona. Report completeness percent per field. Identify duplicate company clusters (name variants, shared domain) with a survivor per cluster. List invalid emails and domain mismatches. Fill missing company fields only where the enrichment export has a matching row; where CRM and enrichment disagree list both and recommend a source. Never invent a value. End with the 10 fixes with the most pipeline amount at stake.
cost $0.0043 · 93s · in 20,283 / out 6,921 tok
✓ dup_clusters✓ invalid_emails✓ survivor_language✓ completeness_pct✓ no_invent

✓ Correct Answer (ground truth)

n_companies: 34
n_contacts: 52
company_missing: industry=0; employee_count=9; hq_country=6
invalid_email_count: 4
invalid_emails: CT-0010, CT-0080, CT-0081, CT-0192
domain_mismatch_count: 1
domain_mismatches: CT-0011
duplicate_clusters: ['C-0A092931', 'C-0A092932'], ['C-0A092933', 'C-0A092934']
n_dup_clusters: 2
enrichment_matches: 25
graded checks: dup_clusters · invalid_emails · survivor_language · completeness_pct · no_invent

Model's actual answer

show the model's full answer
I now have all the raw data. There is no deals file (deals extract, owner/stage/amount/close date/why-buys / pipeline amounts) was not provided, so I will explicitly flag that and exclude it. Here is the audit.

----------------------------------------------------------------
CRM EXTRACT AUDIT — companies.csv (34), contacts.csv (52), zoominfo_enrichment.csv (25)
No deals file was provided. Deals completeness (owner, stage, amount, close date, why-buys) could not be measured, and pipeline/amount-at-stake ranking therefore cannot be computed. All fixes below note N/A pipeline stake.

----------------------------------------------------------------
1. COMPLETENESS PER FIELD

Companies (34 rows)
- company_alias: 34/34 = 100.0%
- domain: 34/34 = 100.0%
- industry: 34/34 = 100.0%  (but 4 free-text variants: "tech"/"Tech" vs "Technology"; "health care" vs "Healthcare")
- employee_count: 25/34 = 73.5%  (9 missing)
- hq_country: 28/34 = 82.4%  (6 missing; plus 4 aliases: "US"/"USA" for United States)

Contacts (52 rows)
- contact_key: 52/52 = 100.0%
- company_alias: 52/52 = 100.0%
- email: 52/52 = 100.0%  (but 4 are structurally invalid — see §5)
- title: 40/52 = 76.9%  (12 missing)
- persona: 37/52 = 71.2%  (15 missing)
- domain: 51/52 = 98.1%  (1 missing: CT-0080 C-92D97D)

----------------------------------------------------------------
2. MISSING COMPANY FIELDS — FILLED FROM ENRICHMENT (exact domain match only)

| alias     | field          | CRM      | enrichment (ZoomInfo) | action   |
|-----------|----------------|----------|-----------------------|----------|
| C-EC3025  | employee_count | (missing)| 400                   | FILL     |
| C-96039F  | employee_count | (missing)| 400                   | FILL     |
| C-44EA29  | employee_count | (missing)| 400                   | FILL     |
| C-D04904  | employee_count | (missing)| 400                   | FILL     |
| C-B23205  | employee_count | (missing)| 400                   | FILL     |
| C-60C75F  | employee_count | (missing)| 400                   | FILL     |
| C-7BBDFA  | employee_count | (missing)| 400                   | FILL     |
| C-50D386  | employee_count | (missing)| 400                   | FILL     |
(8 fills; all employee_count, all value 400)

NOT filled (enrichment also empty for these):
- C-2D1F1B hq_country  (enrichment zi_hq_country = "")
- C-D73B89 hq_country (enrichment = "")
- C-44EA29 hq_country (enrichment = "")
- C-D04904 hq_country (enrichment = "")
- C-2C60E5 hq_country (enrichment = "")

Companies with NO enrichment row at all (domain not in zoominfo_enrichment.csv) — fields stay missing, no fill possible:
C-BA969B, C-332637, C-93C8BF, C-EE9FFB, C-C9BB20, C-0A092931, C-0A092932, C-0A092933, C-0A092934

----------------------------------------------------------------
3. CRM vs ENRICHMENT DISAGREEMENTS (both present, different value) — 21 rows

Recommendation rule applied: hq_country → trust the canonicalized country (US/USA both = United States; keep "United States" as canonical); industry → trust ZoomInfo (more specific); employee_count → trust ZoomInfo (authoritative).

| alias     | field          | CRM           | ZoomInfo         | recommend                  |
|-----------|----------------|---------------|------------------|----------------------------|
| C-66D1FC  | industry       | tech          | Computer Software| ZIP (canonical: Technology)|
| C-66D1FC  | hq_country     | US            | United States    | canonical = United States  |
| C-950043  | hq_country     | US            | United States    | canonical = United States  |
| C-EC3025  | industry       | Technology    | Computer Software| keep CRM (Technology)      |
| C-EC3025  | hq_country     | USA           | United States    | canonical = United States  |
| C-96039F  | hq_country     | USA           | United States    | canonical = United States  |
| C-44EA29  | industry       | tech          | Computer Software| ZIP                        |
| C-92D97D  | industry       | Technology    | Computer Software| keep CRM                   |
| C-D04904  | industry       | Technology    | Computer Software| keep CRM                   |
| C-77A95A  | industry       | Technology    | Computer Software| keep CRM                   |
| C-77A95A  | hq_country     | US            | United States    | canonical                  |
| C-AA8DDA  | industry       | Technology    | Computer Software| keep CRM                   |
| C-B23205  | hq_country     | US            | United States    | canonical                  |
| C-E51FB7  | hq_country     | USA           | United States    | canonical                  |
| C-D0662E  | hq_country     | US            | United States    | canonical                  |
| C-B25F40  | industry       | Tech          | Computer Software| ZIP                        |
| C-B25F40  | employee_count | 50            | 120              | ZIP (120)                  |
| C-60C75F  | industry       | tech          | Computer Software| ZIP                        |
| C-425E2A  | industry       | Tech          | Computer Software| ZIP                        |
| C-425E2A  | hq_country     | USA           | United States    | canonical                  |
| C-2D7423  | hq_country     | USA           | United States    | canonical                  |

Note: "Technology" vs "Computer Software" is a genuine classification disagreement, not a synonym, so for those rows CRM=Technology / ZIP=Computer Software both are listed and ZIP is recommended as the more granular source. C-B25F40 employee_count is a hard disagreement (50 vs 120) — ZIP preferred (120 wins). For all hq_country rows the values are the SAME country in different aliases; recommend standardizing on "United States".

----------------------------------------------------------------
4. DUPLICATE COMPANY CLUSTERS (shared domain)

Cluster A — domain acme-corp.com (2 rows)
- C-0A092931  industry=Technology, emp=500, hq=US   (survivor: earliest alias C-0A092931)
- C-0A092932  industry=tech,      emp=510, hq=USA
Survivor: C-0A092931. Merge C-0A092932 into it. Reconciled field: industry=Technology (CRM), employee_count=510 (highest confidence — keep max, note the 500/510 split), hq_country=United States (canonicalize both).

Cluster B — domain globex.io (2 rows)
- C-0A092933  industry=SaaS,        emp=200, hq=US   (survivor)
- C-0A092934  industry=Technology,  emp=200, hq=US
Survivor: C-0A092933. Merge C-0A092934 into it. Reconciled: industry=SaaS (CRM-specific), employee_count=200, hq_country=United States.

No other duplicate domains. Name-variants detection (Levenshtein on company_alias) found no additional clusters because aliases are opaque IDs with no shared name root — duplicates are domain-driven only.

----------------------------------------------------------------
5. INVALID EMAILS + DOMAIN MISMATCHES — contacts.csv

Invalid emails (no local part or no host) — 4 rows:
- CT-0010  C-66D1FC   email="user0@"          domain=66d1fc.com
- CT-0080  C-92D97D   email="user0@"          domain=(missing)
- CT-0081  C-92D97D   email="user1@"          domain=92d97d.com
- CT-0192  C-425E2A   email="user2@"          domain=425e2a.com

Domain mismatches (email host != company domain, excluding invalids) — 1 row:
- CT-0011  C-66D1FC   email=user1@other-domain.com  domain=66d1fc.com

All other 47 contacts have email host matching their company domain.

----------------------------------------------------------------
6. CONTACT MISSING FIELD DETAIL

Title missing (12 rows, 76.9%):
CT-0000, CT-0080, CT-0081, CT-0092, CT-0100, CT-0120, CT-0121, CT-0122, CT-0132, CT-0162, CT-0170, CT-0171

Persona missing (15 rows, 71.2%):
CT-0000, CT-0022, CT-0070, CT-0072, CT-0080, CT-0081, CT-0082, CT-0092, CT-0100, CT-0110, CT-0132, CT-0162, CT-0170, CT-0171, CT-0172

----------------------------------------------------------------
7. TOP 10 FIXES (most pipeline amount at stake)

No deals file was provided, so there are no deal amounts / pipeline values to rank by. The fixes below are ordered by impact to data model integrity instead. All are marked pipeline=N/A.

1. [N/A pipeline] Remove/deactivate duplicate companies C-0A092932 (acme-corp.com) — survivor C-0A092931. Reason: shared domain, risk of double-counting accounts.
2. [N/A pipeline] Remove/deactivate duplicate companies C-0A092934 (globex.io) — survivor C-0A092933. Reason: shared domain.
3. [N/A pipeline] Standardize hq_country to "United States" across all 15 rows aliased as US/USA. Reason: 10 CRM-vs-ZI disagreements collapse to a single canonical value.
4. [N/A pipeline] Fill 8 missing employee_count values from ZoomInfo enrichment (C-EC3025, C-96039F, C-44EA29, C-D04904, C-B23205, C-60C75F, C-7BBDFA, C-50D386 → 400 each).
5. [N/A pipeline] Correct CT-0011 email user1@other-domain.com → real domain 66d1fc.com (domain mismatch).
6. [N/A pipeline] Fix CT-0080 C-92D97D: missing company domain AND invalid email "user0@". Needs domain backfill + valid email.
7. [N/A pipeline] Fix CT-0010/CT-0081/CT-0192 invalid emails ("user0@", "user1@", "user2@") — local-part-only, no host.
8. [N/A pipeline] Standardize industry free-text: "tech"/"Tech"/"Technology" → "Technology"; "health care"/"Healthcare" → "Healthcare". Removes 4 variants; resolves 10 industry disagreements by preferring ZoomInfo granularity where CRM used abbreviation.
9. [N/A pipeline] Resolve C-B25F40 employee_count disagreement (CRM 50 vs Zip 120) → 120 (trust enrichment as authoritative).
10. [N/A pipeline] Fill 5 missing hq_country where enrichment also empty (C-2D1F1B, C-D73B89, C-44EA29, C-D04904, C-2C60E5) — no fill possible from current enrichment; flag for manual enrichment (these have no ZoomInfo coverage).

----------------------------------------------------------------
BLOCKER: deals data missing. To produce the originally requested "10 fixes ranked by pipeline amount at stake" and deals completeness (owner/stage/amount/close date/why-buys per deal), the deals extract is required. Provide deals.csv (or its equivalent) and I will recompute §1 deals completeness, attach amounts, and re-rank the top-10 fixes by pipeline at stake.
deal-intelligence
closed-lost-classificationdiscarded

? The Question

Classify every deal lost in the last 6 months below into one primary category: pricing, competitor, no decision, timing, product gap, champion left, or other. Also assign a side (Bonusly, buyer, unknown). Then summarize: category counts, the side split, the number of deals where the structured closed_lost_tag clearly disagrees with the free-text reason, and the two patterns most worth acting on. Base everything only on the tag and free-text provided.

✓ Correct Answer (ground truth)

graded checks: total_90 · competitor_top · category_counts_present · side_split · two_patterns

Model's answer

not yet run — check back after the runners complete
pipeline-tieringPASS 1.00

? The Question

Score the open pipeline below on these signals: forecast category, stage, meetings_30d, contact count, and engagement recency. Assign exactly one tier per deal: LOCK, ACTION, BUILD, REVIVE, WATCH, or RISKY (RISKY = the forecast category disagrees with the engagement evidence). Never tier a deal LOCK if it has zero meetings_30d. Do NOT list every deal. Return ONLY a compact JSON object with exactly these keys: - "tier_counts": object mapping each tier to the number of deals in it (must sum to the total deal count), - "tier_examples": object mapping each tier to up to 3 example deal aliases from the data, - "risky_deals": array of the deal aliases you tiered RISKY, - "lock_violations": number of deals tiered LOCK that have zero meetings_30d (should be 0), - "pipeline_shape": one short paragraph on the overall pipeline shape. Note: inbound_emails_30d is 0 for every row due to a data defect, so treat meetings_30d as the inbound signal.
cost $0.0176 · 329s · in 60,779 / out 29,364 tok
✓ json_returned✓ tier_vocabulary✓ tier_counts_sum✓ risky_deals_listed✓ no_lock_violation_claimed

✓ Correct Answer (ground truth)

total: 156
graded checks: json_returned · tier_vocabulary · tier_counts_sum · risky_deals_listed · no_lock_violation_claimed

Model's actual answer

show the model's full answer
```json
{
  "tier_counts": {
    "LOCK": 4,
    "ACTION": 23,
    "BUILD": 60,
    "REVIVE": 43,
    "WATCH": 2,
    "RISKY": 24
  },
  "tier_examples": {
    "LOCK": ["Deal-D348E1", "Deal-C26D20", "Deal-944310"],
    "ACTION": ["Deal-25F752", "Deal-9AAE5F", "Deal-3974EB"],
    "BUILD": ["Deal-6787C2", "Deal-D73B89", "Deal-012CB1"],
    "REVIVE": ["Deal-2D1F1B", "Deal-66D1FC", "Deal-950043"],
    "WATCH": ["Deal-3EED2C", "Deal-57FF13"],
    "RISKY": ["Deal-E53952", "Deal-5408B0", "Deal-547B2B"]
  },
  "risky_deals": [
    "Deal-E53952", "Deal-5408B0", "Deal-547B2B", "Deal-B7EBD1", "Deal-2465CE",
    "Deal-584EE5", "Deal-C6D97A", "Deal-A5E80A", "Deal-0660B4", "Deal-FD9F4E",
    "Deal-499BF6", "Deal-BA571A", "Deal-7BBDFA", "Deal-4A13AD", "Deal-8AD4A5",
    "Deal-9D0060", "Deal-690476", "Deal-635B8E", "Deal-ED725A", "Deal-5FDCE4",
    "Deal-F336B6", "Deal-5EED42", "Deal-BA3DDC", "Deal-FA32A0"
  ],
  "lock_violations": 0,
  "pipeline_shape": "The open pipeline spans 156 deals split 105 PIPELINE, 40 BEST_CASE, and 11 COMMIT. Tiering shows 4 LOCK (3%), 23 ACTION (15%), 60 BUILD (38%), 43 REVIVE (28%), 24 RISKY (15%), 2 WATCH (1%). The pipeline is top-heavy in BUILD/REVIVE (103 deals, 66%), reflecting many PIPELINE-stage deals with old or absent engagement. Of 51 BEST_CASE/COMMIT deals, 24 are tier RISKY due to zero meetings_30d and no activity since 2026-09-02, indicating forecast categories are inflated relative to actual engagement evidence. Only 4 deals show recent meetings, recent activity, and advanced stage to justify forecast confidence."
}
```

Tiering logic applied:

- **LOCK**: BEST_CASE or COMMIT forecast, advanced stage (DS4/DS5), >=1 meeting_30d, recent activity (>= 2026-09-02), >=3 contacts.
- **ACTION**: BEST_CASE or COMMIT forecast with some engagement signals but gaps (no meetings + old activity = RISKY instead).
- **BUILD**: PIPELINE forecast with meetings_30d >=1 or recent activity and >=2 contacts.
- **REVIVE**: PIPELINE forecast with old activity (<= 2026-09-02) but some past engagement.
- **WATCH**: PIPELINE forecast with no activity data or no engagement at all.
- **RISKY**: Forecast category contradicts engagement evidence — BEST_CASE/COMMIT deals with zero meetings_30d and no recent activity, or stage/forecast mismatches (e.g., COMMIT at DS1/DS2, BEST_CASE at DS2).

Reference date: 2026-10-02 (latest activity in dataset). 30-day cutoff: 2026-09-02. Meetings_30d used as the inbound signal per the data defect note.
call-transcript-extractionPASS 1.00

? The Question

For each transcript, extract CRM write-back fields as JSON: why-buys (prospect statements only), pain points, stakeholders from the speaker list, budget signal (prospect-stated or null), timeline signal, competitor mentioned (only if the prospect raised it), next step (only if explicitly agreed), objections, confidence. Never fill a field from what the rep said or hoped.
cost $0.0024 · 149s · in 2,138 / out 11,409 tok
✓ json_emitted✓ budget_signal✓ null_discipline✓ stakeholders✓ competitor_prospect_only

✓ Correct Answer (ground truth)

rep: Alex Franklin
per_transcript: {"TX-001": {"why_buys": ["automating anniversary and birthday awards"], "pain_points": ["HR team of three cannot keep up with it manually", "track everything in a spreadsheet, and people slip through
rep_sourced_nulls: {"TX-003": ["budget_signal \u2014 only the rep mentioned pricing/budget"], "TX-004": ["next_step \u2014 rep proposed, prospect said 'no promises'"], "TX-006": ["budget_signal \u2014 rep decoy, prospec
allowed_new: TX-001, TX-002, TX-003, TX-004, TX-005, TX-006, Achievers, Bucketlist, Nectar
graded checks: json_emitted · budget_signal · null_discipline · stakeholders · competitor_prospect_only

Model's actual answer

show the model's full answer
I'll extract the CRM write-back fields from each transcript, pulling only from prospect statements (never the rep's lines or hoped-for outcomes).

Here is the JSON:

```json
[
  {
    "transcript_id": "TX-001",
    "deal_alias": "Deal-CFE7F4",
    "stakeholders": [
      "Prospect (VP People)",
      "Prospect (HR Admin)"
    ],
    "why_buys": [
      "automating anniversary and birthday awards"
    ],
    "pain_points": [
      "our HR team of three cannot keep up with it manually",
      "we track everything in a spreadsheet, and people slip through the cracks"
    ],
    "budget_signal": "$40k earmarked for engagement tools this fiscal year",
    "timeline_signal": "live before open enrollment in November",
    "competitor_mentioned": "Achievers",
    "next_step": "security review on September 12",
    "objections": [
      "need SSO and audit logs for IT to sign off"
    ],
    "confidence": "HIGH"
  },
  {
    "transcript_id": "TX-002",
    "deal_alias": "Deal-70BB30",
    "stakeholders": [
      "Prospect (Head of Total Rewards)",
      "Prospect (CFO)"
    ],
    "why_buys": [
      "tie recognition to retention for our hourly workforce"
    ],
    "pain_points": [
      "regretted turnover for hourly workforce is over 30%"
    ],
    "budget_signal": "$25k pilot budget approved for this quarter",
    "timeline_signal": "decision by end of September",
    "competitor_mentioned": null,
    "next_step": "send pilot agreement and route to legal this week",
    "objections": [
      "Integration with Workday has to be rock solid — that's my one condition"
    ],
    "confidence": "HIGH"
  },
  {
    "transcript_id": "TX-003",
    "deal_alias": "Deal-530B50",
    "stakeholders": [
      "Prospect (People Ops Manager)"
    ],
    "why_buys": [
      "make recognition visible across our 12 retail locations"
    ],
    "pain_points": [
      "Store managers have zero budget autonomy for on-the-spot recognition today"
    ],
    "budget_signal": null,
    "timeline_signal": "no rush until Q1",
    "competitor_mentioned": "Bucketlist",
    "next_step": "schedule a call with our CEO",
    "objections": [
      "The CEO has to be sold first — she decides anything people-related"
    ],
    "confidence": "MEDIUM"
  },
  {
    "transcript_id": "TX-004",
    "deal_alias": "Deal-180D02",
    "stakeholders": [
      "Prospect (VP People)",
      "Prospect (IT Security Lead)"
    ],
    "why_buys": [
      "consolidate three separate recognition tools into one"
    ],
    "pain_points": [
      "We're paying for three tools and none of them talk to our HRIS"
    ],
    "budget_signal": "$15k annually (VP People can approve without board below this)",
    "timeline_signal": "procurement cycle runs six to eight weeks minimum",
    "competitor_mentioned": null,
    "next_step": null,
    "objections": [
      "Our procurement cycle runs six to eight weeks minimum",
      "The security review took three months for our last vendor — that's my hesitation"
    ],
    "confidence": "MEDIUM"
  },
  {
    "transcript_id": "TX-005",
    "deal_alias": "Deal-F8767A",
    "stakeholders": [
      "Prospect (HR Director)",
      "Prospect (People Ops Coordinator)"
    ],
    "why_buys": [
      "automate service milestones, and give us analytics on recognition equity across departments"
    ],
    "pain_points": [
      "Our night-shift teams feel invisible — their engagement scores run 20 points lower"
    ],
    "budget_signal": "$12k approved under our engagement line",
    "timeline_signal": "running before our January all-hands",
    "competitor_mentioned": "Nectar",
    "next_step": "present to exec team on October 2",
    "objections": [
      "Our exec team is skeptical after a failed rollout two years ago"
    ],
    "confidence": "HIGH"
  },
  {
    "transcript_id": "TX-006",
    "deal_alias": "Deal-EE195F",
    "stakeholders": [
      "Prospect (HR Manager)"
    ],
    "why_buys": [
      "cut the admin time on service awards"
    ],
    "pain_points": [
      "I personally spend five hours a month ordering and shipping plaques"
    ],
    "budget_signal": "Budget isn't the issue — time is",
    "timeline_signal": "Q1 start realistic (fiscal year resets in February)",
    "competitor_mentioned": null,
    "next_step": "send one-page overview and forward to our COO this week",
    "objections": [
      "our COO usually prefers building things in-house"
    ],
    "confidence": "HIGH"
  }
]
```

Extraction notes:
- TX-001: VP People explicitly agreed to "security review on September 12" (yes response). HR Admin raised the SSO/audit-log concern as a blocker for IT sign-off — captured as an objection. Achievers was raised unprompted by the prospect.
- TX-002: Head of Total Rewards confirmed they are the first vendor with a real demo — no competitor. CFO explicitly agreed to pilot agreement routing to legal. Workday integration stated as "my one condition" — objection.
- TX-003: People Ops Manager did not state a budget; the rep floated $8/employee but the prospect never confirmed or reacted. Budget signal = null. Bucketlist raised unprompted by the prospect (used at the CEO's last company). CEO not a speaker, so excluded from stakeholder list. Next step explicitly agreed ("Yes, let's schedule a call with our CEO").
- TX-004: VP People set a $15k approval ceiling herself. Follow-up with CFO was "Maybe — I need to check her calendar, no promises" — not an explicit agreement, so next_step = null. Both procurement and security-review timeline concerns captured as objections.
- TX-005: Nectar raised unprompted by the prospect ("mid-pilot with Nectar"). Exec-team skepticism from prior failed rollout captured as objection. Presentation to exec team on October 2 explicitly agreed ("Yes").
- TX-006: Prospect said "Nobody else — comparing against just doing it internally" — no vendor competitor raised. Budget signal captured as the prospect's own qualitative statement ("Budget isn't the issue — time is"). One-pager forwarding to COO explicitly agreed ("Yes").
next-to-closePASS 1.00

? The Question

Which 3 deals are closest to signature right now? Use the CRM data and the Slack thread; Slack often has the real status before the CRM does. For each: deal, amount, why it is close, what is left. Under 250 words.
cost $0.0009 · 46s · in 1,564 / out 3,528 tok
✓ exactly_three✓ top3_named✓ slack_first✓ blocked_excluded

✓ Correct Answer (ground truth)

top3: rank=1; deal_alias=Deal-547B2B; amount=11200; stage=DS5; forecast_category=COMMIT; close_date=2026-09-11 | rank=2; deal_alias=Deal-B7EBD1; amount=9000; stage=DS5; forecast_category=COMMIT; close_date=2026-09-10 | rank=3; deal_alias=Deal-403845; amount=9000; stage=DS5; forecast_category=COMMIT; close_date=2026-09-11
exactly_three: True
blocked_excluded: deal_alias=Deal-2465CE; reason=champion left + procurement freeze (Slack #sales-sparring 2026-09-04)
ranking_rule: Slack signature-imminent deal ranks first; remaining ranked by stage (DS5>DS4), forecast (COMMIT>BEST_CASE>PIPELINE), close_date asc, amount desc
graded checks: exactly_three · top3_named · slack_first · blocked_excluded

Model's actual answer

show the model's full answer
1. **Deal-547B2B** | $11,200 | Slack: redlines came back clean, signing page is out, VP People confirmed signing tomorrow — signature-imminent. | Awaiting signature.

2. **Deal-403845** | $9,000 | Slack: order form is with their finance team (final approval step). | Finance team review/approval.

3. **Deal-A2B47C** | $6,360 | Slack: still warm, normal legal-review pace (DS5/COMMIT, closes 2026-09-11). | Legal review conclusion.

Notable exclusion: **Deal-2465CE** ($5,400) shares the earliest close date (2026-09-10) but Dana's Slack confirms it is blocked — champion left, procurement frozen, pulled from commit. This is why CRM close dates alone are misleading.

**Deal-B7EBD1** ($9,000, close 2026-09-10) also sits in DS5/COMMIT with the earliest close date, but has no Slack mention, so I cannot confirm its real-time status.
product-gap-detectionPASS 1.00

? The Question

Surface true product gaps raised by prospects in active deals. For each candidate: quote the prospect line, classify as real gap, plan gate, or rollout/enablement issue using the product docs excerpt, and name the deal and amount. Only real gaps go in the summary. Rep language does not count as prospect voice.
cost $0.0004 · 15s · in 792 / out 995 tok
✓ real_gaps✓ plan_gate_classified✓ enablement_classified✓ gap_summary

✓ Correct Answer (ground truth)

real_gaps: transcript_id=TG-001; deal_alias=Deal-EC3025; amount=62000; feature=SCIM user provisioning; quote=We need SCIM user provisioning — our security team will not approve any tool that requires manual account management.; why_real=not listed in product docs at any tier | transcript_id=TG-002; deal_alias=Deal-D0D6B5; amount=23800; feature=ADP Workforce Now integration; quote=Our payroll runs on ADP Workforce Now and I don't see ADP anywhere in your integration list — that's a dealbreaker for us.; why_real=supported HRIS list is Workday/BambooHR/Gusto/Rippling only
plan_gates: transcript_id=TG-003; deal_alias=Deal-CFE7F4; amount=32000; feature=Custom report builder; tier_required=Enterprise; quote=I tried to build a custom report combining recognition frequency with turnover and couldn't — is that possible on our current plan?
enablement_issues: transcript_id=TG-004; deal_alias=Deal-84DBA6; amount=16000; feature=Slack integration recognition adoption; quote=We turned on the Slack integration in July, but half our managers still haven't given a single recognition — nobody ever trained them on it.; why_enablement=Slack integration exists on all plans; managers were never trained
excluded_rep_voice: transcript_id=TG-005; line=Full transparency — we don't have a native mobile app for hourly workers yet, though it's on the roadmap.; reason=rep voice, not prospect-raised
allowed_new: TG-001, TG-002, TG-003, TG-004, TG-005, SCIM user provisioning, ADP Workforce Now integration, Custom report builder
graded checks: real_gaps · plan_gate_classified · enablement_classified · gap_summary

Model's actual answer

show the model's full answer
## Product Gaps Raised by Prospects in Active Deals

Based on the product docs, SCIM user provisioning and ADP Workforce Now are both explicitly listed as NOT currently supported capabilities. Three candidates met the threshold of "real prospect voice" (prospect speaker, not sales rep). Below is the full analysis.

---

### Candidate 1 — Deal-EC3025 (Prospect IT Security Lead)

**Prospect line:** "We need SCIM user provisioning — our security team will not approve any tool that requires manual account management."

**Classification: REAL GAP**

**Reasoning:** The product docs explicitly state: "SCIM user provisioning and ADP Workforce Now integrations are NOT currently listed as supported capabilities." The prospect's requirement directly maps to an unsupported capability — there is no plan tier that includes SCIM. This is a genuine product gap (missing feature), not a plan gate (which would mean the feature exists but is tier-restricted) nor a rollout/enablement issue (no feature to roll out or train on).

**Deal alias:** Deal-EC3025 | **Amount:** not provided in data

---

### Candidate 2 — Deal-D0D6B5 (Prospect HRIS Manager)

**Prospect line:** "Our payroll runs on ADP Workforce Now and I don't see ADP anywhere in your integration list — that's a dealbreaker for us."

**Classification: REAL GAP**

**Reasoning:** The product docs explicitly state: "SCIM user provisioning and ADP Workforce Now integrations are NOT currently listed as supported capabilities." The supported HRIS integrations are listed as Workday, BambooHR, Gusto, Rippling (Pro and above). ADP Workforce Now is not among them. The prospect's requirement directly maps to an unsupported integration. No plan tier includes ADP, so this is a true product gap (missing integration), not a plan gate nor a rollout/enablement issue.

**Deal alias:** Deal-D0D6B5 | **Amount:** not provided in data

---

### Candidate 3 — Deal-CFE7F4 (Prospect VP People)

**Prospect line:** "I tried to build a custom report combining recognition frequency with turnover and couldn't — is that possible on our current plan?"

**Classification: PLAN GATE** — excluded from summary

**Reasoning:** The product docs list "Custom report builder" as an Enterprise-only capability. The prospect explicitly asks whether it is "possible on our current plan." The sales rep confirms: "The custom report builder sits on our Enterprise tier." This is a tier restriction (plan gate), not a product gap — the feature exists but is gated to Enterprise. Excluded per instructions.

---

### Candidate 4 — Deal-84DBA6 (Prospect People Ops Manager)

**Prospect line:** "We turned on the Slack integration in July, but half our managers still haven't given a single recognition — nobody ever trained them on it."

**Classification: ROLLOUT/ENABLEMENT ISSUE** — excluded from summary

**Reasoning:** The product docs confirm Slack integration is available on all plans. The problem is not that the feature is missing or tier-gated; it is that managers were never trained on using an existing feature. The sales rep's response confirms this: "That's fixable — I'll set up a manager enablement session." This is a rollout/enablement issue, not a product gap. Excluded per instructions.

---

### Candidate 5 — Deal-36C33F (Alex Franklin rep statement)

**Line:** "Full transparency — we don't have a native mobile app for hourly workers yet, though it's on the roadmap."

**Classification: EXCLUDED — rep language, not prospect voice**

**Reasoning:** This line is spoken by Alex Franklin (sales rep), not the prospect. Per instructions, rep language does not count as prospect voice. The follow-up prospect line ("The web version should be fine for our office staff for now.") does not surface a gap — it accepts the web version. Excluded per instructions.

---

## Summary — Real Product Gaps Only

| Deal Alias | Gap | Amount |
|---|---|---|
| Deal-EC3025 | SCIM user provisioning (not supported on any plan) | not provided |
| Deal-D0D6B5 | ADP Workforce Now integration (not supported on any plan) | not provided |

Note: No deal amounts were provided in the transcript or product docs data. Both gaps are confirmed against the product docs excerpt, which explicitly lists SCIM and ADP Workforce Now as NOT currently supported capabilities, while the supported HRIS integrations are Workday, BambooHR, Gusto, and Rippling (Pro and above).
rep-performance
stale-pipeline-by-rep0.67

? The Question

The snapshot date is 2026-09-05. Using the open deals and the engagements-by-deal table, list every open deal with no logged email, call, or meeting in the last 7 days. Compute recency from the engagements table's last_email/last_call/last_meeting fields, NOT from any date field on the deal (the deal's last_contacted_field is unreliable). For each stale deal give: deal alias, owner name, stage, amount, days since last contact. Group by owner, order by amount descending within each owner. Then state, per owner, how many stale deals they carry and the total stale amount. (Do not write outreach text.)
cost $0.0111 · 270s · in 56,050 / out 28,779 tok
✓ stale_recall_ge_80✓ grouped_by_owner✓ jake_top_amount✓ kevin_stale_amt✗ jenn_stale_amt✗ stale_count

✓ Correct Answer (ground truth)

stale_count: 65
jake_amt: 626243.0
kevin_amt: 252905.03
jenn_amt: 261645.0
top_alias: Deal-2D1F1B
top_amount: 240000.0
graded checks: stale_recall_ge_80 · grouped_by_owner · jake_top_amount · kevin_stale_amt · jenn_stale_amt · stale_count

Model's actual answer

show the model's full answer
I have the complete computation. Two deals have no engagement row at all (Deal-3EED2C, Deal-57FF13), and both are correctly treated as stale with days = None (no logged contact). Let me present the final results.

Snapshot date: 2026-09-05. Last 7-day window = 2026-08-29 through 2026-09-05 (inclusive). A deal is stale if its most recent email/call/meeting is on or before 2026-08-28 (i.e., >=7 days since) or there is no logged contact at all. Recency computed from last_email/last_call/last_meeting; all future-dated entries in those fields were excluded (treated as not-yet-completed), and deals whose only engagement dates were future-dated had no valid recent contact. 2 deals have no engagement row at all: Deal-3EED2C (Alex Franklin) and Deal-57FF13 (Elena Sinclair).

=== Bryce Harmon (18 stale deals, total stale amount = 692,964.00) ===
- Deal-2D1F1B   DS1   240,000.00   81 days  (last contact 2026-06-16)
- Deal-66D1FC   DS1    99,000.00   16 days  (last contact 2026-08-20)
- Deal-950043   DS1    70,000.00   19 days  (last contact 2026-08-17)
- Deal-B23205   DS1    45,000.00   16 days  (last contact 2026-08-20)
- Deal-7BBDFA   DS3    37,440.00   46 days  (last contact 2026-07-21)
- Deal-332637   DS2    36,000.00    9 days  (last contact 2026-08-27)
- Deal-1BEEBF   DS1    31,500.00   19 days  (last contact 2026-08-17)
- Deal-A414F6   DS1    25,200.00   19 days  (last contact 2026-08-17)
- Deal-C5658B   DS1    23,400.00   16 days  (last contact 2026-08-20)
- Deal-40522D   DS3    21,000.00   19 days  (last contact 2026-08-17)
- Deal-C1FA6D   DS1    18,000.00   16 days  (last contact 2026-08-20)
- Deal-01E193   DS1    12,600.00    8 days  (last contact 2026-08-28)
- Deal-F0EBBB   DS3    11,400.00   24 days  (last contact 2026-08-12)
- Deal-927338   DS1    10,920.00   18 days  (last contact 2026-08-18)
- Deal-E25A09   DS1     6,000.00    9 days  (last contact 2026-08-27)
- Deal-C9C286   DS2     5,502.00    9 days  (last contact 2026-08-27)
- Deal-012CB1   DS1         1.00   23 days  (last contact 2026-08-13)
- Deal-3795AD   DS2         1.00    8 days  (last contact 2026-08-28)

=== Dana Mercer (16 stale deals, total stale amount = 279,495.00) ===
- Deal-44EA29   DS2    60,000.00   10 days  (last contact 2026-08-26)
- Deal-E51FB7   DS2    43,875.00   12 days  (last contact 2026-08-24)
- Deal-B42F46   DS1    27,000.00   19 days  (last contact 2026-08-17)
- Deal-BA3DDC   DS3    23,400.00   15 days  (last contact 2026-08-21)
- Deal-9DDE86   DS2    20,000.00   15 days  (last contact 2026-08-21)
- Deal-215CCA   DS3    18,900.00   17 days  (last contact 2026-08-19)
- Deal-5EED42   DS3    16,250.00   11 days  (last contact 2026-08-25)
- Deal-57887A   DS2    15,000.00    8 days  (last contact 2026-08-28)
- Deal-944310   DS4    10,500.00   33 days  (last contact 2026-08-03)
- Deal-B7EBD1   DS5     9,000.00   16 days  (last contact 2026-08-20)
- Deal-3974EB   DS4     9,000.00    8 days  (last contact 2026-08-28)
- Deal-F40F04   DS2     8,100.00   15 days  (last contact 2026-08-21)
- Deal-7599B8   DS3     7,350.00   18 days  (last contact 2026-08-18)
- Deal-87DDD1   DS1     5,000.00   19 days  (last contact 2026-08-17)
- Deal-F336B6   DS3     4,200.00   15 days  (last contact 2026-08-21)
- Deal-0660B4   DS4     1,920.00   16 days  (last contact 2026-08-20)

=== Alex Franklin (20 stale deals, total stale amount = 113,936.00) ===
- Deal-CC08D1    DS1    24,000.00   16 days  (last contact 2026-08-20)
- Deal-E73427    DS3    18,000.00   10 days  (last contact 2026-08-26)
- Deal-885F45    DS2     9,300.00   12 days  (last contact 2026-08-24)
- Deal-C2FF3C    DS1     8,316.00   10 days  (last contact 2026-08-26)
- Deal-3EED2C    DS2     7,200.00    —       (no engagement row logged)
- Deal-0D2F7A    DS3     5,100.00   12 days  (last contact 2026-08-24)
- Deal-6C60D4    DS3     4,800.00   12 days  (last contact 2026-08-24)
- Deal-13FEBD    DS2     4,680.00   12 days  (last contact 2026-08-24)
- Deal-819506    DS1     4,400.00    8 days  (last contact 2026-08-28)
- Deal-9D0060    DS3     3,840.00   12 days  (last contact 2026-08-24)
- Deal-690476    DS2     3,600.00   18 days  (last contact 2026-08-18)
- Deal-C6D97A    DS4     3,240.00    8 days  (last contact 2026-08-28)
- Deal-EE195F    DS3     3,120.00    8 days  (last contact 2026-08-28)
- Deal-278DEC    DS3     2,700.00    8 days  (last contact 2026-08-28)
- Deal-635B8E    DS3     2,600.00   18 days  (last contact 2026-08-18)
- Deal-6883F3    DS1     2,400.00   16 days  (last contact 2026-08-20)
- Deal-4A13AD    DS3     2,160.00   26 days  (last contact 2026-08-10)
- Deal-F67D31    DS2     1,800.00    8 days  (last contact 2026-08-28)
- Deal-5FDCE4    DS3     1,600.00   12 days  (last contact 2026-08-24)
- Deal-BA571A    DS4     1,080.00   18 days  (last contact 2026-08-18)

=== Cole Ingram (18 stale deals, total stale amount = 252,905.03) ===
- Deal-D04904    DS2    58,529.25   11 days  (last contact 2026-08-25)
- Deal-B25F40    DS3    40,000.00    8 days  (last contact 2026-08-28)
- Deal-813836    DS2    32,175.00   11 days  (last contact 2026-08-25)
- Deal-1BA595    DS2    31,750.00   11 days  (last contact 2026-08-25)
- Deal-CFE1E8    DS3    18,000.00   11 days  (last contact 2026-08-25)
- Deal-CD47A6    DS2    12,168.00   11 days  (last contact 2026-08-25)
- Deal-627646    DS3    11,193.00   11 days  (last contact 2026-08-25)
- Deal-FF809F    DS2     7,781.20   11 days  (last contact 2026-08-25)
- Deal-AF932D    DS2     7,225.40   11 days  (last contact 2026-08-25)
- Deal-A71728    DS2     6,947.50   11 days  (last contact 2026-08-25)
- Deal-8BC9F5    DS2     5,616.00   10 days  (last contact 2026-08-26)
- Deal-175395    DS3     4,779.88   11 days  (last contact 2026-08-25)
- Deal-481E24    DS3     4,140.00   10 days  (last contact 2026-08-26)
- Deal-C7F9BF    DS2     3,360.00   11 days  (last contact 2026-08-25)
- Deal-2F3A66    DS3     3,334.80   11 days  (last contact 2026-08-25)
- Deal-342E96    DS2     2,700.00   24 days  (last contact 2026-08-12)
- Deal-E568D5    DS3     1,875.00   11 days  (last contact 2026-08-25)
- Deal-FD9F4E    DS5     1,330.00   10 days  (last contact 2026-08-26)

=== Farid Osman (2 stale deals, total stale amount = 2,881.00) ===
- Deal-8BA24E   DS1     2,880.00    8 days  (last contact 2026-08-28)
- Deal-8FDCD2   DS1         1.00   15 days  (last contact 2026-08-21)

=== Elena Sinclair (1 stale deal, total stale amount = 2,100.00) ===
- Deal-57FF13   DS1     2,100.00    —       (no engagement row logged)

---

Per-owner summary:
- Bryce Harmon: 18 stale deals, total stale amount = 692,964.00
- Dana Mercer: 16 stale deals, total stale amount = 279,495.00
- Alex Franklin: 20 stale deals, total stale amount = 113,936.00
- Cole Ingram: 18 stale deals, total stale amount = 252,905.03
- Farid Osman: 2 stale deals, total stale amount = 2,881.00
- Elena Sinclair: 1 stale deal, total stale amount = 2,100.00

Totals across all owners: 75 stale deals, sum of stale amount = 1,344,276.03.

Notes / data limitations:
- Deal-3EED2C (Alex Franklin) and Deal-57FF13 (Elena Sinclair) have no row in engagements_by_deal_90d.csv at all, so days since last contact is not computable (shown as "—"); both are treated as stale (no logged email/call/meeting within 7 days, or at all).
- For deals with engagement rows whose only contact dates fall after the snapshot (2026-09-05), future-dated entries were excluded from recency and those deals are stale because there is no valid past contact within 7 days. None of the deals fell into this edge case in the final output.
- Owner name comes from owners.csv. Archived owners (Gavin Porter, Hugo Lindqvist) have no open deals in this snapshot.
activity-mix-vs-outcomePASS 1.00

? The Question

The snapshot date is 2026-09-05. For each sales rep (owner), compute the last-30-day totals of emails, calls, and meetings from the engagements table, and the number of deals that entered DS2 in the last 30 days (t_ds2 within 30 days of the snapshot). Report per rep: the activity mix as percentages (emails/calls/meetings share of that rep's total activities), total activities per DS2 entry, and rank the reps by that efficiency ratio (lowest activities per DS2 entry = most efficient). Name the most efficient rep and the highest-volume rep, and note if they differ. Do not attribute cause.
cost $0.0110 · 140s · in 40,888 / out 12,312 tok
✓ justin_ds2✓ justin_volume_high✓ efficient_named✓ mix_present✓ no_causal_claim

✓ Correct Answer (ground truth)

justin_ds2: 4
graded checks: justin_ds2 · justin_volume_high · efficient_named · mix_present · no_causal_claim

Model's actual answer

show the model's full answer
Now the analysis is clean and correct. Here are the final results.

## Method

- Snapshot date: 2026-09-05. Last-30-day window = 2026-08-06 through 2026-09-05 (inclusive), i.e. `snapshot - 30 days <= t_ds2 <= snapshot`.
- Per-rep activity totals: join deals to the engagements table on `deal_id`, summing `emails_30d + inbound_emails_30d` (all `inbound_emails_30d` are 0), `calls_30d`, and `meetings_30d`.
- DS2 entry count per rep = number of that rep's deals with a non-null `t_ds2` in the 30-day window.
- Efficiency ratio = total activities / DS2 entries (lowest = most efficient).
- Activity mix = each of {emails, calls, meetings} as a percentage of the rep's total activities.
- Archived owners (Gavin Porter, Hugo Lindqvist) excluded. Elena Sinclair has 1 deal, 0 recorded activities and 0 DS2 entries in window; her ratio is undefined (N/A).

## Per-rep results (sorted by efficiency, ascending)

| Rank | Rep | Emails | Calls | Meetings | Total Activities | DS2 Entries | Act / DS2 | Email % | Call % | Meet % | # Deals |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Alex Franklin | 307 | 36 | 41 | 384 | 18 | 21.33 | 79.9% | 9.4% | 10.7% | 67 |
| 2 | Bryce Harmon | 162 | 0 | 43 | 205 | 4 | 51.25 | 79.0% | 0.0% | 21.0% | 35 |
| 3 | Cole Ingram | 96 | 14 | 1 | 111 | 2 | 55.50 | 86.5% | 12.6% | 0.9% | 22 |
| 4 | Farid Osman | 38 | 0 | 34 | 72 | 1 | 72.00 | 52.8% | 0.0% | 47.2% | 7 |
| 5 | Dana Mercer | 84 | 18 | 11 | 113 | 1 | 113.00 | 74.3% | 15.9% | 9.7% | 24 |
| 6 | Elena Sinclair | 0 | 0 | 0 | 0 | 0 | N/A | — | — | — | 1 |

## DS2 entries in window (2026-08-06 to 2026-09-05), by rep

- Alex Franklin: 18 deals (Deal-EE195F, Deal-D9A72E, Deal-7FA0C3, Deal-E531A6, Deal-36C33F, Deal-D1E6C2, Deal-317E6F, Deal-4F775F, Deal-F436DA, Deal-CA5E44, Deal-46988D, Deal-5296C9, Deal-898FC5, Deal-E73427, Deal-403845, Deal-92D97D, Deal-1FC049, Deal-3EED2C)
- Bryce Harmon: 4 deals (Deal-1CCE5C, Deal-25F752, Deal-CA7DC0, Deal-D73B89)
- Cole Ingram: 2 deals (Deal-1BA595, Deal-42326B)
- Farid Osman: 1 deal (Deal-499BF6)
- Dana Mercer: 1 deal (Deal-57887A)
- Elena Sinclair: 0 deals

## Most efficient vs. highest volume

- Most efficient rep: Alex Franklin, 21.33 activities/DS2 entry (384 activities / 18 DS2 entries).
- Highest-volume rep: Alex Franklin, 384 total activities.
- They are the same person.

No cause is attributed. The reproducible script is saved at /Users/amaniphipps/analyze_activities.py.
ae-qtd-scorecardPASS 1.00

? The Question

Build a QTD scorecard for the named AE as of 2026-09-05: bookings vs quota with attainment percent, new vs expansion split, active pipeline by stage with amount, rolling 90-day DS2-to-won rate, win and loss counts with top loss reason, activity volume by type in the last 30 days. Exclude any closed-won deal dated before the quarter. Close with three coaching observations grounded in the numbers.
cost $0.0092 · 137s · in 68,173 / out 9,546 tok
✓ bookings✓ quota✓ attainment✓ new_expansion_split✓ top_loss_reason✓ coaching_obs

✓ Correct Answer (ground truth)

owner: Alex Franklin
quarter: 2026-Q3
snapshot_date: 2026-09-05
bookings: 150000
quota: 200000
attainment_pct: 75.0
new_bookings: 113500
new_count: 5
expansion_bookings: 36500
expansion_count: 3
pipeline_by_stage: {"DS1": {"count": 20, "amount": 284621}, "DS2": {"count": 28, "amount": 353760}, "DS3": {"count": 67, "amount": 552705}, "DS4": {"count": 5, "amount": 23574}, "DS5": {"count": 5, "amount": 45730}}
open_deals_count: 125
ds2_to_won: window_start=2026-06-07; won_in_window=8; closed_in_window=35; rate=0.2286
wins_qtd: 8
losses_qtd: 27
top_loss_reason: reason=Lost- Timing (1 year or more); count=13
activity_30d_totals: emails_30d=807; calls_30d=112; meetings_30d=128; notes_30d=50
prior_quarter_excluded: deal_alias=Deal-B3E6F1; close_date=2026-06-20; amount=24000
allowed_new: 18 items — e.g. Deal-A1C3E5, Deal-B7D2F4, Deal-C9E1A6, Deal-D4B8C2
graded checks: bookings · quota · attainment · new_expansion_split · top_loss_reason · coaching_obs

Model's actual answer

show the model's full answer
QTD Scorecard — Alex Franklin — as of 2026-09-05

QUOTA
  2026-Q3 quota: $200,000 (snapshot 2026-09-05)

1. BOOKINGS vs QUOTA
  Bookings (closed-won, close_date in [2026-07-01, 2026-09-05]): $150,000
  Attainment: 75.0%  (150,000 / 200,000)
  - New:        $113,500
  - Expansion:  $36,500
  QTD won deals: 8 (one pre-quarter won deal on 2026-06-20 excluded)
    Deal-A1C3E5   close=07-15  new        $40,000
    Deal-B7D2F4   close=07-31  new        $35,000
    Deal-F2C7D8   close=07-24  expansion  $20,000
    Deal-C9E1A6   close=08-12  new        $21,000
    Deal-A8B4D6   close=08-19  expansion  $12,000
    Deal-D4B8C2   close=08-21  new        $11,000
    Deal-E6F3A9   close=09-02  new         $6,500
    Deal-C5D9E2   close=09-03  expansion   $4,500

2. ACTIVE PIPELINE by STAGE (open, as of 2026-09-05)
  DS1: $284,621  (20 deals)
  DS2: $353,760  (28 deals)
  DS3: $552,705  (67 deals)
  DS4: $  23,574  (5 deals)
  DS5: $  45,730  (5 deals)
  TOTAL: $1,260,390  (125 deals)

3. ROLLING 90-DAY DS2-to-WON RATE
  Window: 2026-06-07 to 2026-09-05  (111 deals entered DS2 in window)
  Won: 8  Lost: 27  Still open: 76
  Rate: 7.2%  (8 won / 111 entered)
  [Among 35 resolved: 22.9% win rate]

4. WIN / LOSS COUNTS (all-time for Alex)
  Wins: 9  Losses: 27
  Top loss reasons:
    13x  Lost- Timing (1 year or more)
     5x  Competitor
     5x  MIA
     2x  Lost DM
     1x  Feature Request
     1x  Lost- Does not fit ICP (write in notes)

5. ACTIVITY VOLUME (last 30 days, all deals)
  Emails: 807  Calls: 112  Meetings: 128  Notes: 50

COACHING OBSERVATIONS

  1. Conversion funnel is leaking at the back. The AE entered 111 deals into
     DS2 over the trailing 90 days but only 8 (7.2%) have won. Of the 35 deals
     that have resolved, 27 (77%) were lost — vs 8 won (23%). The 27 losses
     are concentrated in timing (13 x "Lost-Timing 1+ year") and competitors
     (5 x "Competitor") and MIA (5 x). Alex is advancing deals to DS2 but
     not closing them — activity volume (128 meetings / 112 calls / 807
     emails in 30 days) is high, but meetings and calls produce no downstream
     closes. This suggests a qualification or late-cycle execution gap rather
     than prospecting effort.

  2. Pipeline is large but sits shallow. The active book is $1.26M across 125
     open deals, yet $939k of that (75%) sits in DS1/DS2 ($638k) and only
     $23.6k sits in DS4. DS5 (intent-to-buy, executive review) holds just
     $45.7k. The QTD win total is $150k against a $1.26M open book — the
     velocity from DS2-to-close is the constraint, not pipeline quantity.

  3. On track for the quarter but not the pace needed. At 75% of Q3 quota with
     the quarter essentially over (as of Sep 5), Alex needs every remaining
     open deal to convert. The 8 QTD wins came entirely from deals that
     entered DS2 at least 2+ weeks before quarter-end (DS2 entry Jul 9–Aug 10).
     The 76 still-open DS2-entered deals — many entered in Aug/Sep — are
     unlikely to close in Q3 given the observed 7.2% conversion. Coaching
     focus: push the 5 DS4 + 5 DS5 deals to finish, and surface the 8 QTD
     wins' playbooks (Deal-A1C3E5, Deal-B7D2F4, etc.) to replicate on the
     shallow DS2/DS3 inventory before the quarter closes.
multithreading-gapPASS 1.00

? The Question

Find every open deal that is single-threaded (fewer than 2 active contacts) or under-threaded (fewer than 3, or all contacts in one persona). Active means engaged in the last 60 days and not marked former. Personas: economic buyer, champion, HR admin, IT security, finance. For each flagged deal: amount, stage, active contact count, personas present, personas missing, the most valuable persona to add given the stage, and any on-file unengaged contact at that company who fits; otherwise say none on file.
cost $0.0073 · 229s · in 39,166 / out 16,519 tok
✓ flagged_recall✓ persona_language✓ single_thread_term✓ on_file_fit

✓ Correct Answer (ground truth)

active_window_start: 2026-07-07
persona_universe: economic buyer, champion, HR admin, IT security, finance
flag_rule: flag if active_contacts < 2 (single-threaded) or active_contacts < 3 or all active contacts share one persona (under-threaded); active = engaged within 60 days and not former
best_persona_rule: DS2/DS3: economic buyer > IT security > finance > HR admin > champion; DS4/DS5: economic buyer > finance > IT security > HR admin > champion; first persona not present among active contacts
flagged: deal_alias=Deal-36C33F; amount=15000; stage=DS2; active_count=1; reason=single-threaded; best_persona_to_add=economic buyer | deal_alias=Deal-50D386; amount=36000; stage=DS2; active_count=2; reason=under-threaded; best_persona_to_add=economic buyer | deal_alias=Deal-5408B0; amount=14850; stage=DS4; active_count=2; reason=under-threaded; best_persona_to_add=economic buyer | deal_alias=Deal-5BFE3B; amount=18000; stage=DS2; active_count=2; reason=under-threaded; best_persona_to_add=economic buyer | deal_alias=Deal-885F45; amount=9300; stage=DS2; active_count=2; reason=under-threaded; best_persona_to_add=IT security | deal_alias=Deal-92D97D; amount=60000; stage=DS2; active_count=1; reason=single-threaded; best_persona_to_add=economic buyer … (+5 more)
healthy_not_flagged: Deal-4B0BEB, Deal-84DBA6, Deal-D348E1
allowed_new: 59 items — e.g. C-077A0E, C-0D15DF, C-1BDFE0, C-2AE3AA
graded checks: flagged_recall · persona_language · single_thread_term · on_file_fit

Model's actual answer

show the model's full answer
Here are the results.

Method: Active = engaged on or after 2026-07-08 (ref date 2026-09-06 minus 60 days) and not former. I joined deal_id against ~/deals_open.csv for amount and stage. 3 deals' IDs were not found in deals_open.csv, so amount/stage is "NOT FOUND" for those.

Reference date: 2026-09-06 | Active cutoff (>=): 2026-07-08
Deals analyzed: 14 | Flagged: 11

=== Deal-36C33F ===
  amount: NOT FOUND
  stage: NOT FOUND
  active contact count: 1
  personas present: IT security
  personas missing: economic buyer, champion, HR admin, finance
  flag: single-threaded (<2 active contacts)
  most valuable persona to add: economic buyer
  on-file unengaged contact at C-077A0E fitting economic buyer: CT-1DB73E (Chief People Officer, economic buyer)

=== Deal-50D386 ===
  amount: NOT FOUND
  stage: NOT FOUND
  active contact count: 2
  personas present: HR admin, champion
  personas missing: economic buyer, IT security, finance
  flag: under-threaded (<3 active contacts)
  most valuable persona to add: economic buyer
  on-file unengaged contact at C-EB10E4 fitting economic buyer: CT-A1C4B3 (Chief People Officer, economic buyer)

=== Deal-5408B0 ===
  amount: 14850
  stage: DS4
  active contact count: 2
  personas present: HR admin, champion
  personas missing: economic buyer, IT security, finance
  flag: under-threaded (<3 active contacts)
  most valuable persona to add: economic buyer
  on-file unengaged contact at C-2AE3AA fitting economic buyer: CT-07FA76 (Chief People Officer, economic buyer)

=== Deal-5BFE3B ===
  amount: NOT FOUND
  stage: NOT FOUND
  active contact count: 2
  personas present: champion
  personas missing: economic buyer, HR admin, IT security, finance
  flag: under-threaded (<3 active): all active contacts in one persona (champion)
  most valuable persona to add: economic buyer
  on-file unengaged contact at C-535D36 fitting economic buyer: none on file

=== Deal-885F45 ===
  amount: NOT FOUND
  stage: NOT FOUND
  active contact count: 2
  personas present: champion, economic buyer
  personas missing: HR admin, IT security, finance
  flag: under-threaded (<3 active contacts)
  most valuable persona to add: finance
  on-file unengaged contact at C-5E8EFB fitting finance: none on file
  (other unengaged contacts on file at C-5E8EFB: CT-B3F25D (IT Security Lead, IT security))

=== Deal-92D97D ===
  amount: 60000
  stage: DS2
  active contact count: 1
  personas present: HR admin
  personas missing: economic buyer, champion, IT security, finance
  flag: single-threaded (<2 active contacts)
  most valuable persona to add: economic buyer
  on-file unengaged contact at C-E23238 fitting economic buyer: none on file

=== Deal-C6D97A ===
  amount: 3240
  stage: DS4
  active contact count: 3
  personas present: champion
  personas missing: economic buyer, HR admin, IT security, finance
  flag: under-threaded (<3 active): all active contacts in one persona (champion)
  most valuable persona to add: economic buyer
  on-file unengaged contact at C-5A8FC2 fitting economic buyer: none on file

=== Deal-D0D6B5 ===
  amount: NOT FOUND
  stage: NOT FOUND
  active contact count: 3
  personas present: champion
  personas missing: economic buyer, HR admin, IT security, finance
  flag: under-threaded (<3 active): all active contacts in one persona (champion)
  most valuable persona to add: economic buyer
  on-file unengaged contact at C-32918E fitting economic buyer: CT-1FA4DB (Chief People Officer, economic buyer)

=== Deal-EC3025 ===
  amount: NOT FOUND
  stage: NOT FOUND
  active contact count: 1
  personas present: champion
  personas missing: economic buyer, HR admin, IT security, finance
  flag: single-threaded (<2 active contacts)
  most valuable persona to add: economic buyer
  on-file unengaged contact at C-FDD0C7 fitting economic buyer: CT-6827DB (Chief People Officer, economic buyer)

=== Deal-F9A08A ===
  amount: 2484
  stage: DS4
  active contact count: 1
  personas present: champion
  personas missing: economic buyer, HR admin, IT security, finance
  flag: single-threaded (<2 active contacts)
  most valuable persona to add: economic buyer
  on-file unengaged contact at C-0D15DF fitting economic buyer: CT-697541 (Chief People Officer, economic buyer)

=== Deal-FCBE5B ===
  amount: NOT FOUND
  stage: NOT FOUND
  active contact count: 1
  personas present: champion
  personas missing: economic buyer, HR admin, IT security, finance
  flag: single-threaded (<2 active contacts)
  most valuable persona to add: economic buyer
  on-file unengaged contact at C-737030 fitting economic buyer: none on file

--- Not flagged (sufficient threading) ---
  Deal-84DBA6: amount=NOT FOUND stage=NOT FOUND active=3 personas=IT security,champion,economic buyer
  Deal-4B0BEB: amount=12000 stage=DS2 active=4 personas=HR admin,champion,economic buyer,finance
  Deal-D348E1: amount=13770 stage=DS5 active=5 personas=HR admin,IT security,champion,economic buyer,finance

Summary: 11 flagged deals, 3 not flagged. For 6 of the flagged deals the recommended economic buyer contact is on file (unengaged); for the other 5, no matching persona exists in the unengaged_contacts file. Amount/stage could not be retrieved for 8 of 14 deals because their deal_id values are absent from ~/deals_open.csv (Deal-36C33F, Deal-5BFE3B, Deal-885F45, Deal-D0D6B5, Deal-EC3025, Deal-FCBE5B, and also the two non-flagged deals Deal-84DBA6 and Deal-F9A08A).
talk-track-from-transcriptsPASS 1.00

? The Question

From this rep's last 10 calls: what they lead with in the first five minutes, how they handle the three most common objections, how often a concrete next step is agreed (rate), and every competitor a prospect raised. Quote at most one line per finding and only lines that appear in the transcripts. End with two coaching notes.
cost $0.0030 · 136s · in 8,787 / out 10,416 tok
✓ objection_count✓ next_step_rate✓ competitors✓ coaching✓ quote_used

✓ Correct Answer (ground truth)

rep: Alex Franklin
n_transcripts: 10
opener_theme: theme=opens with a customer recognition win story (400-person retailer, 18% turnover reduction); count=8; total=10; quote=Quick one to kick off: a 400-person retailer we work with cut regretted turnover 18% in two quarters after automating milestone awards, and I wanted to walk you through exactly how they did it.; transcript_id=TT-001
objections: objection=budget; count=4; quote=Honestly, budget is locked until next fiscal year — I can't add a new line item right now.; transcript_id=TT-001 | objection=timing; count=3; quote=This is good, but can we revisit it next quarter? Open enrollment starts in October and we're underwater.; transcript_id=TT-002 | objection=in-house; count=3; quote=We already do recognition with a spreadsheet and quarterly gift cards — why would we change?; transcript_id=TT-004
objection_count: 3
next_step: agreed=7; total=10; rate=0.7
competitors_prospect_raised: Awardco, Kudos
competitor_quotes: Awardco=We're also in late talks with Awardco — their rewards catalog looks bigger than yours.; Kudos=How are you different from Kudos? Our CEO used them at her last company.
rep_raised_competitors_excluded: Workhuman
allowed_new: 13 items — e.g. TT-001, TT-002, TT-003, TT-004
graded checks: objection_count · next_step_rate · competitors · coaching · quote_used

Model's actual answer

show the model's full answer
Note: the on-disk file at /Users/amaniphipps/transcripts.csv contains 6 transcripts (TX-001 through TX-006) with different content and no `minute` column. I analyzed the 10 transcripts (TT-001 through TT-010) you pasted inline, since your question references "the last 10 calls." Flagging this in case the on-disk file is the one you actually want me to use.

WHAT THEY LEAD WITH (first 5 min)
8 of 10 calls open with the identical retailer social-proof hook:
"Quick one to kick off: a 400-person retailer we work with cut regretted turnover 18% in two quarters after automating milestone awards, and I wanted to walk you through exactly how they did it."
The other 2 open deal-specific: TT-004 ("I put together a short agenda — security review first, then pricing.") and TT-009 ("You asked for straight pricing last time, so let's start there."). TT-005 adds a mid-opening Workhuman pricing contrast at minute 2.

THREE MOST COMMON OBJECTIONS & HOW THEY ARE HANDLED
1. Budget locked — 4 calls (TT-001, TT-003, TT-006, TT-010)
   Handling: "Totally fair. Most teams fund this out of turnover savings — that retailer saved about $210k in avoided backfills, which is how their finance team signed off."
2. Spreadsheet / gift cards status quo — 3 calls (TT-004, TT-007, TT-009)
   Handling: "Spreadsheets work until they scale — the difference is automation: milestones fire without HR lifting a finger, and you get analytics on who is being recognized."
3. Revisit next quarter — 3 calls (TT-002, TT-005, TT-008)
   Handling: "Makes sense. What if we scope a 90-day pilot with one department so you have internal data before next quarter's planning?"

NEXT-STEP AGREEMENT RATE
7 of 10 calls agree to a Thursday 2pm working session. The confirming line is identical each time: "Yes, Thursday at 2pm works — send the invite and I'll bring our HRIS manager."
TT-004, TT-007, TT-010 do not commit (committee deferral or "need to think").
Rate: 7/10 = 70%.

COMPETITORS RAISED BY PROSPECTS
- Awardco (TT-003): "We're also in late talks with Awardco — their rewards catalog looks bigger than yours."
- Kudos (TT-007): "How are you different from Kudos? Our CEO used them at her last company."
(Workhuman is named by Alex in TT-005, not raised by the prospect.)

COACHING NOTES
1. The budget-locked objection is met with a single repeated line — the $210k turnover-savings figure — across all 4 occurrences. If finance pushes back on that specific number, there is no secondary funding angle to fall back on.
2. All three no-next-step calls (TT-004, TT-007, TT-010) end in a deferral — a budget committee or "need to think" — and Alex accepts without probing the decision-maker's timeline or securing an interim value touchpoint.
reporting-analytics
q3-weighted-forecastPASS 1.00

? The Question

Below are the open deals in the default sales pipeline with a close date between 2026-07-01 and 2026-10-15, pulled from the CRM warehouse on 2026-09-05. Stages: DS1 (discovery) through DS5 (contract). Build the Q3 2026 forecast (quarter = 2026-07-01 to 2026-09-30). Weighting rule: 100 percent of COMMIT plus 35 percent of BEST_CASE. PIPELINE deals count zero. Only deals with a close date inside the quarter count. Report: COMMIT total, BEST_CASE total, weighted forecast, count of deals in each category inside the quarter, the deals you excluded for being outside the quarter (count and total amount), and the top 5 BEST_CASE deals by amount inside the quarter. Show arithmetic. Then, in a final '## Data quality' section of no more than four sentences, name the data quality problems in this extract that would make the forecast unreliable if it were run unattended.
cost $0.0058 · 99s · in 37,336 / out 8,426 tok
✓ commit_total_44729✓ best_case_total_203565✓ weighted_115976.75✓ commit_count_7✓ best_case_count_24✓ excluded_count_32✓ excluded_amount_227575✓ top5_aliases✓ flags_blank_owner✓ flags_missing_why_buys

✓ Correct Answer (ground truth)

graded checks: commit_total_44729 · best_case_total_203565 · weighted_115976.75 · commit_count_7 · best_case_count_24 · excluded_count_32 · excluded_amount_227575 · top5_aliases · flags_blank_owner · flags_missing_why_buys

Model's actual answer

show the model's full answer
# Q3 2026 Forecast

Quarter: 2026-07-01 to 2026-09-30.  Total deals in extract: 86.

## Deals inside the quarter (count by forecast category)

| Category | Count | Amount total |
|---|---|---|
| COMMIT | 7 | 44,729.00 |
| BEST_CASE | 24 | 203,565.00 |
| PIPELINE | 23 | 201,637.40 |
| All inside | 54 | — |

## Arithmetic

COMMIT total = 11,200 + 9,000 + 9,000 + 6,360 + 5,400 + 2,520 + 1,249 = 44,729.00

BEST_CASE total = 38,935 + 24,000 + 19,656 + 16,250 + 11,116 + 10,800 + 10,500 + 9,890 + 9,720 + 7,200 + 5,400 + 3,840 + 3,780 + 3,600 + 3,600 + 3,600 + 3,240 + 3,150 + 3,120 + 3,060 + 2,916 + 2,760 + 2,100 + 1,920 + 1,080 + 528 = 203,565.00

Weighted forecast = 100% of COMMIT + 35% of BEST_CASE
  = 44,729.00 + 0.35 × 203,565.00
  = 44,729.00 + 71,247.75
  = 115,976.75

**Weighted forecast: 115,976.75**

## Top 5 BEST_CASE deals inside the quarter (by amount)

1. Deal-2D7423 — 38,935.00 — close 2026-09-30
2. Deal-25F752 — 24,000.00 — close 2026-09-25
3. Deal-E53952 — 19,656.00 — close 2026-09-30
4. Deal-5EED42 — 16,250.00 — close 2026-09-30
5. Deal-FA32A0 — 11,116.00 — close 2026-09-25

## Excluded deals (close date outside the quarter)

Count: 32.  Total amount: 227,575.00.

These 32 deals all have close dates in October 2026 (2026-10-01 through 2026-10-15), falling outside the 2026-07-01 to 2026-09-30 window. Of these, 1 is COMMIT (Deal-D348E1, 13,770.00) and 9 are BEST_CASE; the remaining 22 are PIPELINE. PIPELINE deals contribute zero to the weighted forecast regardless, but the 1 excluded COMMIT and 9 excluded BEST_CASE would have added 13,770.00 + 7,830.00 (5 + 2,400 + 2,916 + 3,600 + 5,160 + 3,600 + 3,600 + 3,600 + 1,600 + 1,080 = 29,464.75; weighted = 29,464.75 × 0.35) — but since they close in Q4, they belong to the Q4 forecast, not Q3.

## Data quality

The owner field is blank for 85 of 86 deals (only Deal-C9C286 is populated with "Bryce Harmon"), making it impossible to attribute forecast by rep or hold owners accountable. 15 deals carry non-zero why_buys_chars values in the 270–2,824 range with no documented encoding meaning, so that signal is unusable without a data dictionary. The extract includes deals with close dates far into the future (2026-10-01 to 2026-10-15) that a naive "all deals" query would sweep into the Q3 forecast, biasing it upward; the quarter-boundary filter must be applied explicitly. Finally, Deal-42326B has a non-integer amount (2,480.40) suggesting possible currency precision issues that should be reconciled.
aha-moment-2x2PASS 1.00

? The Question

Test the activation hypothesis on the company cohort below: companies with 5+ unique givers (m1_users >= 5) AND 1+ successful redemption (m1_redemptions >= 1) in their first calendar month retain better at 24 months than those with one signal or neither. Every company in this extract is already 25+ months old, so current_status = 'active' means retained at 24 months. Report the full 2x2 (cohort size and 24-month retention rate for: both signals, givers-only, redemption-only, neither), state how many companies were excluded from the denominator and why (if none, say so), name the single signal with the largest retention lift, and state what this does and does not prove.
cost $0.0085 · 359s · in 30,710 / out 25,779 tok
✓ both_66✓ givers_47✓ redemp_31✓ neither_40✓ excluded_0✓ largest_lift_givers

✓ Correct Answer (ground truth)

graded checks: both_66 · givers_47 · redemp_31 · neither_40 · excluded_0 · largest_lift_givers

Model's actual answer

show the model's full answer
## 2x2 Activation Hypothesis Results

**Excluded from denominator: 0 companies.** All 220 companies had complete data for m1_users, m1_redemptions, and current_status.

### Full 2x2 Matrix

| Cohort | n | Retained (24-mo) | 24-mo Retention Rate |
|---|------|---|
| **Both signals** (m1_users >= 5 AND m1_redemptions >= 1) | 47 | 31 | 65.96% (31/47) |
| **Givers-only** (m1_users >= 5 AND m1_redemptions = 0) | 49 | 23 | 46.94% (23/49) |
| **Redemption-only** (m1_users < 5 AND m1_redemptions >= 1) | 29 | 9 | 31.03% (9/29) |
| **Neither signal** (m1_users < 5 AND m1_redemptions = 0) | 95 | 38 | 40.00% (38/95) |

### Single-signal lift (vs. its absence)

- **Givers signal** (m1_users >= 5): 56.25% retain vs. 37.90% without — **lift = +18.35 pp**
- Redemption signal (m1_redemptions >= 1): 52.63% retain vs. 42.36% without — lift = +10.27 pp

**The single signal with the largest retention lift is the givers signal (m1_users >= 5), at +18.35 percentage points.**

### What this does and does not prove

**This proves:** Within this cohort of 220 companies (all 25+ months old as of the snapshot), there is a strong association between first-month depth of usage (5+ unique givers) and 24-month retention. Companies with both activation signals retained at the highest rate (65.96%). The givers signal alone produced the largest single-signal retention lift (+18.35 pp), and companies with both signals retained substantially better than those with neither (65.96% vs. 40.00%).

**This does not prove** causation — this is an observational, correlational analysis. Retained companies may differ from churned ones on unmeasured confounders (company size, industry, product tier, integration adoption, pricing, sales motion quality, etc.). The givers-only cohort (46.94%) actually retained better than the redemption-only cohort (31.03%), meaning having many users but no redemptions still beat having redemptions with few users — but this could reflect that companies with 5+ users are inherently larger/more established. Additionally, this is a single cohort snapshot with no time-to-event modeling, no censoring adjustment, and no control for the `signup_month` cohort effects. The activation hypothesis is supported by the pattern but not confirmed as causal.

⚠️ File-mutation verifier: 1 file(s) were NOT modified this turn despite any wording above that may suggest otherwise. Run `git status` or `read_file` to confirm.
  • `/Users/amaniphippis/plg_company_cohort_2023.csv` — [write_file] Failed to write file: mkdir: /Users/amaniphippis: Permission denied
arr-rollforward-reconciliation0.60

? The Question

Reconcile ARR between CRM company records and billing subscriptions as of 2026-09-05. Billing ARR is MRR x 12. Report both totals, the variance, and a decomposition into buckets (status mismatch, rounding, missing records, other) that sums exactly to the variance. List each mismatched account with a suggested owner. Business rule: any subscription with a term other than 12 months must have cf_agreement_end_date populated; list violations.
cost $0.0061 · 210s · in 27,072 / out 17,140 tok
✓ billing_total✓ crm_total✗ variance✗ buckets_sum✓ violations_listed

✓ Correct Answer (ground truth)

as_of: 2026-09-05
billing_arr_total: 604739.28
crm_arr_total: 603581.76
variance: 1157.52
variance_definition: billing_arr_total - crm_arr_total (billing ARR = sum of MRR x 12 over status=active subscriptions)
buckets: {"status_mismatch": {"amount": "-13158.48", "accounts": [{"company_alias": "C-0C8323BF", "billing_arr": "0.00", "hubspot_arr": "4905.24", "diff": "-4905.24", "suggested_owner": "Dana Mercer"}, {"compa
bucket_sum_equals_variance: True
n_mismatched_accounts: 7
rule: term_months != 12 requires cf_agreement_end_date
violations: subscription_id=SUB-0002; company_alias=C-1794A52C; term_months=24 | subscription_id=SUB-0019; company_alias=C-22170CA1; term_months=36
n_companies: 40
graded checks: billing_total · crm_total · variance · buckets_sum · violations_listed

Model's actual answer

show the model's full answer
Here is the reconciliation as of 2026-09-05. All figures use the literal data in the two files; Billing ARR = MRR x 12 (no term discount).

== TOTALS ==
Total Billing ARR (Chargebee, MRR x 12):     617,897.76
Total CRM ARR (HubSpot):                    603,581.76
Variance (CRM - Billing):                  -14,316.00

== VARIANCE DECOMPOSITION (sums exactly) ==
Missing records:              -11,952.00
Status mismatch:                    0.00
Rounding:                           0.00
Other:                         -2,364.00
                             ----------
Sum of buckets:              -14,316.00   = Variance (check passes)

== BUCKET BREAKDOWN ==

1) Missing records (-11,952.00)
   - Billing-only: C-21629AA4 — billing ARR 28,449.24 (SUB-0004, MRR 2370.77 x 12), no CRM record.
   - CRM-only: C-0D5BBE3A — CRM ARR 16,497.24, no billing subscription.
   - Net: 16,497.24 - 28,449.24 = -11,952.00

2) Status mismatch (0.00)
   - C-0C8323BF (SUB-000E, cancelled): CRM 4,905.24 vs billing 4,905.24 -> diff 0.00
   - C-0DC4FB8C (SUB-000F, cancelled): CRM 8,253.24 vs billing 8,253.24 -> diff 0.00
   - Both cancelled accounts happen to have identical ARR on both sides; no variance contribution.

3) Rounding (0.00)
   - No active shared accounts have |CRM - Billing| < 1.00.

4) Other (-2,364.00) — active shared accounts with non-round differences
   - C-0D66DF9E: billing 23,184.00 vs CRM 23,200.00 -> +16.00
   - C-0F7269D7: billing 26,796.00 vs CRM 24,396.00 -> -2,400.00
   - C-14D70CE0: billing 18,180.00 vs CRM 18,200.00 -> +20.00
   - Subtotal: 16.00 - 2,400.00 + 20.00 = -2,364.00

== MISMATCHED ACCOUNTS == (active shared, diff != 0)
   Account      | Billing ARR | CRM ARR   | Diff    | Suggested owner
   C-0D66DF9E    |  23,184.00  | 23,200.00 | +16.00 | RevOps / Ops (data entry)
   C-0F7269D7    |  26,796.00  | 24,396.00 | -2,400.00 | RevOps / Ops (pricing/discount gap)
   C-14D70CE0    |  18,180.00  | 18,200.00 | +20.00 | RevOps / Ops (data entry)

   + Billing-only not in CRM: C-21629AA4 (Billing ARR 28,449.24). Suggested owner: RevOps (ingest into CRM).
   + CRM-only not in billing: C-0D5BBE3A (CRM ARR 16,497.24). Suggested owner: RevOps / Sales Ops (confirm subscription exists or remove CRM record).

== BUSINESS RULE: term != 12 requires cf_agreement_end_date ==
Non-12-month subscriptions: 4 of 39

   Subscription | Company alias | Term (months) | cf_agreement_end_date | Status
   SUB-000C     | C-0DB48281    | 24            | 2027-11-30            | active  (OK)
   SUB-001A     | C-0FC4DBB8    | 36            | 2027-11-30            | active  (OK)
   SUB-0002     | C-1794A52C    | 24            | (empty)               | active  (VIOLATION)
   SUB-0019     | C-22170CA1    | 36            | (empty)               | active  (VIOLATION)

Violations (2): SUB-0002 (C-1794A52C, term 24) and SUB-0019 (C-22170CA1, term 36) both have non-12-month terms with no cf_agreement_end_date populated.

Note on cancelled subscriptions: C-0C8323BF (SUB-000E) and C-0DC4FB8C (SUB-000F) are cancelled but still carry active-amount ARR on both systems with no agreement end dates. I did not flag these as business-rule violations since their term is 12 months; however, a cancelled subscription with an empty cf_agreement_end_date may warrant its own review outside the stated rule.
kvm-month-over-monthPASS 1.00

? The Question

Report the four core KVMs for 2026-08 against 2026-07: giving rate, redemptions per user, 1:1 meetings engagement, pulse check engagement. For each: value, prior value, absolute and relative change, direction. Name the KVM with the largest relative move and the segment (plan_tier or size_band) driving it if the data supports that; otherwise say the data does not. Under 200 words plus one table.
cost $0.0013 · 21s · in 8,023 / out 1,444 tok
✓ four_kvms✓ biggest_mover_named✓ direction_correct✓ segment_named✓ word_cap

✓ Correct Answer (ground truth)

aggregation: simple mean of company-level values per month
kvms: {"giving_rate": {"2026-07": 0.6023, "2026-08": 0.6027, "abs_change": 0.0004, "rel_change_pct": 0.07, "direction": "up"}, "redemptions_per_user": {"2026-07": 1.73, "2026-08": 1.7302, "abs_change": 0.00
biggest_mover: pulse_engagement
driving_segment: size_band=enterprise; 2026-07=0.55; 2026-08=0.2743; rel_change_pct=-50.13
segment_detail: {"smb": {"2026-07": 0.6588, "2026-08": 0.6573, "rel_change_pct": -0.23}, "mid_market": {"2026-07": 0.593, "2026-08": 0.5942, "rel_change_pct": 0.2}, "enterprise": {"2026-07": 0.55, "2026-08": 0.2743,
n_company_rows_per_month: 30
graded checks: four_kvms · biggest_mover_named · direction_correct · segment_named · word_cap

Model's actual answer

show the model's full answer
Pulse check engagement is the KVM with the largest relative move: -50.13% in enterprise, which drives the overall -15.31%. The other three KVMs are essentially flat (~0% change).

| KVM | 2026-08 | 2026-07 | Abs. change | Rel. change | Direction |
|---|---|---|---|---|---|
| Giving rate | 0.6027 | 0.6023 | +0.0004 | +0.07% | Up |
| Redemptions per user | 1.7302 | 1.7300 | +0.0002 | +0.01% | Up |
| 1:1 meetings engagement | 0.4472 | 0.4469 | +0.0003 | +0.06% | Up |
| Pulse check engagement | 0.5086 | 0.6006 | -0.0920 | -15.31% | Down |

Largest relative move: pulse check engagement (-15.31% overall), driven by the enterprise size_band (-50.13%), where engagement dropped from 0.5500 to 0.2743.
rewards-ytd-sectionPASS 1.00

? The Question

Produce the redemption section of the weekly rewards report through the last completed month (2026-08): redemption count, spend, unique redeemers, redemptions per redeemer, provider mix as percent of spend, top 5 countries by redemptions. Say which month is the last completed month. Provider shares must sum to 100.
cost $0.0101 · 288s · in 64,304 / out 22,195 tok
✓ count✓ spend✓ unique_redeemers✓ month_named✓ provider_mix✓ top_country

✓ Correct Answer (ground truth)

redemption_count: 378
total_spend_usd: 27846.00
unique_redeemers: 235
redemptions_per_redeemer: 1.61
provider_mix_pct_of_spend: TangoCard=11.6; Tremendous=30.54; custom=39.05; Snappy=18.81
top5_countries_by_count: country=US; count=244 | country=CA; count=24 | country=AU; count=21 | country=GB; count=17 | country=NL; count=17
graded checks: count · spend · unique_redeemers · month_named · provider_mix · top_country

Model's actual answer

show the model's full answer
## Redemption Report — through 2026-08 (last completed month)

**Last completed month:** August 2026 (2026-08).

**Summary metrics:**
- Redemption count: 378
- Total spend: $27,751.00
- Unique redeemers: 235
- Redemptions per redeemer: 378 / 235 = 1.61

**Provider mix (percent of spend):**
| Provider | Spend | % of Spend |
|---|---|---|
| custom | $10,873.00 | 39.2% |
| Tremendous | $8,410.00 | 30.3% |
| Snappy | $5,238.00 | 18.9% |
| TangoCard | $3,230.00 | 11.6% |
| **Total** | **$27,751.00** | **100.0%** |

**Top 5 countries by redemptions (count):**
1. US: 245
2. CA: 24
3. AU: 21
4. NL: 17
5. GB: 16

*All 378 rows fall between 2026-01 and 2026-08 inclusive. Provider shares sum to 100.0%. Note: Grok/x-ai models are permanently excluded from all benchmarking and comparisons (Sep 2026).*
customer-success
churn-save-eligibilityPASS 1.00

? The Question

Which at-risk accounts qualify for a churn-save offer under the documented eligibility rules, what amount is at stake per account and in total, and which play fits each (usage revival, executive touch, commercial concession)? Cite the signal that justifies each play. List accounts that look at risk but do not qualify and why.
cost $0.0033 · 156s · in 10,634 / out 10,719 tok
✓ eligible_set✓ total_at_stake✓ plays_cited✓ noneligible_named✓ rules_applied

✓ Correct Answer (ground truth)

snapshot_date: 2026-09-05
rules: health_score < 60, churn_save_eligible_amount > 0, renewal within 120 days of snapshot
eligible: account_alias=C-0F6C0F34; amount_at_stake=49707.00; play=executive touch; justifying_signal=champion_active is false - no executive sponsor engaged | account_alias=C-0B827671; amount_at_stake=25365.00; play=usage revival; justifying_signal=usage_trend_3m=declining over the last 3 months | account_alias=C-0B360C78; amount_at_stake=35748.00; play=commercial concession; justifying_signal=usage stable/growing with seat utilization 75% - risk is commercial, not adoption | account_alias=C-0B0F1BAB; amount_at_stake=5494.00; play=executive touch; justifying_signal=champion_active is false - no executive sponsor engaged | account_alias=C-0CA21961; amount_at_stake=16829.00; play=usage revival; justifying_signal=seat utilization 26% is below 50% | account_alias=C-0E9C27D1; amount_at_stake=41235.00; play=commercial concession; justifying_signal=usage stable/growing with seat utilization 85% - risk is commercial, not adoption … (+2 more)
total_amount_at_stake: 224601.00
non_eligible_at_risk: account_alias=C-0BC71BDD; health_score=55 | account_alias=C-0BA71F12; health_score=52 | account_alias=C-0F6694C3; health_score=43 | account_alias=C-0BE96399; health_score=54 | account_alias=C-0F876796; health_score=47 | account_alias=C-0FCCD2DF; health_score=43 … (+1 more)
n_accounts: 30
graded checks: eligible_set · total_at_stake · plays_cited · noneligible_named · rules_applied

Model's actual answer

show the model's full answer
Snapshot date: 2026-09-05. Renewal-window rule R3 = renewal within 120 days => window end 2027-01-03 (inclusive). An account is at-risk for churn-save when it passes R1 (health_score<60), R2 (churn_save_eligible_amount>0), and R3 (renewal within window).

Qualifying accounts (8) — total at stake $224,601.00

| Account | Health | Eligible $ | Renewal | Usage 3m | Champion | Play | Justifying signal |
|---|---|---|---|---|---|---|---|
| C-0F6C0F34 | 51 | $49,707.00 | 2026-10-03 | growing | false | executive touch | champion_active=false (R1+R2+R3 pass) |
| C-0B827671 | 56 | $25,365.00 | 2026-11-14 | declining | true | usage revival | usage_trend_3m=declining |
| C-0B360C78 | 57 | $35,748.00 | 2026-10-28 | growing | true | commercial concession | healthy signals, offer discount to retain |
| C-0B0F1BAB | 38 | $5,494.00 | 2026-09-23 | flat | false | executive touch | champion_active=false |
| C-0CA21961 | 58 | $16,829.00 | 2026-12-28 | flat | true | commercial concession | usage flat, champion active, price-led retention |
| C-0E9C27D1 | 39 | $41,235.00 | 2026-09-24 | flat | true | commercial concession | usage flat, large book at risk on price |
| C-0CEF69FD | 53 | $32,621.00 | 2026-11-21 | growing | false | executive touch | champion_active=false |
| C-0D3278C7 | 54 | $17,602.00 | 2026-11-12 | declining | true | usage revival | usage_trend_3m=declining |

At-risk accounts that do NOT qualify and why

| Account | Health | Eligible $ | Renewal | Why disqualified |
|---|---|---|---|---|
| C-0BC71BDD | 55 | $0.00 | 2026-10-27 | churn_save_eligible_amount=0 (R2 fails) |
| C-0BA71F12 | 52 | $6,824.00 | 2027-04-11 | renewal 128 days out, outside 120-day window (R3 fails) |
| C-0F6694C3 | 43 | $0.00 | 2027-03-21 | eligible amount=0 (R2 fails) AND renewal outside window (R3 fails) |
| C-0BE96399 | 54 | $0.00 | 2026-10-29 | churn_save_eligible_amount=0 (R2 fails) |
| C-0F876796 | 47 | $19,958.00 | 2027-02-06 | renewal 153 days out, outside 120-day window (R3 fails) |
| C-0FCCD2DF | 43 | $0.00 | 2027-04-23 | eligible amount=0 (R2 fails) AND renewal outside window (R3 fails) |
| C-10A56B0F | 54 | $0.00 | 2026-12-12 | churn_save_eligible_amount=0 (R2 fails) |

All remaining accounts have health_score >= 60 and are therefore not at-risk under R1, so they are out of scope for churn-save entirely.

Summary: 8 accounts qualify, $224,601.00 at stake total.
expansion-kitPASS 1.00

? The Question

A customer replied to outreach (reply included). Build the expansion kit: seat coverage (licensed over headcount), two lines on usage health, headroom in seats and ARR at the current per-seat rate, who replied and whether they can buy, the right buyer from our contacts, and a reply email under 150 words that moves toward the right person without being pushy and cites one real usage fact.
cost $0.0007 · 37s · in 468 / out 2,846 tok
✓ coverage_pct✓ headroom_arr✓ right_buyer✓ replier_not_buyer✓ usage_fact✓ email_under_150

✓ Correct Answer (ground truth)

company_alias: C-0DDFC9A7
coverage_pct: 37.5
headroom_seats: 250
per_seat_annual_rate: 60.00
headroom_arr: 15000.00
replier: Maria S.
replier_can_buy: False
right_buyer: name=Dana R.; title=VP People
usage_facts: active_users_2026_08=126; active_users_2026_03=88; growth_pct_6m=43.2; licensed_seat_utilization_pct=84.0
usage_health_lines: Monthly active users grew from 88 (2026-03) to 126 (2026-08), +43.2% over 6 months., Last month 126 of 150 licensed seats were active (84.0% utilization), approaching the seat cap.
graded checks: coverage_pct · headroom_arr · right_buyer · replier_not_buyer · usage_fact · email_under_150

Model's actual answer

show the model's full answer
---

# Expansion Kit — C-0DDFC9A7

## 1. Seat Coverage
- Licensed seats: 150
- Headcount: 400
- Coverage = 150 / 400 = **37.5%**
- Only 37.5% of employees currently have licenses.

## 2. Usage Health (two lines)
- **MAU grew 88 (Mar) → 126 (Aug), +38 users (+43.2% over 6 months), +7.6 users/month on average.**
- **August active users (126) represent 84% of 150 licensed seats — near-full seat utilization.**

## 3. Headroom (at current per-seat rate)
- Per-seat rate = $9,000 / 150 = **$60.00/seat**
- Seat headroom to cover full headcount = 400 − 150 = **250 seats**
- ARR headroom = 250 × $60 = **$15,000**
- Note: They are not yet at their licensed seat limit (126/150 = 84%).

## 4. Who Replied & Can They Buy?
- **Maria S.** (People Operations Coordinator) replied on 2026-09-02.
- She explicitly stated she is **NOT** the right person for purchasing decisions: *"Budget and seat expansion sit with Dana R., our VP People."*
- **She cannot buy** — she is a referral point.

## 5. Right Buyer From Our Contacts
- **Dana R., VP People** — holds budget authority and seat expansion decisions per Maria's reply.
- Dana is also "been asking about our usage numbers lately" (per Maria).
- Last engaged: **2026-05-18** (~3.5 months ago — a warm but slightly stale lead).

## 6. Reply Email (under 150 words)

> Hi Maria,
>
> Thanks for the note — great to hear the team loves Bonusly and the recognition feed stays busy.
>
> Quick data point: your active users grew from 88 to 126 this summer (84% of your 150 licensed seats), showing strong organic adoption.
>
> Would you be open to connecting me with Dana? I'd be happy to share how we can right-size your seat coverage as your team grows. No pressure at all.
>
> Best,
> Cole

Word count: 80.
onboarding-statusPASS 1.00

? The Question

Prep the CSM for the mid-onboarding call: what is complete (integrations connected, allowance set, admins added, first recognition given, first redemption), what is not, early engagement signals, and the three things to cover on the call. Mark nothing complete without a data field that shows it.
cost $0.0004 · 20s · in 535 / out 1,284 tok
✓ complete_items✓ incomplete_items✓ three_items✓ grounded

✓ Correct Answer (ground truth)

company_alias: C-0D284E42
days_live: 25
checklist: {"slack_integration_connected": {"complete": true, "field": "integration_slack", "value": "2026-08-12"}, "hris_integration_connected": {"complete": false, "field": "integration_hris", "value": ""}, "a
engagement_signals: avg_active_givers_week1=4.3; avg_active_givers_last_week=13.0; latest_active_givers=15; days_with_activity=25
call_items: Connect the HRIS integration (integration_hris is empty) to automate user provisioning and offboarding., Get to first redemption (first_redemption_at is empty): walk admins through the rewards catalog and confirm allowance funding., Build the giving habit: daily active givers averaged 4.3 in week 1 vs 13.0 last week - agree on manager nudges and a recognition cadence.
graded checks: complete_items · incomplete_items · three_items · grounded

Model's actual answer

show the model's full answer
Company: C-0D284E42 (signup 2026-08-11). Here is the status derived solely from the CSV data fields.

## Complete

1. Slack integration connected — integration_slack = 2026-08-12
2. Allowance set — allowance_set = 2026-08-13
3. Admins added — admins_added = 2
4. First recognition given — first_recognition_at = 2026-08-15 14:22

## Not complete

1. HRIS integration — integration_hris is blank (no value)
2. First redemption — first_redemption_at is blank (no value)

## Early engagement signals

- Active givers: 3 on 2026-08-11 (signup day) → 15 on 2026-09-04 (latest record). That is +12 givers, +400%.
- Growth is generally upward but with stalls:
  - 8/23–8/25 flat at 9
  - 8/29 dipped to 11 from the 13 seen on 8/31's prior day (8/28=10, 8/29=11, 8/30=13, 8/31=11)
- First recognition occurred on 8/15. From 8/16 through 9/4 (19 days of usage records after first recognition), there is no redemption on record.

## Three things to cover on the mid-onboarding call

1. HRIS integration is still unconnected (integration_hris is empty). With Slack already live and admins in place, walk through the HRIS connector to complete the data sync.
2. First redemption has not occurred (first_redemption_at is blank). Even though active givers rose to 15 by 9/4, the incentive loop is not yet closed — confirm the recipient experience and nudge the first redeemer.
3. Giver momentum grew 3→15 (+400%) but stalled at 8/23–8/25. Review which givers are active vs. idle from the usage trend and identify a plan to re-activate the stalled tail while sustaining the recent climb to 15.
renewal-risk-conflicting-datesPASS 1.00

? The Question

Produce a 90-day renewal risk brief. Two systems hold renewal dates and disagree on some accounts; decide which to trust per account and say why (multi-year contracts are known to be wrong in ChurnZero). For every renewal: company, CSM, ARR, date used, seat utilization, 3-month usage trend, risk rating with one sentence of evidence. Flag every disagreement. Close with total ARR renewing and ARR at risk.
cost $0.0083 · 234s · in 39,858 / out 21,122 tok
✓ total_renewing✓ arr_at_risk✓ disagreements_flagged✓ trust_rule

✓ Correct Answer (ground truth)

snapshot_date: 2026-09-05
window: 2026-09-05 to 2026-12-04
trust_rule: multi-year contracts: Chargebee is authoritative (ChurnZero known wrong); otherwise systems agree or Chargebee wins
accounts: 20 items — e.g. account_alias=C-0B144C78; csm=Cole Ingram; arr=30899.00; trusted_renewal_date=2026-11-02; trusted_source_why=systems agree (annual term); in_90d_window=True; dates_disagree=False; seat_utilization_pct=75.4; usage_3m_ratio=1.03; risk=low; evidence=3-month usage ratio 1.03 (last3 avg 103 vs prior3 100), seat utilization 75% | account_alias=C-0B20DB64; csm=Dana Mercer; arr=21770.00; trusted_renewal_date=2026-10-07; trusted_source_why=systems agree (annual term); in_90d_window=True; dates_disagree=False; seat_utilization_pct=56.6; usage_3m_ratio=1.0; risk=medium; evidence=3-month usage ratio 1.00 (last3 avg 295 vs prior3 295), seat utilization 57% | account_alias=C-0B344485; csm=Elena Sinclair; arr=64384.00; trusted_renewal_date=2026-11-16; trusted_source_why=systems agree (annual term); in_90d_window=True; dates_disagree=False; seat_utilization_pct=78.0; usage_3m_ratio=1.04; risk=low; evidence=3-month usage ratio 1.04 (last3 avg 241 vs prior3 231), seat utilization 78% | account_alias=C-0B369871; csm=Dana Mercer; arr=85128.00; trusted_renewal_date=2026-10-29; trusted_source_why=systems agree (annual term); in_90d_window=True; dates_disagree=False; seat_utilization_pct=75.1; usage_3m_ratio=1.04; risk=low; evidence=3-month usage ratio 1.04 (last3 avg 330 vs prior3 316), seat utilization 75%
disagreements: account_alias=C-0B7D2C30; cz_renewal_date=2026-09-10; cb_renewal_date=2026-09-15; is_multi_year=True; trusted=2026-09-15 | account_alias=C-0BBE3E60; cz_renewal_date=2027-09-26; cb_renewal_date=2026-09-26; is_multi_year=True; trusted=2026-09-26 | account_alias=C-0BCDB8C2; cz_renewal_date=2027-09-18; cb_renewal_date=2026-09-18; is_multi_year=True; trusted=2026-09-18 | account_alias=C-0D2AB865; cz_renewal_date=2026-09-10; cb_renewal_date=2026-09-22; is_multi_year=True; trusted=2026-09-22 | account_alias=C-0F5D2323; cz_renewal_date=2026-09-10; cb_renewal_date=2026-09-29; is_multi_year=True; trusted=2026-09-29
n_disagreements: 5
total_arr_renewing_90d: 1048715.00
arr_at_risk_90d: 359409.00
n_accounts: 20
graded checks: total_renewing · arr_at_risk · disagreements_flagged · trust_rule

Model's actual answer

show the model's full answer
The analysis is complete. Here is the corrected brief — the usage_12m.csv only contains data for 3 accounts (all multi-year), so for the remaining 17 accounts, seat utilization alone drives the risk rating.

---

90-DAY RENEWAL RISK BRIEF
As of: September 2026 | Window: Sep 15 - Nov 30, 2026
Total accounts renewing: 20 | Total ARR: $1,048,715.00

DATA LIMITATION: usage_12m.csv contains 12-month active user data for only 3 of 20 accounts (C-0B7D2C30, C-0BCDB8C2, C-0D2AB865 — all multi-year contracts). For the remaining 17 accounts, seat utilization is the sole engagement signal.

---

Company: C-0B144C78 | CSM: Cole Ingram | ARR: $30,899.00
  Renewal Date: 2026-11-02 [ChurnZero = Chargebee (agreed)]
  Seat Utilization: 169/224 = 75.4% | 3-Month Usage Trend: data not available
  Risk Rating: LOW
  Evidence: Healthy seat utilization at 75.4% (169/224) with no usage trend data available, but utilization alone indicates sufficient engagement to support renewal.

Company: C-0B20DB64 | CSM: Dana Mercer | ARR: $21,770.00
  Renewal Date: 2026-10-07 [ChurnZero = Chargebee (agreed)]
  Seat Utilization: 214/378 = 56.6% | 3-Month Usage Trend: data not available
  Risk Rating: MEDIUM
  Evidence: Seat utilization is 56.6% (214/378 seats used) with no usage trend data, representing moderate renewal risk that warrants proactive outreach to confirm engagement levels.

Company: C-0B344485 | CSM: Elena Sinclair | ARR: $64,384.00
  Renewal Date: 2026-11-16 [ChurnZero = Chargebee (agreed)]
  Seat Utilization: 224/287 = 78.0% | 3-Month Usage Trend: data not available
  Risk Rating: LOW
  Evidence: Healthy seat utilization at 78.0% (224/287) with no usage trend data, but utilization is sufficient to support renewal confidence.

Company: C-0B369871 | CSM: Dana Mercer | ARR: $85,128.00
  Renewal Date: 2026-10-29 [ChurnZero = Chargebee (agreed)]
  Seat Utilization: 317/422 = 75.1% | 3-Month Usage Trend: data not available
  Risk Rating: LOW
  Evidence: Healthy seat utilization at 75.1% (317/422) with no usage trend data, but utilization is sufficient to support renewal confidence.

Company: C-0B7A7546 | CSM: Elena Sinclair | ARR: $35,062.00
  Renewal Date: 2026-10-25 [ChurnZero = Chargebee (agreed)]
  Seat Utilization: 182/205 = 88.8% | 3-Month Usage Trend: data not available
  Risk Rating: LOW
  Evidence: Healthy seat utilization at 88.8% (182/205) with no usage trend data, but utilization is sufficient to support renewal confidence.

Company: C-0B7D2C30 | CSM: Dana Mercer | ARR: $65,901.00
  Renewal Date: 2026-09-15 [Chargebee (multi-year; CZ known wrong)]
  *** DISAGREEMENT FLAGGED *** CZ=2026-09-10 vs CB=2026-09-15
  Seat Utilization: 274/476 = 57.6% | 3-Month Usage Trend: 97 -> 94 -> 84 (-13.4%)
  Risk Rating: HIGH
  Evidence: Three-month active-user trend is declining 13.4% (Jun 97 -> Jul 94 -> Aug 84) with seat utilization at 57.6% (274/476), indicating engagement erosion ahead of renewal.

Company: C-0BBC4E7A | CSM: Cole Ingram | ARR: $56,374.00
  Renewal Date: 2026-10-10 [ChurnZero = Chargebee (agreed)]
  Seat Utilization: 228/337 = 67.7% | 3-Month Usage Trend: data not available
  Risk Rating: LOW
  Evidence: Healthy seat utilization at 67.7% (228/337) with no usage trend data, but utilization is sufficient to support renewal confidence.

Company: C-0BBE3E60 | CSM: Dana Mercer | ARR: $30,993.00
  Renewal Date: 2026-09-26 [Chargebee (multi-year; CZ known wrong)]
  *** DISAGREEMENT FLAGGED *** CZ=2027-09-26 vs CB=2026-09-26
  Seat Utilization: 74/114 = 64.9% | 3-Month Usage Trend: data not available
  Risk Rating: MEDIUM
  Evidence: Seat utilization is 64.9% (74/114 seats used) with no usage trend data, representing moderate renewal risk that warrants proactive outreach.

Company: C-0BC34584 | CSM: Cole Ingram | ARR: $16,740.00
  Renewal Date: 2026-10-22 [ChurnZero = Chargebee (agreed)]
  Seat Utilization: 327/494 = 66.2% | 3-Month Usage Trend: data not available
  Risk Rating: LOW
  Evidence: Healthy seat utilization at 66.2% (327/494) with no usage trend data, but utilization is sufficient to support renewal confidence.

Company: C-0BCDB8C2 | CSM: Cole Ingram | ARR: $54,427.00
  Renewal Date: 2026-09-18 [Chargebee (multi-year; CZ known wrong)]
  *** DISAGREEMENT FLAGGED *** CZ=2027-09-18 vs CB=2026-09-18
  Seat Utilization: 232/424 = 54.7% | 3-Month Usage Trend: 127 -> 118 -> 110 (-13.4%)
  Risk Rating: HIGH
  Evidence: Three-month active-user trend is declining 13.4% (Jun 127 -> Jul 118 -> Aug 110) with seat utilization at 54.7% (232/424), indicating engagement erosion ahead of renewal.

Company: C-0CB2C1B4 | CSM: Dana Mercer | ARR: $40,628.00
  Renewal Date: 2026-11-20 [ChurnZero = Chargebee (agreed)]
  Seat Utilization: 386/473 = 81.6% | 3-Month Usage Trend: data not available
  Risk Rating: LOW
  Evidence: Healthy seat utilization at 81.6% (386/473) with no usage trend data, but utilization is sufficient to support renewal confidence.

Company: C-0D2AB865 | CSM: Elena Sinclair | ARR: $38,022.00
  Renewal Date: 2026-09-22 [Chargebee (multi-year; CZ known wrong)]
  *** DISAGREEMENT FLAGGED *** CZ=2026-09-10 vs CB=2026-09-22
  Seat Utilization: 250/407 = 61.4% | 3-Month Usage Trend: 125 -> 117 -> 109 (-12.8%)
  Risk Rating: HIGH
  Evidence: Three-month active-user trend is declining 12.8% (Jun 125 -> Jul 117 -> Aug 109) with seat utilization at 61.4% (250/407), indicating engagement erosion ahead of renewal.

Company: C-0D5BBE3A | CSM: Dana Mercer | ARR: $39,740.00
  Renewal Date: 2026-11-09 [ChurnZero = Chargebee (agreed)]
  Seat Utilization: 85/102 = 83.3% | 3-Month Usage Trend: data not available
  Risk Rating: LOW
  Evidence: Healthy seat utilization at 83.3% (85/102) with no usage trend data, but utilization is sufficient to support renewal confidence.

Company: C-0EC6999D | CSM: Elena Sinclair | ARR: $79,419.00
  Renewal Date: 2026-10-03 [ChurnZero = Chargebee (agreed)]
  Seat Utilization: 31/112 = 27.7% | 3-Month Usage Trend: data not available
  Risk Rating: HIGH
  Evidence: Seat utilization is only 27.7% (31/112 seats used) with no usage trend data to confirm engagement, signaling an underutilized license at risk of contraction or non-renewal.

Company: C-0F5D2323 | CSM: Cole Ingram | ARR: $90,647.00
  Renewal Date: 2026-09-29 [Chargebee (multi-year; CZ known wrong)]
  *** DISAGREEMENT FLAGGED *** CZ=2026-09-10 vs CB=2026-09-29
  Seat Utilization: 111/390 = 28.5% | 3-Month Usage Trend: data not available
  Risk Rating: HIGH
  Evidence: Seat utilization is only 28.5% (111/390 seats used) with no usage trend data to confirm engagement, signaling an underutilized license at risk of contraction or non-renewal.

Company: C-0F9F8F13 | CSM: Dana Mercer | ARR: $46,230.00
  Renewal Date: 2026-10-18 [ChurnZero = Chargebee (agreed)]
  Seat Utilization: 199/352 = 56.5% | 3-Month Usage Trend: data not available
  Risk Rating: MEDIUM
  Evidence: Seat utilization is 56.5% (199/352 seats used) with no usage trend data, representing moderate renewal risk that warrants proactive outreach.

Company: C-0FB9D5AF | CSM: Cole Ingram | ARR: $63,158.00
  Renewal Date: 2026-11-13 [ChurnZero = Chargebee (agreed)]
  Seat Utilization: 144/199 = 72.4% | 3-Month Usage Trend: data not available
  Risk Rating: LOW
  Evidence: Healthy seat utilization at 72.4% (144/199) with no usage trend data, but utilization is sufficient to support renewal confidence.

Company: C-0FC4DBB8 | CSM: Elena Sinclair | ARR: $94,732.00
  Renewal Date: 2026-11-05 [ChurnZero = Chargebee (agreed)]
  Seat Utilization: 356/464 = 76.7% | 3-Month Usage Trend: data not available
  Risk Rating: LOW
  Evidence: Healthy seat utilization at 76.7% (356/464) with no usage trend data, but utilization is sufficient to support renewal confidence.

Company: C-0FD551AB | CSM: Elena Sinclair | ARR: $48,815.00
  Renewal Date: 2026-10-14 [ChurnZero = Chargebee (agreed)]
  Seat Utilization: 210/376 = 55.9% | 3-Month Usage Trend: data not available
  Risk Rating: MEDIUM
  Evidence: Seat utilization is 55.9% (210/376 seats used) with no usage trend data, representing moderate renewal risk that warrants proactive outreach.

Company: C-22170CA1 | CSM: Cole Ingram | ARR: $45,646.00
  Renewal Date: 2026-11-24 [ChurnZero = Chargebee (agreed)]
  Seat Utilization: 251/294 = 85.4% | 3-Month Usage Trend: data not available
  Risk Rating: LOW
  Evidence: Healthy seat utilization at 85.4% (251/294) with no usage trend data, but utilization is sufficient to support renewal confidence.

---

DISAGREEMENTS (5 flagged, all multi-year):

All 5 disagreements involve multi-year contracts. Per the known data quality issue, ChurnZero is wrong for multi-year accounts, so Chargebee (the billing system of record) is trusted in every case:

1. C-0B7D2C30: CZ=2026-09-10 vs CB=2026-09-15 -> Trusted Chargebee (36-month contract; CZ known wrong)
2. C-0BBE3E60: CZ=2027-09-26 vs CB=2026-09-26 -> Trusted Chargebee (24-month contract; CZ known wrong)
3. C-0BCDB8C2: CZ=2027-09-18 vs CB=2026-09-18 -> Trusted Chargebee (36-month contract; CZ known wrong)
4. C-0D2AB865: CZ=2026-09-10 vs CB=2026-09-22 -> Trusted Chargebee (24-month contract; CZ known wrong)
5. C-0F5D2323: CZ=2026-09-10 vs CB=2026-09-29 -> Trusted Chargebee (24-month contract; CZ known wrong)

For all 15 single-year accounts, CZ and CB dates agree, so no trust decision was needed.

---

RISK DISTRIBUTION:

HIGH risk (5 accounts): $328,416.00 ARR at risk
  - C-0B7D2C30 ($65,901) — declining usage -13.4% + 57.6% util
  - C-0BCDB8C2 ($54,427) — declining usage -13.4% + 54.7% util
  - C-0D2AB865 ($38,022) — declining usage -12.8% + 61.4% util
  - C-0EC6999D ($79,419) — 27.7% seat utilization
  - C-0F5D2323 ($90,647) — 28.5% seat utilization

MEDIUM risk (4 accounts): $147,808.00 ARR
  - C-0B20DB64 ($21,770) — 56.6% util
  - C-0BBE3E60 ($30,993) — 64.9% util
  - C-0F9F8F13 ($46,230) — 56.5% util
  - C-0FD551AB ($48,815) — 55.9% util

LOW risk (11 accounts): $572,491.00 ARR

---

BOTTOM LINE:

Total ARR renewing (next 90 days): $1,048,715.00
ARR at risk (HIGH risk accounts): $328,416.00
ARR at elevated risk (HIGH + MEDIUM): $476,224.00
ticket-theme-synthesis0.60

? The Question

Synthesize the quarter's support tickets into 4 to 7 themes. Do not trust existing tags; read the text. Per theme: name, count, share, distinct accounts, ARR affected, two ticket ids, one-line recommendation. Separate broad patterns from single-account noise and rank by ARR exposure, not volume.
cost $0.0101 · 422s · in 40,465 / out 33,548 tok
✗ theme_count_range✓ counts_verifiable✗ single_account_flagged✓ arr_ranked✓ ticket_ids

✓ Correct Answer (ground truth)

quarter_window: 2026-06-01 through 2026-08-31
total_tickets: 80
themes: {"points_not_posting": {"count": 20, "share_pct": 25.0, "distinct_accounts": 9, "arr_affected": "31100.00", "example_ticket_ids": ["IC-460001", "IC-460002"], "single_account": false}, "slack_sync": {"
ranking_by_arr_exposure: hris_provisioning, redemption_failures, billing_dispute, points_not_posting, slack_sync
single_account_noise_theme: billing_dispute
note: existing tags are noisy/unreliable; classify by text
sample_ticket_ids: 80 items — e.g. IC-460004, IC-460016, IC-460001, IC-460041
graded checks: theme_count_range · counts_verifiable · single_account_flagged · arr_ranked · ticket_ids

Model's actual answer

show the model's full answer
All 80 tickets verified unique with no duplicate IDs, all body texts mapped. Counts sum to 80. ARR is deduplicated per account (each account counted once per theme at its listed ARR). Here is the synthesis, ranked by ARR exposure.

---

1. HRIS provisioning failures
Count: 12 / 80 (15.0%) | Distinct accounts: 3 | ARR affected: $114,000
Accounts: C-0B2213A9 ($36,000), C-0F6C0F34 ($30,000), C-0DDFC9A7 ($48,000)
Ticket IDs: IC-460059, IC-460055
Recommendation: Automate HRIS provisioning with retry + escalation; add health checks on new-hire sync pipelines.

2. Billing/invoice discrepancies
Count: 17 / 80 (21.2%) | Distinct accounts: 2 | ARR affected: $54,900
Accounts: C-0E9C27D1 ($52,000), C-21FEBCBB ($2,900)
Ticket IDs: IC-460071, IC-460069
Note: Broad pattern across 2 accounts; C-0E9C27D1 alone accounts for 15 of 17 tickets and $52k of the $54.9k. C-21FEBCBB ($2.9k) is borderline noise but repeats the same seat-count error.
Recommendation: Audit billing engine seat-count and tier-pricing logic; implement pre-bill invoice approval workflow.

3. Redemption/checkout failures
Count: 13 / 80 (16.2%) | Distinct accounts: 5 | ARR affected: $48,900
Accounts: C-0CEF69FD ($8,900), C-0B827671 ($10,700), C-0F876796 ($8,700), C-0FCCD2DF ($9,600), C-14264ABD ($11,000)
Ticket IDs: IC-460025, IC-460030
Recommendation: Implement idempotent checkout with transaction rollback on failure; add timeout and error-rate monitoring on the redemption flow.

4. Gift card order errors
Count: 5 / 80 (6.2%) | Distinct accounts: 4 | ARR affected: $38,200
Accounts: C-0FCCD2DF ($9,600), C-0F876796 ($8,700), C-0D9CA315 ($9,600), C-0B0F1BAB ($10,300)
Ticket IDs: IC-460024, IC-460023
Recommendation: Ensure order atomicity — only deduct points after successful gift card confirmation; add compensation workflow for failed-but-charged orders.

5. Points not posting
Count: 19 / 80 (23.8%) | Distinct accounts: 8 | ARR affected: $28,200
Accounts: C-0D3278C7 ($3,500), C-0BF20542 ($4,500), C-0D0B047C ($4,500), C-0BE96399 ($2,700), C-0D284E42 ($3,400), C-0D6CC8E3 ($4,200), C-0DD0626C ($2,500), C-0B2895EF ($2,900)
Ticket IDs: IC-460004, IC-460016
Recommendation: Investigate points ledger reconciliation pipeline; add async worker retry with dead-letter queue for recognition-to-balance posting.

6. Slack integration/sync failures
Count: 14 / 80 (17.5%) | Distinct accounts: 4 | ARR affected: $18,900
Accounts: C-0B843542 ($4,400), C-0BA71F12 ($3,900), C-10A56B0F ($5,400), C-8C2E8F00 ($5,200)
Ticket IDs: IC-460041, IC-460047
Recommendation: Add connection health monitoring with automated re-auth; persist sync toggle state to prevent silent resets.

---

Arithmetic summary: all 80 tickets classified, 0 unmapped, 0 duplicate IDs. ARR totals verified: $114,000 + $54,900 + $48,900 + $38,200 + $28,200 + $18,900 = $299,700 total ARR across all affected accounts. Top 2 themes (HRIS provisioning + billing discrepancies) account for $168,900 of the $299,700 ARR exposure (56.4%) and 29 of 80 tickets (36.3%).
marketing
case-study-match0.75

? The Question

Given the prospect, rank the three most similar existing customers with public case studies for use as social proof. Similarity on industry, size band, use case, region. Cite the fields that drove each match. Do not name any customer without a case study and never include billing data or contact names.
cost $0.0004 · 16s · in 728 / out 876 tok
✓ top3✗ decoys_excluded✓ cites_fields✓ no_billing

✓ Correct Answer (ground truth)

top3: company_alias=C-11C31562; similarity_score=3 | company_alias=C-64171065; similarity_score=3 | company_alias=C-A13C193D; similarity_score=3
decoys_excluded: company_alias=C-D6217CAA; similarity_score=4; reason=no_case_study | company_alias=C-C153868F; similarity_score=4; reason=no_case_study
constraints: no_billing_data, no_contact_names, only_customers_with_has_case_study_true_named
prospect_fields_used: industry, size_band, use_case, region
prospect_alias: C-82AF3719
graded checks: top3 · decoys_excluded · cites_fields · no_billing

Model's actual answer

show the model's full answer
Ranked by total matching fields (3 fields each, all tied). I'll use field-count as the tiebreaker (all 3 match), then list by customer_alias.

Ranking of existing customers with public case studies, by similarity to prospect C-82AF3719 (Technology, Mid-Market, employee_recognition, NA-West):

1. C-7047C549 — Wait, no case study. Excluding.
Let me re-filter to has_case_study=true only first.

Customers with has_case_study=true that are Technology, Mid-Market, employee_recognition, NA-West:
- C-64171065: Technology, Mid-Market, employee_recognition, NA-East, true → matches industry, size_band, use_case (3 fields). Region differs.
- C-D6217CAA: Technology, Mid-Market, employee_recognition, NA-West, false → excluded (no case study)
- C-A13C193D: Technology, Mid-Market, retention, NA-West, true → matches industry, size_band, region (3 fields). Use case differs.

So the two strongest are C-64171065 and C-A13C193D. I need a third. Let me scan all case-study customers for 2-field matches:

- C-4D73F23A: Technology, SMB, employee_recognition, NA-East, false → excluded (no case study)
- C-C153868F: Technology, Mid-Market, employee_recognition, NA-West, false → excluded (no case study)
- C-CD4829A7: Technology, Mid-Market, retention, NA-East, true → matches industry, size_band (2 fields). Use_case and region differ.
- C-E87CED46: Technology, Mid-Market, wellness, EMEA, false → excluded (no case study)
- C-5B49A902: Technology, Mid-Market, peer_milestones, EMEA, false → excluded (no case study)
- C-307DFC3B: Technology, Mid-Market, peer_milestones, APAC, false → excluded (no case study)
- C-CEB757ABE: Technology, Mid-Market, peer_milestones, APAC, false → excluded (no case study)
- C-9D69D147: Financial Services, Mid-Market, retention, NA-West, true → matches size_band, region (2 fields). Industry and use_case differ.
- C-11C31562: Manufacturing, Mid-Market, employee_recognition, NA-West, true → matches size_band, use_case, region (3 fields). Industry differs.
- C-11209339: Manufacturing, SMB, peer_milestones, APAC, true → 0 matches of the top fields (wrong industry, size, use_case, region)

So C-11C31562 is a 3-field match (size_band, use_case, region). 

Final ranking (3 strongest with case studies):
1. C-64171065 — matched: industry, size_band, use_case (region: NA-East vs NA-West)
2. C-A13C193D — matched: industry, size_band, region (use_case: retention vs employee_recognition)
3. C-11C31562 — matched: size_band, use_case, region (industry: Manufacturing vs Technology)

Arithmetic: prospect fields = (Technology/Mid-Market/employee_recognition/NA-West). Each rank scored by counting matching fields against the prospect.

Note: Several Technology Mid-Market employee_recognition NA-West customers exist without case studies (C-D6217CAA, C-C153868F) and were excluded per instructions — they cannot be named as social proof without a case study.
channel-attribution-mathPASS 1.00

? The Question

Compute channel performance for the trailing 6 months. Paid: spend, SQMs, SQOs, cost per SQM, cost per SQO, SQM-to-SQO rate, pipeline amount, pipeline per dollar. Organic: volume, SQO rate, pipeline. Report a channel with spend and zero SQMs as undefined, not zero. Flag rows where SQO date precedes SQM date. Finish with a reallocation recommendation and your confidence given sample sizes.
cost $0.0056 · 55s · in 48,718 / out 5,339 tok
✓ undefined_channel✓ undefined_not_zero✓ flagged_rows✓ best_channel✓ reallocation

✓ Correct Answer (ground truth)

months: 2026-03, 2026-04, 2026-05, 2026-06, 2026-07, 2026-08
per_channel: {"linkedin_ads": {"spend_usd": 24000, "sqms": 25, "sqos": 8, "pipeline_amount": 96000, "cost_per_sqm": 960.0, "cost_per_sqo": 3000.0, "sqm_to_sqo_rate": 0.32, "pipeline_per_dollar": 4.0}, "organic_sea
undefined_channels: paid_social
flagged_rows_sqo_before_sqm: CT-000041, CT-000044
best_pipeline_per_dollar_channel: paid_search
reallocation_recommendation: from=paid_social; to=paid_search; rationale=paid_social has spend but zero SQMs (cost per SQM undefined, not zero); paid_search has the highest pipeline per dollar.
confidence_note: webinars and referral have small SQM samples (<20); treat rates as directional.
graded checks: undefined_channel · undefined_not_zero · flagged_rows · best_channel · reallocation

Model's actual answer

show the model's full answer
All verified. Let me confirm the paid_social count — spend $18,000 across 2026-03 through 2026-08, zero contacts in the contacts file with `paid_social` as first-touch channel. Correct, undefined.

Here is the analysis:

```
CHANNEL PERFORMANCE — TRAILING 6 MONTHS (2026-03 to 2026-08)
=========================================================================
PAID CHANNELS
  Channel            Spend     SQMs   SQOs   Cost/SQM  Cost/SQO  SQM->SQO  Pipeline    Pipe/$
  --------------------------------------------------------------------------------------------
  paid_search      $ 36,000       40     18    $900      $2,000     45.0%   $ 720,000   $20.00
  webinars          $  9,000       12      5    $750      $1,800     41.7%   $  60,000   $ 6.67
  linkedin_ads      $ 24,000       25      8    $960      $3,000     32.0%   $  96,000   $ 4.00
  paid_social       $ 18,000        0      0   undef     undef     undef   $       0   $ 0.00

ORGANIC CHANNELS
  Channel           Volume  SQOs   SQO Rate   Pipeline
  -------------------------------------------------------
  organic_search       30     10    33.3%    $  90,000
  referral             15      6    40.0%    $  48,000

DATE FLAGS — SQO date precedes SQM date:
  CT-000041 | linkedin_ads | SQM=2026-06-14 | SQO=2026-06-09  (-5 days)
  CT-000044 | linkedin_ads | SQM=2026-07-23 | SQO=2026-07-18  (-5 days)
```

Arithmetic verification (selected):
- paid_search: $36,000 / 40 SQMs = $900/SQM; $36,000 / 18 SQOs = $2,000/SQO; 18/40 = 45.0%; $720,000 / $36,000 = $20.00 pipeline per dollar. All 18 SQO contacts carry $40,000 pipeline each = $720,000. Confirmed.
- linkedin_ads: $24,000 / 25 SQMs = $960/SQM; $24,000 / 8 SQOs = $3,000/SQO; 8/25 = 32.0%; $96,000 / $24,000 = $4.00. All 8 SQO contacts carry $12,000 each = $96,000. Confirmed.
- paid_social: $18,000 spend, 0 SQMs and 0 SQOs in contacts_first_touch.csv. Cost per SQM and cost per SQO are undefined (not zero), SQM-to-SQO rate is undefined. Pipeline per dollar = $0.00 (no pipeline generated).
- webinars: $9,000 / 12 = $750/SQM; $9,000 / 5 = $1,800/SQO; 5/12 = 41.7%; $60,000 / $9,000 = $6.67. Confirmed.
- organic_search: 10/30 = 33.3%; referral: 6/15 = 40.0%. Confirmed.

---

RE ALLOCATION RECOMMENDATION

Shift budget from linkedin_ads toward paid_search. paid_search delivers 5x the pipeline per dollar ($20.00 vs $4.00), 9.4 percentage points higher SQM-to-SQO conversion (45.0% vs 32.0%), and a 45% lower cost per SQO ($2,000 vs $3,000). A $6,000 monthly rebalancing from linkedin_ads to paid_search would, at current rates, add approximately $240,000 annualized pipeline ($6,000 × 4 additional pipeline-per-dollar = $24,000/month) with no increase in total spend.

Pause paid_social entirely. The channel generated zero SQMs and zero SQOs across 6 months of $3,000/month spend ($18,000 total) — a tracking gap or execution failure that cannot be diagnosed from this dataset. Redirect those funds to paid_search.

Maintain webinars at current levels. At $750/SQM and $1,800/SQO, webinars are the most cost-efficient paid acquisition path, though sample size is small (5 SQOs).

Double down on organic_search and referral (zero spend, strong rates: 33.3% and 40.0% SQO rates). These are under-monetized — investigate whether referral volume can be scaled and whether organic_search can capture more share of voice.

CONFIDENCE: MEDIUM. paid_search and organic_search are supported by the largest samples (40 and 30 SQMs respectively). paid_social's undefined metrics need investigation (it may be a tracking or attribution issue rather than a creative failure). linkedin_ads's 2 flagged date records (SQO preceding SQM by 5 days each) introduce data-quality uncertainty. webinars at 12 SQMs is borderline for statistical significance.

One caveat: paid_social has spend and zero SQMs, which is undefined rather than a zero conversion — this needs diagnostic investigation before concluding the channel is dead versus misattributed.
competitor-battlecard-updatePASS 1.00

? The Question

Update the battlecard for the competitor. Sections: one-line positioning, pricing with source and date (newer source wins, note the conflict), where they win, where we win, objections and responses, recent changes, our 12-month win/loss record against them. Cite a snippet id for every factual claim. Rep opinion on a call is not a fact about the competitor. Anything from the old card you cannot re-source gets marked unverified.
cost $0.0053 · 350s · in 6,990 / out 20,254 tok
✓ competitor_named✓ pricing✓ win_loss✓ unverified_marked✓ citations

✓ Correct Answer (ground truth)

competitor: Rivally
pricing: value_per_user_per_month=7; source_snippet=S17; source_date=2026-08-12
win_loss_12mo: wins=13; losses=7
unverified_old_claims: Rivally lacks a Slack integration., Rivally was acquired by WorkHuman in 2025.
rep_opinion_snippet_ids_not_facts: S09, S21
citation_ids_available: 25 items — e.g. S01, S02, S03, S04
graded checks: competitor_named · pricing · win_loss · unverified_marked · citations

Model's actual answer

show the model's full answer
Updated battlecard written to /Users/amaniphipps/battlecard-rivally-updated.md.

Key decisions:

Pricing conflict resolved — three conflicting sources ($5, $6.50, $7). Per the sourcing rules, newer source wins. S17 (2026-08-12, pricing page) at $7 is the current published list price, and S18 (2026-08-14, call notes) independently confirms $7 in a deal context shortly after. The old card's $5 figure is outdated.

Two old-card claims explicitly contradicted by the new data:
- "Rivally lacks a Slack integration" — CONTRADICTED by S04 ("Slack integration worked out of the box").
- "Rivally was acquired by WorkHuman in 2025" — UNVERIFIED. No snippet supports this; only the Series C funding (S01) is on record.

Rep opinion marked as unverified and excluded: S09 (clunky UI, Bryce Harmon) and S21 (aggressive discounting, Elena Sinclair) are explicitly labeled as AE opinions and carried no product-verified evidence, so they are flagged as unverified / excluded from objections.

Win/loss record: 13 wins, 7 losses, 20 total = 65% win rate, computed directly from deals_with_competitor.csv. Seven consecutive loss deal aliases listed with their months.

Every factual claim carries a snippet ID citation. The Pulse add-on pricing conflict (S06 launch vs S23 exit beta) was resolved by treating S06 as the feature launch and S23 as the exit-beta status, both retained with their respective sources.
nurture-sequence-diagnosisPASS 1.00

? The Question

Review the sequences. Per sequence: sent, open rate, reply rate, meeting rate, weakest step. Identify tracking errors (opened above sent), audience overlap between sequences, and the failure mode for anything under 2 percent reply. One change per weak sequence and which to fix first. Under 350 words.
cost $0.0064 · 189s · in 19,969 / out 19,261 tok
✓ tracking_error✓ overlap_pair✓ weakest_step✓ fix_priority

✓ Correct Answer (ground truth)

per_sequence: {"New Logo Nurture": {"sent": 1386, "opened": 490, "replied": 90, "meetings": 27, "open_rate": 0.3535, "reply_rate": 0.0649, "meeting_rate": 0.0195, "weakest_step": 3}, "Expansion Nurture": {"sent": 8
tracking_error: sequence=Expansion Nurture; step=2; sent=300; opened=340; issue=opened exceeds sent
audience_overlap_pair: Cold Outbound - HR Leaders, Cold Outbound - People Ops
all_pairwise_overlaps: ['Cold Outbound - HR Leaders', 'Cold Outbound - People Ops'], ['Expansion Nurture', 'New Logo Nurture']
under_2pct_reply: Cold Outbound - HR Leaders
failure_mode: Cold Outbound - HR Leaders=opens healthy (~37%) but replies near zero from step 1 -> body/CTA not landing, not a deliverability or subject-line problem
fix_priority_first: Cold Outbound - HR Leaders
graded checks: tracking_error · overlap_pair · weakest_step · fix_priority

Model's actual answer

show the model's full answer
Here is the analysis.

## PER SEQUENCE

New Logo Nurture: sent=1386, open=35.4%, reply=6.5%, meet=1.9%
- Step 1: open 42.0% (210/500), reply 8.4% (42/500), meet 2.4%
- Step 2: open 34.9% (160/458), reply 6.6% (30/458), meet 2.0%
- Step 3: open 28.0% (120/428), reply 4.2% (18/428), meet 1.4% — weakest step (open declines)

Expansion Nurture: sent=875, open=64.6%, reply=6.7%, meet=1.4%
- Step 1: open 43.3% (130/300), reply 7.3% (22/300), meet 1.7%
- Step 2: open 113.3% (340/300) — tracking error, reply 8.3% (25/300), meet 1.3%
- Step 3: open 34.5% (95/275), reply 4.4% (12/275), meet 1.1%

Cold Outbound - HR Leaders: sent=1785, open=30.5%, reply=0.4%, meet=0.0%
- Step 1: open 40.0% (240/600), reply 0.8% (5/600), meet 0.0% — under 2%
- Step 2: open 29.4% (175/595), reply 0.3% (2/595), meet 0.0% — under 2%
- Step 3: open 22.0% (130/590), reply 0.2% (1/590), meet 0.0% — under 2%, weakest step

Cold Outbound - People Ops: sent=1163, open=29.2%, reply=2.5%, meet=0.5%
- Step 1: open 37.5% (150/400), reply 3.5% (14/400), meet 0.8%
- Step 2: open 28.5% (110/386), reply 2.3% (9/386), meet 0.5%
- Step 3: open 21.2% (80/377), reply 1.6% (6/377), meet 0.3% — weakest step, under 2%

## TRACKING ERRORS
Expansion Nurture Step 2: opened=340 exceeds sent=300 (113.3% open rate). Data integrity flag.

## AUDIENCE OVERLAP
12 contacts appear in more than one sequence:
- Expansion Nurture + New Logo Nurture: 7 contacts
- Cold Outbound - HR Leaders + Cold Outbound - People Ops: 3 contacts
- Cold Outbound - HR Leaders + Expansion Nurture: 1 contact
- 1 malformed key "CCT-301148" (data quality issue)

## UNDER-2% REPLY FAILURE MODE
Cold Outbound - HR Leaders (all steps): reply rates 0.8%, 0.3%, 0.2% — zero meetings produced. Failure mode: messaging irrelevance to the HR Leaders persona; declining opens (40% to 22%) signal fatigue.

## ONE CHANGE PER WEAK SEQUENCE
1. New Logo Nurture: A/B test or rewrite Step 3 subject line to stop the open-rate decline (28% vs Step 1's 42%).
2. Expansion Nurture: correct the tracking/data pipeline that let opened (340) exceed sent (300) in Step 2.
3. Cold Outbound - HR Leaders: rewrite messaging to align with HR Leaders' priorities; segment needs qualification overhaul.
4. Cold Outbound - People Ops: prune or replace Step 3 — reply dropped to 1.6% and opens fell to 21.2%.

## FIX FIRST
Cold Outbound - HR Leaders. Reply rate is 0.4% with 0 meetings across all steps — the lowest reply rate and the only sequence generating no pipeline. Highest ceiling for improvement.
weekly-marketing-goals-rollupPASS 1.00

? The Question

Produce the weekly marketing goals update for the active quarter: SQMs, SQOs, DS2s, closed-lost MIA rate, same-quarter close count, active pipeline coverage against target. For each: QTD actual, target, delta, pace (on/behind/ahead given days elapsed). One paragraph on what moved this week.
cost $0.0025 · 83s · in 6,263 / out 6,388 tok
✓ metrics_present✓ deltas✓ coverage_ratio✓ pace✓ paragraph

✓ Correct Answer (ground truth)

days_elapsed: 66
days_in_quarter: 92
elapsed_fraction: 0.7174
metrics: {"SQMs": {"qtd_actual": 230, "target": 300, "delta": -70, "expected_at_day_elapsed": 215.22, "pace": "ahead"}, "SQOs": {"qtd_actual": 84, "target": 120, "delta": -36, "expected_at_day_elapsed": 86.09,
coverage_ratio_active_pipeline: 0.75
pace_rule: expected = target * days_elapsed/days_in_quarter; within +/-5% of expected = on, above = ahead, below = behind; lower_better metrics invert.
this_week_paragraph_required: True
graded checks: metrics_present · deltas · coverage_ratio · pace · paragraph

Model's actual answer

show the model's full answer
Q3-2026 WEEKLY MARKETING GOALS UPDATE
Days elapsed: 66/92 (71.7%)

SQMs: QTD 230 | Target 300 | Delta -70 | Pace-vs-at-pace 215 -> ahead
SQOs: QTD 84 | Target 120 | Delta -36 | Pace-vs-at-pace 86 -> on
DS2s: QTD 40 | Target 75 | Delta -35 | Pace-vs-at-pace 54 -> behind
Closed-lost MIA rate: QTD 0.20 (5/25) | Target 0.10 | Delta +0.10 -> behind
Same-quarter closes: QTD 10 | Target 20 | Delta -10 | Pace-vs-at-pace 14 -> behind
Active pipeline: QTD $3,000,000 | Target $4,000,000 | Delta $-1,000,000 | Pace-vs-at-pace $2,869,565 -> ahead

This week, marketing output remains below target on SQMs (-70), SQOs (-36), DS2s (-35), and same-quarter closes (-10), though SQMs and active pipeline exceed the proportional-at-pace threshold (ahead/on). The closed-lost MIA rate rose to 0.20 (5/25), double the 0.10 target, flagging a follow-up gap in deal visibility. No new data was provided for per-week movement, so the week-over-week shift cannot be attributed to specific campaigns or channels.
communication
ceo-slack-compressionPASS 1.00

? The Question

Compress the forecast analysis below into a single Slack message for the CEO. Rules: at most three sentences; under 80 words; lead with the decision or the so-what; keep every distinct number that matters and the one caveat that changes the conclusion; cut any sentence that does not carry a claim, a number, or a directive. No em dashes, no throat-clearing phrases.
cost $0.0002 · 4s · in 669 / out 136 tok
✓ max_3_sentences✓ under_80_words✓ weighted_present✓ caveat_present✓ no_em_dash

✓ Correct Answer (ground truth)

graded checks: max_3_sentences · under_80_words · weighted_present · caveat_present · no_em_dash

Model's actual answer

show the model's full answer
Q3 forecast: COMMIT $44,729 plus 35% of $203,565 BEST_CASE = $116K, but $13,770 COMMIT (Deal-D348E1) and $28,240 BEST_CASE are excluded by the Oct-15 push-out, so the real at-risk total is $215,525. Deal owner is blank on 85 of 86 deals and all 7 COMMIT deals lack why-buy data, making the forecast unverifiable. Recommendation: hold the forecast until ownership and close-date issues are corrected.
stalled-deal-followup-emailPASS 1.00

? The Question

Write a follow-up email for this stalled deal. Under 120 words. Reference one specific prior touch from the engagement history, make one clear ask, and match the tone of the prior thread. No invented details.
cost $0.0005 · 26s · in 446 / out 1,629 tok
✓ word_cap✓ references_touch✓ single_ask

✓ Correct Answer (ground truth)

deal_alias: Deal-0D2F7A
amount: 5100
stage: DS3
owner: Alex Franklin
days_since_last_engagement: 31
word_cap: 120
must_reference: engagement_id=ENG-00002; date=2026-07-28; type=meeting; why=most substantive prior touch; concrete prospect reactions to milestone awards and points catalog
single_cta: True
tone: warm, concise, professional (matches prior thread)
allowed_facts: 14 items — e.g. Deal-0D2F7A, $5,100, DS3, Alex Franklin
allowed_new: ENG-00001, ENG-00002, ENG-00003, ENG-00004, ENG-00005
graded checks: word_cap · references_touch · single_ask

Model's actual answer

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Hi there,

Quick follow-up on the 150-seat pricing I sent August 5th, after your team's strong reaction to the automated milestone awards and points catalog during the July 28th demo. I know birthdays and anniversaries are still tracked manually today.

Would you be open to a 15-minute call this week to address any pricing questions and keep things moving?

Best,
Alex
funnel-digest-paragraph0.33

? The Question

Write the weekly GTM digest: one paragraph per team (Marketing, Sales, CS, PLG) in Bonusly brand voice. Every number must trace to the extracts. No customer contact names, no employee performance commentary, no compensation. Four paragraphs, under 400 words total.
cost $0.0005 · 19s · in 401 / out 997 tok
✗ four_paragraphs✓ word_cap✗ teams

✓ Correct Answer (ground truth)

required_paragraphs: Marketing, Sales, CS, PLG
allowed_numbers: 16 items — e.g. 2, 3, 9, 08
word_cap: 400
banned: customer contact names, employee performance commentary, compensation
graded checks: four_paragraphs · word_cap · teams

Model's actual answer

show the model's full answer
No file exists at /Users/amaniphipps/week_extracts.md — the content you posted is only in the chat, not on disk. I'll work from the embedded markdown you provided.

## Weekly GTM Digest — Week of 2026-08-31

Marketing generated 46 SQMs against a 52-SQM weekly target, with the webinar channel accounting for 18 of those Molecules. Paid search held stable at 150 dollars per SQM. Sales converted 14 SQOs into the funnel and set 9 DS2 meetings, while adding 310000 dollars in new pipeline; same-quarter close count for the week ended at 3. Customer Success secured 2 renewal saves and lifted Team NPS to 61, though 3 red-flag accounts remain open heading into next week. Product-Led Growth added 412 new signups at a 31 percent activation rate, with 38 companies reaching the aha moment of 10 recognition gives.
partner-digest-sectionPASS 1.00

? The Question

Write the partner digest for the period. Every partner in the roster appears once: active plays get status with numbers, quiet partners get one line. Partner-sourced pipeline count and amount must match the deal data. Nothing omitted, nothing added.
cost $0.0004 · 19s · in 491 / out 1,315 tok
✓ all_partners✓ pipeline_amount✓ quiet_one_line

✓ Correct Answer (ground truth)

roster: partner_name=Apex Rewards Co; status=active | partner_name=HRCloud Partners; status=active | partner_name=CultureBridge; status=active | partner_name=WorkWell Group; status=active | partner_name=Recogniq; status=quiet | partner_name=KudosWave; status=quiet … (+2 more)
per_partner_pipeline: {"Apex Rewards Co": {"count": 2, "amount": 275000}, "HRCloud Partners": {"count": 1, "amount": 140000}, "CultureBridge": {"count": 2, "amount": 135000}}
partner_sourced_pipeline: count=5; amount=550000
quiet_partners: Recogniq, KudosWave, PeopleFirst Advisors, TotalPerk
active_no_deals: WorkWell Group
rule: every roster partner named exactly once; quiet partners get one line; no partners outside roster.
graded checks: all_partners · pipeline_amount · quiet_one_line

Model's actual answer

show the model's full answer
**Partner Digest — Period 2026-08-24 to 2026-09-04**

Partner-sourced pipeline total: 5 deals, $550,000

---

**Active**

Apex Rewards Co — 2 sourced opps, $275,000 (Deal-DDAAF2 $180,000, Deal-2CF33E $95,000). Both in DS1. Co-webinar locked for 09-15.

HRCloud Partners — 1 sourced opp, $140,000 (Deal-F1CDA5). Moved to DS2 after closing security review.

CultureBridge — 2 sourced opps, $135,000 (Deal-096E1D $60,000, Deal-067213 $75,000). Both early stage from lunch-and-learn.

WorkWell Group — 0 sourced opps. No joint playbook activity this period; planning call booked for 09-09 to restart in Q4.

---

**Quiet**

Recogniq — no activity this period.

KudosWave — no activity this period.

PeopleFirst Advisors — no activity this period.

TotalPerk — no activity this period.
rfp-answer-from-policyPASS 1.00

? The Question

Answer the 10 questionnaire items from the policy excerpts only. Cite the excerpt id for every answer. Where the excerpts do not answer the question, mark it unanswerable and say what document would; do not guess. Tone: vendor response, direct.
cost $0.0003 · 12s · in 458 / out 680 tok
✓ answered_count✓ unanswerable_flagged✓ citations✓ no_guess

✓ Correct Answer (ground truth)

answered_count: 7
answer_map: {"Q1": ["P1"], "Q2": ["P1"], "Q3": ["P2"], "Q4": ["P3"], "Q5": ["P4"], "Q6": ["P5"], "Q7": ["P6"]}
unanswerable: Q8, Q9, Q10
valid_citation_ids: P1, P2, P3, P4, P5, P6, P7, P8
rule: unanswerable items must be flagged and the document that would answer them named; do not guess.
graded checks: answered_count · unanswerable_flagged · citations · no_guess

Model's actual answer

show the model's full answer
Looking at this task, I need to answer 10 questionnaire items using only the provided policy excerpts (P1-P8). Let me work through each question systematically.

Q1: Is customer data encrypted at rest? If so, how?
P1 states: "All customer data is encrypted at rest using AES-256." Answer: Yes, customer data is encrypted at rest using AES-256. [P1]

Q2: Is data encrypted in transit?
P1 states: "Data in transit is encrypted with TLS 1.2 or higher." Answer: Yes, data in transit is encrypted with TLS 1.2 or higher. [P1]

Q3: Do you hold a SOC 2 report, and can we obtain a copy?
P2 states: "Bonusly maintains a SOC 2 Type II report, renewed annually. The report is available to prospects under NDA." Answer: Yes, Bonusly holds a SOC 2 Type II report. It is available to prospects under NDA. [P2]

Q4: How long is customer data retained after contract termination?
P3 states: "Customer data is retained for the duration of the contract plus 90 days, after which it is deleted from production systems." Answer: Customer data is retained for the duration of the contract plus 90 days, after which it is deleted from production systems. [P3]

Q5: Where is your subprocessor list published and how are changes notified?
P4 states: "A current list of subprocessors is maintained at trust.bonusly.com. Customers receive 30 days notice before a new subprocessor is added." Answer: The subprocessor list is published at trust.bonusly.com. Customers receive 30 days' notice before a new subprocessor is added. [P4]

Q6: Do you support SAML single sign-on?
P5 states: "SAML 2.0 single sign-on and SCIM user provisioning are available on the Enterprise plan." Answer: Yes, SAML 2.0 single sign-on is supported on the Enterprise plan. [P5]

Q7: What is your production backup schedule and retention?
P6 states: "Production databases are backed up daily. Backups are encrypted and retained for 35 days." Answer: Production databases are backed up daily. Backups are encrypted and retained for 35 days. [P6]

Q8: Do you hold an ISO 27001 certification?
No excerpt references ISO 27001. This is unanswerable. A SOC 2 report is referenced in [P2], but no ISO 27001 certification. The trust portal (trust.bonusly.com) or the SOC 2 report referenced in P2 would be the document needed to verify.

Q9: What is your contractual uptime SLA percentage?
No excerpt mentions an uptime SLA or percentage. This is unanswerable. The customer agreement or service-level agreement document would be needed.

Q10: Will you sign a HIPAA Business Associate Agreement?
No excerpt mentions HIPAA or a Business Associate Agreement. This is unanswerable. The trust portal (trust.bonusly.com) or the legal/compliance documentation would be needed.
ops-maintenance
skill-file-reconciliationPASS 1.00

? The Question

Reconcile the skill set below against its manifest. Find and report (with severity CRITICAL/WARNING/INFO and an action type TRIM_DESC, UPDATE_BODY, DELETE_SKILL, MERGE, or REVIEW): (1) any two skills whose ALWAYS-trigger phrases overlap/duplicate, (2) any circular delegation chain between skills (name it), (3) any delegation target that does not exist (dangling, name it), (4) any version conflict and which skill should survive, (5) how many manifest descriptions exceed 1,024 characters, (6) any hardcoded page ids, dates, or person names in skill bodies, (7) manifest drift in BOTH directions (files with no manifest row, and manifest rows with no file). Make one proposal per finding; rewrite nothing.
cost $0.0651 · 625s · in 116,420 / out 56,148 tok
✓ duplicate_trigger_found✓ circular_chain✓ dangling_target✓ oversized_desc_count✓ hardcoded_values✓ manifest_drift✓ severity_and_action

✓ Correct Answer (ground truth)

graded checks: duplicate_trigger_found · circular_chain · dangling_target · oversized_desc_count · hardcoded_values · manifest_drift · severity_and_action

Model's actual answer

show the model's full answer
Here is the complete reconciliation of the 14-skill manifest against its skills, verified against both the prompt content and the files on disk at `/Users/amaniphipps/Claude/skills/`.

---

## RECONCILIATION SUMMARY — 14-Skill Manifest

### (1) Two Skills Whose ALWAYS-trigger Phrases Overlap/Duplicate

**CRITICAL — comms-drafter vs email-drafter**

These two skills cover identical communication types and share overlapping trigger phrases:

- **Overlapping communication types (6):** outbound prospecting, follow-ups, post-demo recaps, end-of-quarter pushes, QBR follow-ups, onboarding
- **Overlapping trigger phrases:** "bump email," "contract nudge," "what should I say," "write me an email," "draft a follow-up"
- **Additional overlap:** Both trigger on pasted messages with requests for feedback/rewrite
- Both are described as covering "any variation" of their core ask, so the collision zone is broad

**Action type: MERGE** — comms-drafter and email-drafter should be consolidated into a single email-and-communications drafter skill. comms-drafter covers broader external comms (Intercom, support tickets, partner outreach, rewards inquiries); email-drafter is email-only. Merge email-drafter into comms-drafter to avoid duplicate dispatch. Keep comms-drafter as the surviving skill (broader scope).

---

**WARNING — pipeline-intelligence-report vs weekly-pipeline-report**

These two skills share 4 identical ALWAYS-trigger phrases:

- "do the pipeline report"
- "update the pipeline"
- "what does pipeline look like"
- "give me this week's numbers"

Both also trigger when the user mentions "pipeline performance data," "SQM/SQO/DS2 metrics," or "bookings MTD."

**Action type: MERGE** — These are functionally redundant. pipeline-intelligence-report is the v6 full-scoring version (10-tab HTML, 8-signal scoring); weekly-pipeline-report is the older Ben-Lavin-targeted version. pipeline-intelligence-report subsumes weekly-pipeline-report's core function. Merge weekly-pipeline-report into pipeline-intelligence-report, retain the v6 spec, and remove the legacy skill.

---

### (2) Circular Delegation Chain Between Skills

**CRITICAL — deal-strategy-coach ↔ email-drafter**

- **deal-strategy-coach** body (Manager-to-prospect email frameworks section): "When drafting manager-to-prospect emails, use the `email-drafter` skill"
- **email-drafter** description: "For deal strategy, diagnosis, or coaching (not email drafting), use deal-strategy-coach instead"

Both skills delegate to each other. deal-strategy-coach sends drafting output to email-drafter; email-drafter sends strategic questions to deal-strategy-coach. No primary entry point exists — invocation could ping-pong indefinitely.

**Action type: MERGE** — Consolidate the strategic coaching and email drafting logic into a single skill. Keep deal-strategy-coach as the surviving skill (it is the diagnostic/coaching authority) and absorb email-drafter's Gmail signature retrieval and email-specific formatting rules into deal-strategy-coach's output path.

---

**INFO — comms-drafter → deal-strategy-coach (not circular)**

comms-drafter delegates strategy to deal-strategy-coach ("For deep deal strategy, use deal-strategy-coach"). This is one-directional and not circular. No action needed, but worth noting comms-drafter and email-drafter both defer to deal-strategy-coach for strategy.

---

### (3) Delegation Targets That Do Not Exist (Dangling)

**WARNING — `next-to-close`** — declared in manifest (file row: file=`next-to-close`, declared_name=`next-to-close`), but **no SKILL.md file exists on disk** at `/Users/amaniphipps/Claude/skills/next-to-close/`. The manifest row exists but the file is missing. This was confirmed by listing the skills directory.

**WARNING — `stale-pipeline-report`** — declared in manifest (file=`stale-pipeline-report`, declared_name=`stale-pipeline-report`), but **no SKILL.md file exists on disk** at `/Users/amaniphipps/Claude/skills/stale-pipeline-report/`. Confirmed missing.

**INFO — `bonusly-brand`** — referenced for delegation in 5 skill bodies (comms-drafter, email-drafter, sales-forecast, weekly-pipeline-report, signalforge-claim-compressor) and as a cascade target in analysis-validator §11. The skill exists on disk at `/Users/amaniphipps/Claude/skills/bonusly-brand/SKILL.md` but is **not in the manifest**. Not truly dangling — exists as a system skill outside the manifest.

**INFO — `prospect-research-multithreading`** — referenced in email-drafter body ("invoke prospect-research-multithreading in Contact Lookup mode"). Exists on disk at `/Users/amaniphipps/Claude/skills/prospect-research-multithreading/` but is **not in the manifest**. Not truly dangling.

**INFO — `skill-orchestrator`** — referenced in analysis-validator §11 as a cascade check target. Exists on disk but is **not in the manifest**. Not truly dangling.

**INFO — `CUSTOMER_DATA_REFERENCE`, `HUBSPOT_CONNECTOR_REFERENCE`, `SIGNALFORGE_PRODUCT_INSIGHT_SKILL`** — referenced in analysis-validator §11 as cascade check targets. These appear to be internal reference names, not skill names. They do not exist as SKILL.md directories. **INFO-level only** — they are section headers within the analysis-validator skill itself, not delegation targets.

**INFO — `this skill`** — appears in the comms-drafter, email-drafter, and signalforge-feedback descriptions as a placeholder ("this skill drafts, that skill diagnoses"). This is a textual self-reference, not a skill name. It is not a real skill in the manifest or on disk.

**Action type: DELETE_SKILL** (for next-to-close and stale-pipeline-report from the manifest) — either create the missing files or remove the manifest rows. The skill bodies were provided in the prompt, so the files should be written to disk. If the files cannot be created, remove the manifest rows.

---

### (4) Version Conflict

**WARNING — analysis-validator v3.5 vs v3.6**

The analysis-validator.SKILL.md changelog shows two versions released on the same date (May 9, 2026):

- **v3.6** — "G2-F: ID Resolution — raw IDs... must always be resolved to human-readable names"
- **v3.5** — "G1-L: Engagement Coverage Check — three-layer pull protocol"

Both share the same release date. The file header says "Version 3.6" and "Last Updated: May 9, 2026." v3.6 is the higher version number and appears last in the changelog, so it is the surviving version.

**Action type: REVIEW** — Confirm v3.6 is the active version (it is, per the file header). No conflict resolution needed beyond verifying the changelog ordering is correct (v3.6 should be the last entry, which it is).

---

### (5) Manifest Descriptions Exceeding 1,024 Characters

**0 descriptions exceed 1,024 characters.**

Verified against the actual SKILL.md files on disk:

| Skill | Actual Description Length | Over 1024? |
|---|---|---|
| pipeline-intelligence-report | 1,006 | No |
| signalforge-claim-compressor | 1,006 | No |
| partner-digest | 1,004 | No |
| comms-drafter | 996 | No |
| email-drafter | 987 | No |
| sales-forecast | 962 | No |
| deal-strategy-coach | 808 | No |
| stale-pipeline-report | 762 | No |
| analysis-validator | 656 | No |
| weekly-pipeline-report | 656 | No |
| model-selection | 676 | No |
| closed-lost-analysis | 897 | No |
| signalforge-feedback | 708 | No |

The closest are pipeline-intelligence-report and signalforge-claim-compressor, both at exactly 1,006 — well under the 1,024 limit.

---

### (6) Hardcoded Page IDs, Dates, or Person Names in Skill Bodies

**CRITICAL — Hardcoded stage IDs (150582536 through 1175632767):**

Found in 7 skill bodies:
- analysis-validator (§12.2 Deal Stage IDs table)
- closed-lost-analysis (Stage map + HubSpot properties section)
- deal-strategy-coach (Deal stages table + stage map)
- next-to-close (Stage map)
- pipeline-intelligence-report (Phase 1 stage map + data quality guards)
- stale-pipeline-report (Stage ID map in Phase 1)
- weekly-pipeline-report (not in body — only in referenced files)

All are in §12.2 reference tables labeled as "Permanent system constants" or "verify at run time." The analysis-validator explicitly labels them as "known system constants" and the pipeline-intelligence-report says "System Constants (verify at run time — do not hardcode)."

**CRITICAL — Hardcoded person names:**

- **Amani Phipps** — appears in: analysis-validator §10 (escalation: "Finance (Manish or Amani)"), pipeline-intelligence-report (signalforge producer attribution), stale-pipeline-report, next-to-close, partner-digest. Amani is the signalforge producer and a RevOps escalation target.
- **Alaina Loori** — appears in: deal-strategy-coach (VP Sales, HubSpot ID 82535637), pipeline-intelligence-report (KPI strip, AE book), weekly-pipeline-report (quality gates: "when any AE or Alaina asks"). Alaina is the VP of Sales.
- **Shealagh Coughlin** — appears in: deal-strategy-coach (VP CS, HubSpot ID 119069206).
- **Manish** — appears in: analysis-validator §10 and §12.2 (Finance escalation target).
- **Ben / Ben Lavin** — appears in: weekly-pipeline-report ("Ben Lavin · Demand Generation"), sales-forecast (old VP Sales reference, replaced by Alaina).
- **Elena** — appears in: sales-forecast (old VP Sales, explicitly noted as "replaced" by Alaina in v1.1 changelog).
- **JuliusBrussee** — appears in: signalforge-claim-compressor (changelog: "Forked and redesigned from JuliusBrussee/caveman").

**CRITICAL — Hardcoded dates:**

- **April 26, 2026** — analysis-validator (Created date)
- **May 4, 2026** — analysis-validator (CALL_SPOTLIGHT_BRIEF removal), closed-lost-analysis, deal-strategy-coach, pipeline-intelligence-report
- **May 9, 2026** — analysis-validator (Last Updated / v3.6)
- **May 2026** — analysis-validator §8 (expected ranges), closed-lost-analysis (AI field coverage), deal-strategy-coach, pipeline-intelligence-report
- **March 28, 2023** — analysis-validator G1-B (HubSpot DEALS table staleness), pipeline-intelligence-report (HubSpot DEALS table staleness)
- **May 19, 2026** — model-selection (last_checked date for model registry)
- **April 2026** — deal-strategy-coach (AE Excellence Playbook reference)
- **2026-05-16, 2026-05-17** — partner-digest (changelog)
- **May 13, 2022** — pipeline-intelligence-report (Gong data routing reference)
- **2026-05-26** — weekly-pipeline-report (references directory creation date)

**CRITICAL — Hardcoded Confluence page IDs and spreadsheet IDs:**

- **2286616609** — partner-digest (Partnerships Digest folder ID)
- **73fe98de-a4a3-4869-9f8a-bb1eeed4cf7f** — partner-digest (cloudId)
- **2232811524** — partner-digest (space ID), signalforge-feedback (space ID)
- **2234417152** — partner-digest (parent page ID), signalforge-feedback (parent page ID)
- **2295136266** — signalforge-feedback (page ID)
- **1973303** — pipeline-intelligence-report (HubSpot org ID), stale-pipeline-report (HubSpot org ID in URL), weekly-pipeline-report
- **2232582148** — sales-forecast (parent page ID)
- **2247295002** — analysis-validator §11 (Build Log page ID)
- **C0561C1JCPJ** — stale-pipeline-report (Slack channel ID for #revops-team)
- **1612893911** — sales-forecast (Confluence cloud ID), weekly-pipeline-report (Jira cloud ID)
- Spreadsheet IDs (sales-forecast references): `1CLZeOsElVDF_LF0ZG_t2nfwvhnZ6bpwqM_nX3WEYzcw` (pipeline targets), `1ENuaEcCuLjdKhMvp8FK3Ys1ek5Aw9ZuOZhsHJJFoB_k` (bookings forecast)

**CRITICAL — Hardcoded owner IDs (17 IDs in GTM roster):**

All 17 owner IDs from analysis-validator §12.3 appear in:
- analysis-validator §12.3 (full roster)
- deal-strategy-coach §12.3 (full roster)
- pipeline-intelligence-report Phase 1 (Core 6 AEs listed)
- closed-lost-analysis (not present — correctly uses live owner resolution)

IDs: 82535637 (Alaina), 119337721 (Bryce), 77260721 (Hugo), 83155923 (Dana), 84342457 (Alex), 83155924 (Cole), 1520255671 (Gavin), 77938470 (Colleen), 79580306 (Ellie), 81969994 (Ashley), 321546903 (Megan), 701163055 (Elena), 725397794 (Youssef), 1556884388 (Amanda), 210200121 (Amani), 78303262 (John), 89062643 (Yasmin).

**CRITICAL — Hardcoded numeric anchors:**

- **~452,000** / **~110,097** — analysis-validator §8 and §G1-J (provisioned user counts)
- **~452K / 110K** — pipeline-intelligence-report (G1-J anchors)
- **3,000–3,500** — analysis-validator §8 (paying customers)
- **440,000–470,000** — analysis-validator §8 (provisioned users range)
- **850–1,100** — analysis-validator §8 (Gong calls 90d)
- **150–350** — analysis-validator §8 (open pipeline deals)

**Action type: UPDATE_BODY** — The hardcoded stage IDs and owner roster are labeled as "system constants" and "verify at run time" in the skills, so they are semi-acceptable as reference documentation. However, the hardcoded person names (Alaina, Shealagh, Manish), hardcoded Confluence page IDs (2286616609, 2295136266, etc.), spreadsheet IDs, and Slack channel ID should be moved to dynamic resolution or configuration files rather than hardcoded in skill bodies. The dates (May 2026, etc.) are changelog entries and are acceptable as historical records.

---

### (7) Manifest Drift in Both Directions

**Files with no manifest row (132 skills on disk not in the manifest):**

The manifest covers only 14 custom SignalForge/Bonusly GTM skills. The Claude skills directory at `/Users/amaniphipps/Claude/skills/` contains 68 skills total, and `/Users/amaniphipps/.hermes/skills/` contains 79 system skills. Only 12 of the 14 manifest-listed skills have files on disk (next-to-close and stale-pipeline-report are missing).

This means the manifest is a curated subset, not a complete inventory. The 132 off-manifest skills include system skills (apple-notes, github-auth, etc.), library skills (docx, pdf, xlsx), and other custom skills (bonusly-brand, prospect-research-multithreading, competitor-intelligence, etc.).

**Manifest rows with no file (2 skills):**

- **next-to-close** — manifest row exists; file NOT FOUND on disk at `/Users/amaniphipps/Claude/skills/next-to-close/SKILL.md`
- **stale-pipeline-report** — manifest row exists; file NOT FOUND on disk at `/Users/amaniphipps/Claude/skills/stale-pipeline-report/SKILL.md`

Both skill bodies were provided in the prompt content, but the files have not been written to disk.

**Action type: REVIEW** — The 132 off-manifest skills are expected (the manifest is a curated subset). The 2 manifest rows with no files (next-to-close, stale-pipeline-report) require immediate action: either write the SKILL.md files to disk or remove the manifest rows. The skill bodies exist in the prompt, so writing the files is the preferred path.

---

## ACTION SUMMARY

| # | Finding | Severity | Action Type | Skills Affected |
|---|---|---|---|---|
| 1 | comms-drafter ∩ email-drafter ALWAYS-trigger overlap (6 duplicated types + 5 shared trigger phrases) | CRITICAL | MERGE | comms-drafter, email-drafter |
| 2 | pipeline-intelligence-report ∩ weekly-pipeline-report overlap (4 shared trigger phrases + 3 shared condition triggers) | WARNING | MERGE | pipeline-intelligence-report, weekly-pipeline-report |
| 3 | Circular delegation: deal-strategy-coach → email-drafter → deal-strategy-coach | CRITICAL | MERGE | deal-strategy-coach, email-drafter |
| 4 | next-to-close — manifest row exists, file missing on disk | WARNING | DELETE_SKILL (or create file) | next-to-close |
| 5 | stale-pipeline-report — manifest row exists, file missing on disk | WARNING | DELETE_SKILL (or create file) | stale-pipeline-report |
| 6 | Hardcoded person names (Amani, Alaina, Shealagh, Manish, Ben, Elena, JuliusBrussee) in skill bodies | CRITICAL | UPDATE_BODY | analysis-validator, deal-strategy-coach, pipeline-intelligence-report, sales-forecast, signalforge-claim-compressor, etc. |
| 7 | Hardcoded stage IDs, owner IDs, Confluence page IDs, spreadsheet IDs, Slack channel IDs in skill bodies | CRITICAL | UPDATE_BODY | analysis-validator, closed-lost-analysis, deal-strategy-coach, next-to-close, pipeline-intelligence-report, stale-pipeline-report, partner-digest, sales-forecast, signalforge-feedback |
| 8 | Hardcoded numeric anchors (~452,000 users, 3,000–3,500 customers, 850–1,100 calls) | WARNING | UPDATE_BODY | analysis-validator, pipeline-intelligence-report |
| 9 | Hardcoded dates (April/May 2026, March 2023, May 2026) in skill bodies | INFO | UPDATE_BODY | analysis-validator, closed-lost-analysis, deal-strategy-coach, model-selection, etc. |
| 10 | Version conflict: analysis-validator v3.5 vs v3.6 (same date) | WARNING | REVIEW | analysis-validator |
| 11 | 0 descriptions over 1,024 characters | INFO | — | (none) |
| 12 | `bonusly-brand`, `prospect-research-multithreading`, `skill-orchestrator` exist on disk but are not in manifest | INFO | REVIEW | — |
| 13 | `"this skill"` placeholder used as self-reference in 3 skill descriptions | INFO | UPDATE_BODY | comms-drafter, email-drafter, signalforge-feedback |
| 14 | 132 skill files on disk have no manifest row | INFO | REVIEW | (broad — system + library + custom skills) |
runbook-from-slack-threadPASS 1.00

? The Question

Turn the resolved incident thread into a runbook: ordered steps with the exact command or action taken, who ran it, how success was verified, and rollback for each step that changed state. Every step must trace to a message in the thread. Add nothing the thread does not contain; where the thread is unclear, mark the step as needing confirmation.
cost $0.0012 · 56s · in 3,503 / out 3,188 tok
✓ ordered_steps✓ rollback✓ needs_confirmation✓ sourced

✓ Correct Answer (ground truth)

steps: order=1; msg=M01; actor=Bryce Harmon; action=acknowledge PagerDuty alert, take IC; state_change=False; verified_by=M01 | order=2; msg=M02; actor=Farid Osman; action=check queue depth; command=bundle exec rake sidekiq:queue_depth; state_change=False; verified_by=M02 (48,213 pending) | order=3; msg=M04; actor=Farid Osman; action=pause auto-enqueue via feature flag; command=bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'; state_change=True; rollback=bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'; verified_by=M07 (queue depth falling) | order=4; msg=M05; actor=Elena Sinclair; action=cleared the dead set; state_change=True; needs_confirmation=thread gives no command or method for clearing the dead set | order=5; msg=M06; actor=Bryce Harmon; action=scale reward-worker to 6 replicas; command=kubectl scale deployment/reward-worker --replicas=6; state_change=True; rollback=kubectl scale deployment/reward-worker --replicas=3; verified_by=M07/M08 | order=6; msg=M08; actor=Cole Ingram; action=verify queue drained and error rate baseline; command=bundle exec rake sidekiq:queue_depth; state_change=False; verified_by=M08 (returns 0) … (+2 more)
ambiguous_step: order=4; msg=M05; mark=needs-confirmation
rule: no steps beyond what the thread contains; every step traces to a message id.
graded checks: ordered_steps · rollback · needs_confirmation · sourced

Model's actual answer

show the model's full answer
Runbook written to /Users/amaniphipps/incident-2026-08-29-reward-queue-runbook.md

Summary:

10 steps covering the full incident lifecycle from alert (M01) through resolution (M10). Each step traces to its message, lists who ran it, the exact command or action, how success was verified, and rollback for every state-changing step (M04, M06, M09, M10).

Three steps are marked as needing confirmation because the thread does not specify the exact command or data source:
- Step 5 (M05): no command recorded for clearing the dead set
- Step 8 (M08): Datadog baseline error-rate value not specified
- Step 9 (M09): method for counting "40 new jobs processed cleanly" not specified

Steps 2, 3, and 7 are read-only checks (queue depth, dead set inspection, drain-rate monitoring) and have no rollback.
datadog-incident-timelinePASS 1.00

? The Question

From the log slice, sequence the failure: first error with timestamp, the cascade in order, the service and job involved, and the Datadog query that would confirm the first error. State what the logs do not show.
cost $0.0011 · 64s · in 2,064 / out 4,089 tok
✓ first_error_ts✓ service_named✓ cascade_ordered✓ query✓ gaps_stated

✓ Correct Answer (ground truth)

first_error: timestamp=2026-09-03T14:01:12Z; service=reward-service; message=Redis::TimeoutError: Connection to Redis at redis-primary:6379 timed out after 5s
cascade_order: service=reward-service; first_error_at=2026-09-03T14:01:12Z | service=sidekiq; first_error_at=2026-09-03T14:01:40Z | service=api-gateway; first_error_at=2026-09-03T14:03:05Z | service=web-app; first_error_at=2026-09-03T14:03:30Z
job_class_involved: RewardGiveJob
confirming_query: service:reward-service status:error "@message:Redis::TimeoutError"
logs_do_not_show: root cause of the Redis timeout, redis-server's own logs, infrastructure metrics (CPU/memory/network), any deploy or config change preceding the incident, who applied mitigation or when (no actor fields in logs)
clean_services: postgres
graded checks: first_error_ts · service_named · cascade_ordered · query · gaps_stated

Model's actual answer

show the model's full answer
First error (timestamp): 2026-09-03T14:01:12Z — reward-service ERROR "Redis::TimeoutError: Connection to Redis at redis-primary:6379 timed out after 5s"

Cascade in order:

1. 14:01:12 — reward-service: Redis connection to redis-primary:6379 times out (root cause; first error)
2. 14:01:20 — reward-service: retry exhausted for RewardGiveJob
3. 14:01:30 — reward-service: retry exhausted for RewardGiveJob
4. 14:01:40 — reward-service: retry exhausted for RewardGiveJob
5. 14:01:40 — sidekiq: RewardGiveJob failed, Redis::TimeoutError, retrying in 60s
6. 14:02:28 — sidekiq: RewardGiveJob failed, retrying
7. 14:02:30 — sidekiq: WARN, Queue reward depth above 10,000 (backpressure building)
8. 14:03:05 — api-gateway: 502 upstream timeout calling reward-service /gives
9. 14:03:30 — web-app: Give form submission failed, upstream 502 from api-gateway (user-facing failure)
10. 14:03:31 — sidekiq: RewardGiveJob failed, retrying
11. 14:03:48 — api-gateway: 502 upstream timeout calling reward-service
12. 14:04:13 — api-gateway: 502 upstream timeout calling reward-service
13. 14:04:22 — sidekiq: RewardGiveJob failed, retrying
14. 14:04:45 — web-app: Give form submission failed, upstream 502
15. 14:05:16 — api-gateway: 502 upstream timeout calling reward-service
16. 14:05:26 — sidekiq: RewardGiveJob failed, retrying
17. 14:05:42 — web-app: Give form submission failed, upstream 502
18. 14:06:47 — sidekiq: RewardGiveJob failed, retrying
19. 14:06:49 — web-app: Give form submission failed, upstream 502
20. 14:06:52 — api-gateway: 502 upstream timeout calling reward-service

Recovery:
21. 14:22:10 — reward-service: Redis connection restored; resuming job processing
22. 14:24:45 — sidekiq: Queue reward depth below 500

Service(s) and job(s) involved:
- reward-service — Redis connection failure, retry exhaustion for RewardGiveJob
- sidekiq — RewardGiveJob failures, queue depth backlog warning
- api-gateway — 502 upstream timeouts to reward-service /gives endpoint
- web-app — Give form submission failures (user-facing)
- Jobs: RewardGiveJob (primary, in both files), RecognitionDigestJob (secondary, only in sidekiq_jobs.csv)

Datadog query to confirm the first error:
```
service:reward-service @level:ERROR "Connection to Redis at redis-primary"
```

What the logs do not show:
1. The root cause of the Redis timeout itself — no Redis server logs, no infrastructure/network logs. We cannot determine whether redis-primary was down, overloaded, undergoing failover, or experiencing a network partition. The logs only capture reward-service's client-side timeout, not Redis's server-side state.
2. RecognitionDigestJob failures appear in sidekiq_jobs.csv (J-00013 through J-00016, failed_at 14:02:36–14:05:50) but are entirely absent from datadog_logs.csv — no RecognitionDigestJob log entries at any level.
3. No alert firing, on-call notification, manual intervention, or mitigation actions (restarts, failovers, traffic shedding) during the incident window.
4. No traffic or load metrics preceding the timeout — we cannot see what load triggered the Redis saturation (e.g., a spike in enqueued jobs or request rate that preceded 14:01:12).
5. postgres checkpoint logs (14:10:56 onward) continue normally throughout the incident, indicating no database-level impact, but the logs do not show whether postgres was a contributing factor or unaffected — only that its checkpoints kept running.
feature-flag-state-summaryPASS 1.00

? The Question

Summarize the state of the listed feature flags: what each controls per the code excerpt, which companies or segments have it on, targeting rules, and any flag with no code reference or no targets. Company counts must match the export. Do not describe a flag that is not in the export.
cost $0.0003 · 26s · in 486 / out 781 tok
✓ orphans✓ no_targets✓ counts_match✓ no_invented

✓ Correct Answer (ground truth)

flags: {"recognition_streaks_v2": {"state": "on", "targeting_rules": "segment:beta_companies", "company_count": 42}, "points_budget_guardrails": {"state": "on", "targeting_rules": "all_companies", "company_c
code_referenced: recognition_streaks_v2, points_budget_guardrails, slack_dm_nudges, redeem_flow_redesign, analytics_dashboard_v3, ms_teams_app_v2
orphan_flags_no_code_reference: legacy_give_modal, survey_boosters_q3, paused_offboard_cleanup
flags_with_no_targets: paused_offboard_cleanup
rule: company counts must equal the export; do not describe flags absent from the export.
graded checks: orphans · no_targets · counts_match · no_invented

Model's actual answer

show the model's full answer
Here is the summary based strictly on the provided export and code excerpt.

---

**Feature Flag State Summary**

All 9 flags in the export are accounted for below.

---

**1. recognition_streaks_v2**  
- State: ON  
- Code reference: `app/models/recognition.rb` - if enabled, runs `StreakTracker.record(give)` when recognition (give) is recorded.  
- Target: 42 companies in segment `beta_companies`  
- Targeting rule: `segment:beta_companies`

**2. points_budget_guardrails**  
- State: ON  
- Code reference: `app/services/budget_service.rb` - if enabled, calls `BudgetService.new(company).enforce!(giver, points)` to enforce budget limits during give.  
- Target: 220 companies, applies to all companies.  
- Targeting rule: `all_companies`

**3. slack_dm_nudges**  
- State: ON  
- Code reference: `app/jobs/nudge_job.rb` - if enabled, sends a Slack DM nudge via `SlackDm.send_nudge(user)`.  
- Target: 87 companies in segment `region_na`  
- Targeting rule: `segment:region_na`

**4. redeem_flow_redesign**  
- State: OFF  
- Code reference: `app/controllers/redeem_controller.rb` - if enabled, renders the new `RedeemV2Component`; otherwise falls back to the legacy `RedeemV1Component`.  
- Target: 12 companies on a `targeted_list`  
- Targeting rule: `targeted_list`

**5. analytics_dashboard_v3**  
- State: ON  
- Code reference: `app/controllers/analytics_controller.rb` - if enabled, initializes and renders `AnalyticsV3` dashboard.  
- Target: 65 companies in segment `tier_three`  
- Targeting rule: `segment:tier_three`

**6. ms_teams_app_v2**  
- State: OFF  
- Code reference: `app/services/teams_installer.rb` - if enabled, installs the new Teams app v2 via `TeamsAppV2.install(company)`.  
- Target: 9 companies on a `targeted_list`  
- Targeting rule: `targeted_list`

**7. legacy_give_modal**  
- State: OFF  
- Code reference: None found in the code excerpt.  
- Target: 14 companies in segment `legacy_plan`  
- Targeting rule: `segment:legacy_plan`

**8. survey_boosters_q3**  
- State: ON  
- Code reference: None found in the code excerpt.  
- Target: 7 companies in segment `legacy_plan`  
- Targeting rule: `segment:legacy_plan`

**9. paused_offboard_cleanup**  
- State: OFF  
- Code reference: None found in the code excerpt.  
- Target: 0 companies (no targets)  
- Targeting rule: (none)

---

**Flags with no code reference:** `legacy_give_modal`, `survey_boosters_q3`, `paused_offboard_cleanup`  
**Flags with no targets:** `paused_offboard_cleanup` (0 companies)

**Total company count across flags:** 42 + 220 + 87 + 12 + 65 + 9 + 14 + 7 + 0 = 456  
**Flags in export:** 9 total (8 with ≥1 target, 1 with no targets)
nda-triagePASS 1.00

? The Question

Triage each NDA as GREEN (standard approval), YELLOW (counsel review), or RED (full legal review). Cite the clause that drives each classification by quoting it. Catch embedded non-solicits, non-competes, missing carve-outs, and unusual term or governing law. One line of reasoning per NDA.
cost $0.0003 · 8s · in 537 / out 458 tok
✓ green✓ yellow✓ red✓ non_solicit_caught✓ clause_quoted✓ correct_labels

✓ Correct Answer (ground truth)

classifications: {"NDA-1": {"classification": "GREEN", "driving_reason": "standard mutual NDA: 2-year term, standard carve-outs, Delaware law", "driving_clause_quote": "Carve-outs: information that (a) is or becomes p
non_solicit_in: NDA-2
checks: classification_per_nda, clause_quotes_exist_in_document, non_solicit_caught, carveouts_checked
graded checks: green · yellow · red · non_solicit_caught · clause_quoted · correct_labels

Model's actual answer

show the model's full answer
I'll triage each NDA systematically, citing the specific clause driving each classification.

## NDA-1 — [PARTY A] and Bonusly
**Classification: GREEN (standard approval)**
- Reason: No restrictive covenants, no exclusivity. Carve-outs are standard and complete: "information that (a) is or becomes publicly available through no breach, (b) was known prior to disclosure, (c) is received from a third party without duty of confidence, (d) is independently developed, or (e) must be disclosed by law or court order." Governing law is Delaware (standard).

## NDA-2 — [PARTY B] and Bonusly
**Classification: YELLOW (counsel review)**
- Reason: Embedded non-solicitation buried in an NDA: "During the term of this Agreement and for eighteen (18) months thereafter, neither party shall solicit for employment or hire any employee of the other party with whom it came into contact under this Agreement." This is broader than typical (covers any employee "with whom it came into contact"), and the term survives 18 months post-termination. Counsel should verify enforceability and scope narrowing.

## NDA-3 — [PARTY C] and Bonusly
**Classification: RED (full legal review)**
- Reason: Multiple severe issues: (1) Non-compete is overly broad and one-sided: "For a period of three (3) years following the Effective Date, Recipient shall not, directly or indirectly, engage in or provide services to any business that competes with Discloser's business" — this is a general business non-compete with no geographic or scope limitation. (2) Missing confidentiality carve-outs entirely: "No confidentiality carve-outs are stated in this Agreement." (3) One-way confidentiality in favor of [PARTY C] while imposing a broad non-compete on the recipient — unbalanced. (4) Governing law and jurisdiction: "the laws of the Republic of Ireland" with "exclusive jurisdiction of its courts" — unusual for a U.S. company (Bonusly) in a B2B NDA. Full legal review required.