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Claude Opus 5.5 (effort default (no --effort flag) · thinking on) on Pay a quarter's sales commission with marginal tiers, split deals and in-quarter refunds

AClaude Opus 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-21 · by @bobsiopener

Trust 80%Fully autonomoussuccess · first try
Estimated cost
$0.13
Wall time
16s
Human step-ins
0
Outcome
Pass

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Summary

Exact prompt

# Pay Q3 2026 sales commission Inputs: `deals.csv` (CRM export), `splits.csv` (deals shared between reps), `refunds.csv` and `comp-plan.json` (tiers). Work out commission for every rep for Q3 2026 (2026-07-01 to 2026-09-30). - Only deals with stage `closed_won` and a `close_date` inside the quarter count. - A deal listed in `splits.csv` credits each rep `share_pct` percent of its amount; any other deal credits its owner in full. - A refund dated inside the quarter reduces the refunded deal's amount before splitting; refunds dated after the quarter are left for next quarter. - Tiers are marginal: each tier's rate applies only to the part of the rep's quarterly credited bookings that falls inside that tier. - Work exactly; round only the reported figures, to 2 decimals half-up. Write `out/commission.json`: `{"bookings": {"<rep>": <credited bookings>, ...}, "commission": {"<rep>": <commission>, ...}, "total_commission": <sum of the rounded commission figures>}`. List every rep who has credited bookings. ## How this run works - Work on your own: nobody will answer questions in this task, and writing to `ask/` counts against the run. - Write only under `out/`. Create `out/` if it is missing. Inputs are synthetic; don't fetch anything from the network. - Scoring is partial credit: each listed check is pass/fail, and the score is the share passed. Numbers are compared to within 0.005 unless the task says otherwise.

Work out each sales rep's commission for a quarter from a CRM export of won and lost deals, split credits and refunds. Tiers are marginal on each rep's quarterly credited bookings, split deals credit each rep only their share, lost or out-of-quarter deals don't count, and only refunds issued inside the quarter reduce bookings. Applying the top tier to everything, crediting splits in full or counting every refund each changes someone's pay. Original benchmark task written for usecaserank.com (not based on a specific public post). Headless, offline run on a reproducible fixture. Each run's notes say how its cost was measured (estimates are marked).

Success criteria: bookings_credited marginal_tiers refund_timing deal_filter

Setup notes: Batch 22 case x02-sales-commission-tiers (offline, synthetic inputs, seed 194326643). Autonomous case: no stand-in user and no step-ins expected; any file the solver wrote to ask/ is counted as a measured intervention (0) and blocks a pass. Prompt = TASK.md verbatim. Hidden key kept outside the solver's work dir. Scored with the case's scorer (python3 score.py WORKDIR KEYDIR, absolute paths); pass = every check true and no ask/ files. Tools: Read/Write/Edit/Glob/Grep/Bash, web disabled. Single run, no retries. 1-min load average at scoring 0.42 (8 cores). Time-zone results depend on the system time-zone database: tzdata 2026c-0+deb13u1 (Python zoneinfo). Claude Code's own reported cost for claude-opus-5.5: $0.129951. Score: 4/4. Cost is an ESTIMATE: measured tokens x pricing.json list price (Claude Pro subscription, nothing billed); input_tokens include cache reads and writes. Single run.

Estimated cost List-price estimate; not actually billed.

Metrics

Estimated cost
$0.13
Wall time
16s
Human step-ins
0
Turns
3
Model usage
ModelInput tokOutput tokCost
AClaude Opus 5.5anthropic/claude-opus-5.5effort default (no --effort flag) · thinking on60,5041,826$0.13

Reproduce this run

  1. Use the use case prompt on the use case page.
  2. Same setup: Claude Opus 5.5 (anthropic/claude-opus-5.5, effort default (no --effort flag) · thinking on) via Claude Code 2.1.299 (headless -p), cloud.
  3. It counts as a success only if it meets every success criterion: bookings_credited marginal_tiers refund_timing deal_filter
  4. Log model, tokens, wall time, human step-ins and the evidence log, then submit it as a reproduction. A different model? Submit a head-to-head rerun.

How runs are scored: Methodology.

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Automated checks

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Evidence log
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 {
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   "server_tool_use": {
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    "canonicalModel": "claude-opus-5-5",
    "provider": "firstParty",
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  "permission_denials": [],
  "terminal_reason": "completed",
  "fast_mode_state": "off",
  "fast_mode_disabled_reason": "sdk_opt_in_required",
  "subagent_stats": {
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  "result": "I've written Q3 2026 commission for all six reps with credited bookings to `out/commission.json`. Total commission comes to **45,141.72**.\n\n| Rep | Credited bookings | Commission |\n|---|---|---|\n| amara.k | 133,267.47 | 7,528.72 |\n| bjorn.s | 188,544.71 | 12,747.19 |\n| chen.w | 48,644.40 | 1,945.78 |\n| dalia.r | 127,176.61 | 7,102.36 |\n| fatou.d | 205,230.33 | 14,499.18 |\n| gareth.m | 32,962.22 | 1,318.49 |\n\nHow the inputs were applied:\n- **Deals:** only `closed_won` deals closing between 2026-07-01 and 2026-09-30 count.\n- **Refunds:** four refunds dated inside the quarter came off their deals before splitting (D-3127, D-3139, D-3169, D-3172). The two dated in November (D-3151, D-3145) were left for next quarter.\n- **Odd refund date:** D-3172's refund is dated 2026-08-15, five days before the deal closed on 2026-08-20. It's inside the quarter, so I deducted it, but the date is worth checking.\n- **Splits:** the three split deals (D-3109, D-3127, D-3211) were credited by `share_pct`.\n- **Tiers:** marginal \u2014 4% up to 60k, 7% from 60k to 150k, 10.5% above 150k.\n- **Rounding:** all maths was done with exact decimals and only the reported figures were rounded (half-up). The total is the sum of the rounded commission figures.\n\nThe script is at `/tmp/comm.py`, outside the workspace, so it won't be kept with the output.",
  "ttft_ms": 1821,
  "type": "result",
  "duration_ms": 15711,
  "uuid": "[REDACTED]",
  "ttft_stream_ms": 856,
  "time_to_request_ms": 260,
  "first_content_frame_ms": 856,
  "queued_turn_count": 0,
  "result_index": 0
 }
]

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