Claude Opus 5.5 (effort default (no --effort flag) · thinking on) on Monthly cohort retention + revenue from a messy orders CSV

AClaude Opus 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-16 · by @bobsiopener

Trust 80%Fully autonomoussuccess · first tryCompare

Estimated cost: $0.11
Wall time: 16s
Human step-ins: 0
Outcome: Pass

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Exact prompt

# Task: monthly cohort retention from a messy orders export `orders.csv` (in your working directory) is an e-commerce orders export for Jan–Jun 2026. Known data issues: some dates are DD/MM/YYYY instead of YYYY-MM-DD; some user_ids have stray whitespace/lowercase (U00012 == " u00012 "); some rows are exact duplicates; only status=="paid" counts. Produce `answer.json`: ```json {"cohorts": {"2026-01": {"size": <int>, "retention": [1.0, m1, m2, ...]}, ...}, "paid_revenue_by_month": {"2026-01": <float>, ... "2026-06": <float>}} ``` - Cohort = month of a user's first paid order. size = number of users in it. - retention[k] = share of cohort users with ≥1 paid order in cohort month + k (k=0..through 2026-06), rounded to 4 decimals. - Revenue = sum of amount_usd of paid orders (deduplicated) per calendar month, 2 decimals. Do not use the network. You may write and run code.

Compute monthly cohort retention and paid revenue from a messy six-month orders export. The file mixes two date formats, has user ids with stray whitespace and case differences, contains exact duplicate rows, and includes unpaid orders. Every cohort size, retention rate and monthly revenue figure is checked against a key. 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: 1. answer.json must list every cohort month present in the key; each cohort size must match exactly. 2. Each cohort's retention list must have the exact expected length and every value within ±0.002 of the key. 3. Paid revenue for each month Jan–Jun must be within ±$0.05 of the key. 4. Pass only if every check above passes (all cohorts and all 6 months).

Setup notes: Use Case Scout batch 3 case c03-cohort-retention (offline fixture). Prepared with batch3/prepare.sh; hidden key/tests kept outside the agent's work dir. Prompt = TASK.md verbatim. Scored with the scout's scorer: `python score.py WORKDIR/answer.json KEYDIR/key.json`. Tools: Read/Write/Edit/Glob/Grep/Bash, web disabled. Single run, no retries. Claude Code's own reported cost for claude-opus-5.5: $0.105392. Score: 18/18. 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: $0.11
Wall time: 16s
Human step-ins: 0
Turns: 3

Model usage
Model	Input tok	Output tok	Cost
AClaude Opus 5.5anthropic/claude-opus-5.5effort default (no --effort flag) · thinking on	55,566	1,812	$0.11

Reproduce this run

Use the use case prompt on the use case page.
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.
It counts as a success only if it meets every success criterion: 1. answer.json must list every cohort month present in the key; each cohort size must match exactly. 2. Each cohort's retention list must have the exact expected length and every value within ±0.002 of the key. 3. Paid revenue for each month Jan–Jun must be within ±$0.05 of the key. 4. Pass only if every check above passes (all cohorts and all 6 months).
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

Trust 80% comes from these checks (how Trust % works).

Run date is not in the future : passed
Model is in the dated pricing table : passed
Cost recomputed from tokens matches : passed
Cached tokens are consistent : passed
Output throughput is plausible : passed
Autonomy matches step-ins : passed
Step-ins do not exceed turns : passed
Retries agree with first-try result : passed
Evidence log SHA-256 matches : passed
Evidence log is substantial : passed

Evidence log

[
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  "stop_reason": "end_turn",
  "session_id": "[REDACTED]",
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   "server_tool_use": {
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   "service_tier": "standard",
   "cache_creation": {
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     "cache_creation": {
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     },
     "type": "message"
    }
   ],
   "speed": "standard",
   "fallback_credit": null
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  "modelUsage": {
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    "outputTokens": 1812,
    "cacheReadInputTokens": 48122,
    "cacheCreationInputTokens": 7438,
    "webSearchRequests": 0,
    "costUSD": 0.1053924,
    "contextWindow": 1000000,
    "maxOutputTokens": 128000,
    "thinkingTokens": 96,
    "canonicalModel": "claude-opus-5-5",
    "provider": "firstParty",
    "costBasis": "list"
   }
  },
  "permission_denials": [],
  "terminal_reason": "completed",
  "fast_mode_state": "off",
  "fast_mode_disabled_reason": "sdk_opt_in_required",
  "subagent_stats": {
   "spawned": 0,
   "requested": {
    "background": 0,
    "foreground": 0,
    "unset": 0
   },
   "started_in_background": 0,
   "max_depth": 0,
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   "killed": {
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   "refused": {
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   "by_type": {}
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  "safety_stops": 0,
  "is_error": false,
  "num_turns": 3,
  "subtype": "success",
  "api_error_status": null,
  "result": "I've written `answer.json`. After cleaning, 2,450 paid orders from 1,171 distinct users remain.\n\n**How I cleaned the data**\n- **Duplicates:** I dropped 42 exact duplicate rows. That matches the 42 repeated order IDs, so no order was double-counted and no two different orders shared an ID.\n- **Dates:** 133 dates were in DD/MM/YYYY format. Every one parsed as a valid month, so I converted them all.\n- **User IDs:** I trimmed spaces and uppercased them, so `\" u00012 \"` counts as `U00012`.\n- **Status:** I kept only `paid` and excluded the refunded (126) and test (82) rows.\n\n**Cohorts**\n\n| Cohort | Size | Retention (k = 0, 1, 2, \u2026) |\n|---|---|---|\n| 2026-01 | 148 | 1.0, 0.3311, 0.2703, 0.1486, 0.1284, 0.0405 |\n| 2026-02 | 216 | 1.0, 0.3889, 0.2731, 0.1157, 0.0648 |\n| 2026-03 | 228 | 1.0, 0.4123, 0.2544, 0.1228 |\n| 2026-04 | 217 | 1.0, 0.3733, 0.2212 |\n| 2026-05 | 246 | 1.0, 0.3415 |\n| 2026-06 | 116 | 1.0 |\n\n**Paid revenue by month (USD)**\n\n| Month | Revenue |\n|---|---|\n| 2026-01 | 16,862.20 |\n| 2026-02 | 29,862.41 |\n| 2026-03 | 43,820.16 |\n| 2026-04 | 47,451.77 |\n| 2026-05 | 51,556.03 |\n| 2026-06 | 36,742.33 |\n\nThe script that produced this is at `/tmp/a.py`, outside the working directory.",
  "ttft_ms": 2219,
  "type": "result",
  "duration_ms": 15508,
  "uuid": "[REDACTED]",
  "ttft_stream_ms": 891,
  "time_to_request_ms": 242,
  "first_content_frame_ms": 891,
  "queued_turn_count": 0,
  "result_index": 0
 }
]