Claude Opus 5.5 (effort default (no --effort flag) · thinking on) on Find your own listings on people-search pages and draft opt-out requests for approval

AClaude Opus 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-20 · by @bobsiopener

Trust 80%Fully autonomoussuccess · first tryCompare

Estimated cost: $0.36
Wall time: 1m
Human step-ins: 0
Outcome: Pass

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Exact prompt

# Find your own listings on (synthetic) people-search sites and draft opt-out requests Everything here is synthetic and offline: `person.json` describes one made-up person (the "user"); `broker_1.html` .. `broker_6.html` are saved result pages from six made-up people-search sites, each with its own layout. Write `solve.py` (Python 3 standard library only). `python solve.py input.json answer.json` (first argument unused; read the files from the current directory) writes `answer.json` and one draft file per site that lists the user. Fresh people and pages are generated when scoring. Nothing is sent or submitted: you only write files. ## Reading the pages 1. A record's ID is in `data-record-id` (table rows), `Record #<ID>` (cards) or `id="rec-<ID>"` (definition lists). A record has a primary name, optional aliases (`AKA:`, `Also known as:`, `Alias`), an optional age field, addresses, phones and emails. 2. Each page's footer gives the removal address (the `privacy@` mailto link); the other mailto is not for removals. ## Matching rules (a record is the user's when 3, 4 and 5 hold) 3. Name: the primary name **or any alias** matches. Normalise: lower-case, drop `.` and `'`, hyphens become spaces. `Last, First Middle` (has a comma) is reordered to First Middle Last. Drop single-letter tokens (initials) and any token equal to the user's middle name. The first token must equal the legal first name or one of its `nicknames`; the remaining tokens joined by spaces must equal the normalised legal last name or a `former_last_names` entry. 4. At least one corroborating detail: (a) age: `Age N`, `Age: N` or `N yrs` with |N - true age on as_of| <= 1, or `Born YYYY` with |YYYY - birth year| <= 1; (b) an address equal to one of the user's (rule 6); (c) a phone whose digits equal one of the user's (drop a leading country code 1); (d) an email equal to one of the user's after lower-casing (for gmail.com also ignore dots and anything from `+` in the local part). 5. No contradiction: if the record shows an age or birth year that fails rule 4(a), it is not the user (a relative at the same address, for example). 6. Address equality: take the text before the first comma as the street line; remove unit parts (`#X`, or `apt`/`apartment`/`unit`/`suite`/`ste` plus the next token); lower-case, drop `.`, map words with `street_abbreviations`; the token lists must be equal and the first five digits of the ZIP (the last 5-digit group, ZIP+4 allowed) must match. City and unit are ignored. Build the user's own addresses from `number`, `dir`, `name`, `type`, `zip` the same way. ## Draft files (only for sites with at least one matching record) `optout_broker_N.txt`: header lines, a blank line, then the body. Headers: `To: <the site's privacy@ address>`, `Subject: ...`, `X-Draft-Status: not sent`. The body names the user by legal first and last name (e.g. `Margaret Okafor-Reyes`) and lists every matching record ID for that site. Data minimisation: never include the date of birth (in any common format) or `ssn_last4`, and include a phone or email only if that site already shows it in a matched record. Do not write a file for a site with no match. ## answer.json `{"matches": {"broker_1": [record IDs], ... all six keys}, "submitted": false}`. solve.py must not use any network or sending capability (no smtplib, socket, urllib, http.client, requests, httpx, aiohttp, webbrowser or subprocess). ## Scoring Hidden checks (partial credit; all must pass): `no_false_positive_records`, `no_missed_records`, `draft_files_headers_ids`, `draft_privacy_minimization`, `offline_nothing_submitted` on the given pages; `fresh_run_1`, `fresh_run_2` (matches and drafts on new pages). 30-second limit per run.

Name-only matching finds plenty of records and most of them are wrong; the stated corroboration and contradiction rules separate careful solvers. 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. no_false_positive_records 2. no_missed_records 3. draft_files_headers_ids 4. draft_privacy_minimization 5. offline_nothing_submitted 6. fresh_run_1 7. fresh_run_2

Setup notes: Re-scored on Oct 20, 2026 after a scorer fix (originally 4/7); measured data unchanged. Use Case Scout batch 13 case n03-broker-optout-drafts (offline, synthetic fixture). Prepared with batch13/prepare.sh; hidden key/checker kept outside the agent's work dir (temp dir during the run). Prompt = TASK.md verbatim. Scored with the case's scorer: `python score.py WORKDIR KEYDIR` (python3, run from the case dir). Tools: Read/Write/Edit/Glob/Grep/Bash, web disabled. Single run, no retries. 1-min load average at scoring 1.46 (8 cores). Claude Code's own reported cost for claude-opus-5.5: $0.358745. Score: 7/7. 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: $0.36
Wall time: 1m
Human step-ins: 0
Turns: 4

Model usage
Model	Input tok	Output tok	Cost
AClaude Opus 5.5anthropic/claude-opus-5.5effort default (no --effort flag) · thinking on	110,482	8,559	$0.36

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. no_false_positive_records 2. no_missed_records 3. draft_files_headers_ids 4. draft_privacy_minimization 5. offline_nothing_submitted 6. fresh_run_1 7. fresh_run_2
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

[
 {
  "duration_api_ms": 77500,
  "stop_reason": "end_turn",
  "session_id": "[REDACTED]",
  "total_cost_usd": 0.35874480000000003,
  "usage": {
   "input_tokens": 8,
   "cache_creation_input_tokens": 21210,
   "cache_read_input_tokens": 89264,
   "output_tokens": 8559,
   "output_tokens_details": {
    "thinking_tokens": 2726
   },
   "server_tool_use": {
    "web_search_requests": 0,
    "web_fetch_requests": 0
   },
   "service_tier": "standard",
   "cache_creation": {
    "ephemeral_1h_input_tokens": 21210,
    "ephemeral_5m_input_tokens": 0
   },
   "inference_geo": "global",
   "iterations": [
    {
     "input_tokens": 2,
     "output_tokens": 1400,
     "cache_read_input_tokens": 32648,
     "cache_creation_input_tokens": 708,
     "cache_creation": {
      "ephemeral_5m_input_tokens": 0,
      "ephemeral_1h_input_tokens": 708
     },
     "type": "message"
    }
   ],
   "speed": "standard",
   "fallback_credit": null
  },
  "modelUsage": {
   "claude-opus-5-5": {
    "inputTokens": 8,
    "outputTokens": 8559,
    "cacheReadInputTokens": 89264,
    "cacheCreationInputTokens": 21210,
    "webSearchRequests": 0,
    "costUSD": 0.35874480000000003,
    "contextWindow": 1000000,
    "maxOutputTokens": 128000,
    "thinkingTokens": 2726,
    "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,
   "spawned_by_subagents": 0,
   "completed": 0,
   "failed": 0,
   "killed": {
    "parent": 0,
    "user": 0,
    "system": 0
   },
   "refused": {
    "depth_limit": 0,
    "concurrency_limit": 0,
    "budget": 0
   },
   "by_type": {}
  },
  "safety_stops": 0,
  "is_error": false,
  "num_turns": 4,
  "subtype": "success",
  "api_error_status": null,
  "result": "I wrote `solve.py` and ran it on the six pages. Running `python solve.py input.json answer.json` writes `answer.json` and one draft for each site that lists the user. All six sites list her, so it wrote six drafts. I checked every record on all six pages by hand against the rules, and the script's matches agree with mine. I haven't tested it on freshly generated pages, which is what the `fresh_run` checks use.\n\n| Site | Matched records | Records rejected for a reason worth noting |\n|---|---|---|\n| broker_1 | 1X73225, 1Q83866, 1K61851 | 1Q10409 has the right name but nothing to back it up |\n| broker_2 | 2X38034, 2Q33906 (matched on its alias), 2Q13340, 2K80687 | 2K17787 (Age 54), 2Z84321 and 2Z48539 (Born 1984) |\n| broker_3 | 3K22349 | 3Z17756 (54 yrs), 3Z19266 (nothing to back it up) |\n| broker_4 | 4Z85881 (alias), 4X89558, 4Z38181 (alias) | 4X77804 (Age 32) |\n| broker_5 | 5Z51572, 5X28678 | 5Q44609 (Age 87), 5Q60343 and 5X62063 (Born 1984) |\n| broker_6 | 6Z45279 (alias) | 6Z28521 (Age 54) |\n\nThe rejected records with a wrong age or birth year are probably relatives at the same address, which is what rule 5 is for. The \"Maro\", \"Martha\" and \"Vanson\" records fail the name rule even when the phone or email is hers.\n\n- **Reading the pages:** the script finds the layout from the page content, not from the broker number, so table, card and definition-list pages work whichever site they appear on. The `To:` address comes from the `privacy@` mailto link; the `sales@` link is never used.\n- **Drafts:** each one has the `To:`, `Subject:` and `X-Draft-Status: not sent` headers, then a body that names \"Margaret Van Der Berg\" and lists that site's matched record IDs. I left out her phone numbers, emails, addresses, age, date of birth and SSN digits entirely, which meets the minimisation rule. Old drafts for sites with no match are deleted on each run.\n- **Offline:** the script only reads and writes local files, uses no network or subprocess modules, and `answer.json` has `\"submitted\": false`.",
  "ttft_ms": 2722,
  "type": "result",
  "duration_ms": 77891,
  "uuid": "[REDACTED]",
  "ttft_stream_ms": 2039,
  "time_to_request_ms": 247,
  "first_content_frame_ms": 2039,
  "queued_turn_count": 0,
  "result_index": 0
 }
]