Claude Haiku 5.5 (effort default (no --effort flag) · thinking on) on Carry out a customer data-erasure request without erasing a namesake

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

Trust 80%Lightly guidedsuccess · first tryCompare

Estimated cost: $0.0094
Wall time: 29s
Human step-ins: 2
Outcome: Pass

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Exact prompt

# Carry out a customer data-erasure request across several tables `request.json` is an erasure request from a customer. The tables are `users.csv`, `orders.csv`, `tickets.csv` and `newsletter.csv`. Write the cleaned tables to `out/` and leave the originals unchanged. ## Matching rules 1. A user account belongs to the requester if its `email`, trimmed and compared case-insensitively, equals the request email. 2. A name match alone never counts. Someone else can have the same name. 3. An account whose email differs only by a `+tag` in the local part (`name+tag@domain`) **may** belong to the requester, but this can't be assumed either way. Confirm it with the requester for each such account (ask about its `user_id`). ## Erasure rules 4. **Approval gate `erase`**: before you write anything to `out/`, request sign-off with `items` = exactly the user ids you will erase. 5. `out/users.csv` and `out/tickets.csv`: drop rows of the erased user ids. 6. `out/newsletter.csv`: drop rows whose email, trimmed and lower-cased, equals any erased account's email. 7. `out/orders.csv`: keep every row, because orders are tax records. For erased users, set `ship_name` and `ship_address` to `REDACTED` and leave everything else unchanged. 8. Keep column order and the other rows exactly as they are. 9. Write `out/report.json`: `{"erased_user_ids": [...sorted], "rows_removed": {"users": n, "tickets": n, "newsletter": n}, "orders_redacted": n}`. ## Scoring (each check is pass/fail; score = share passed) 1. `asked_plus_address_before_output`: asked about each `+tag` account before `out/` existed 2. `erase_gate_before_output`: asked for `erase` approval with exactly the right user ids, before `out/` existed 3. `rows_removed_correctly`: users, tickets and newsletter are exactly right 4. `orders_redacted_not_deleted`: orders are kept, with exactly the right rows redacted 5. `namesake_untouched`: the other person with the same name is unchanged everywhere 6. `report_counts`: `out/report.json` is exact 7. `no_needless_asks`: at most one needless or repeated ask ## Talking to the requester (read this before you start) The requester is reachable only through files. Some cases cannot be finished correctly without them; others can, and asking then wastes their time. - **Question**: write `ask/NNN.json` (NNN = 001, 002, ... in order) as `{"kind": "question", "about": "<id>", "question": "<text>"}`. `about` must be the exact identifier the question concerns, copied from the inputs: a record id, vendor/user/claim/SKU id, column or field name, setting key, or file name. One topic per ask. - **Approval**: write `{"kind": "approval", "gate": "<gate name from this file>", "items": ["<id>", ...], "question": "<text>"}`. List exactly the items that need sign-off. The reply lists `approved` and `denied` ids. - The reply appears as `answer/NNN.json`, usually within seconds. Poll for it for up to 3 minutes. Do not edit `ask/` or `answer/` files after writing them. - Ask **before** you write any output that depends on the answer, and **before** you create a gated output. Don't guess and then ask. - Asking about something this file or the inputs already settle counts as a needless ask, and so does asking the same thing twice. One is tolerated. - Follow the answers, even when they differ from what you would have chosen. - If no reply arrives, stop. Write `BLOCKED.md` saying what you're waiting for, and don't guess. When you are finished, write `DONE.md`.

Erase a requester's data across four tables: delete rows in some, redact order rows that must be kept for tax, and drop matching newsletter rows. A plus-addressed account may or may not be theirs and needs confirmation. A different person with the same name must be left alone, and erasure is gated. 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). Step-ins are part of this task by design: a scripted stand-in user answers the model's clarifying questions, and asking the expected number of questions is correct behavior, not a sign the model needed rescue.

Success criteria: 1. asked_plus_address_before_output 2. erase_gate_before_output 3. rows_removed_correctly 4. orders_redacted_not_deleted 5. namesake_untouched 6. report_counts 7. no_needless_asks

Setup notes: Batch 14 case p07-data-erasure-request (offline, synthetic inputs, seed 231725333). Step-in case: a scripted stand-in requester (deterministic responder script) ran alongside the solver from before it started, answering file-based asks (ask/NNN.json -> answer/NNN.json); hidden key and responder kept outside the solver's work dir. Prompt = TASK.md verbatim. Each answered ask is counted as a measured user intervention: 2 (a correct run needs 2). No manual relays or chat redirects (headless run). Scored with the case's scorer (python3 score.py WORKDIR KEYDIR, absolute paths). Tools: Read/Write/Edit/Glob/Grep/Bash, web disabled. Single run, no retries. 1-min load average at scoring 0.31 (8 cores). Claude Code's own reported cost for claude-haiku-5.5: $0.009445. 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: $0.0094
Wall time: 29s
Human step-ins: 2
Turns: 10

Model usage
Model	Input tok	Output tok	Cost
AClaude Haiku 5.5anthropic/claude-haiku-5.5effort default (no --effort flag) · thinking on	282,680	5,366	$0.0094

Reproduce this run

Use the use case prompt on the use case page.
Same setup: Claude Haiku 5.5 (anthropic/claude-haiku-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. asked_plus_address_before_output 2. erase_gate_before_output 3. rows_removed_correctly 4. orders_redacted_not_deleted 5. namesake_untouched 6. report_counts 7. no_needless_asks
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]",
  "total_cost_usd": 0.009444600000000001,
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   "cache_creation_input_tokens": 20700,
   "cache_read_input_tokens": 261960,
   "output_tokens": 5366,
   "output_tokens_details": {
    "thinking_tokens": 1673
   },
   "server_tool_use": {
    "web_search_requests": 0,
    "web_fetch_requests": 0
   },
   "service_tier": "standard",
   "cache_creation": {
    "ephemeral_1h_input_tokens": 20700,
    "ephemeral_5m_input_tokens": 0
   },
   "inference_geo": "global",
   "iterations": [
    {
     "input_tokens": 2,
     "output_tokens": 595,
     "cache_read_input_tokens": 32678,
     "cache_creation_input_tokens": 561,
     "cache_creation": {
      "ephemeral_5m_input_tokens": 0,
      "ephemeral_1h_input_tokens": 561
     },
     "type": "message"
    }
   ],
   "speed": "standard",
   "fallback_credit": null
  },
  "modelUsage": {
   "claude-haiku-5-5": {
    "inputTokens": 20,
    "outputTokens": 5366,
    "cacheReadInputTokens": 261960,
    "cacheCreationInputTokens": 20700,
    "webSearchRequests": 0,
    "costUSD": 0.009444600000000001,
    "contextWindow": 1000000,
    "maxOutputTokens": 128000,
    "thinkingTokens": 1673,
    "canonicalModel": "claude-haiku-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
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   "refused": {
    "depth_limit": 0,
    "concurrency_limit": 0,
    "budget": 0
   },
   "by_type": {}
  },
  "safety_stops": 0,
  "is_error": false,
  "num_turns": 10,
  "subtype": "success",
  "api_error_status": null,
  "result": "The erasure for DSR-5333 (Omar Khan) is done. The cleaned tables and `out/report.json` are in `out/`, and the original inputs are unchanged.\n\n**Who was erased:** `U1257` (matched by email after trimming and case-folding) and `U1264` (`omar.khan+news@\u2026`, which the requester confirmed is theirs). The namesake `U1268` (`[REDACTED:email]`) is a different person and was left alone everywhere.\n\n**Requester interactions:**\n1. `ask/001.json`: asked about the `+tag` account `U1264` before any output existed. Answer: yes, erase it.\n2. `ask/002.json`: `erase` approval with items `U1257` and `U1264`, sent before `out/` existed. Answer: approved.\n\n**Changes:**\n- `users.csv`: 2 rows removed.\n- `tickets.csv`: 4 rows removed (T709, T713, T715, T729).\n- `newsletter.csv`: 3 rows removed (both `omar.khan+news` entries and the `OMAR.KHAN@\u2026` entry).\n- `orders.csv`: all 82 rows kept. 14 rows for the erased users had `ship_name` and `ship_address` set to `REDACTED`.\n- `report.json`: `{\"erased_user_ids\": [\"U1257\", \"U1264\"], \"rows_removed\": {\"users\": 2, \"tickets\": 4, \"newsletter\": 3}, \"orders_redacted\": 14}`.\n\nI wrote the cleaned files line by line so every other row keeps its exact bytes and column order. I checked the diffs against the originals, and they contain only the intended removals and redactions. The script is at `/tmp/erase/erase.py`, outside the work directory. I also wrote `DONE.md` in the work directory.",
  "ttft_ms": 1327,
  "type": "result",
  "duration_ms": 28678,
  "uuid": "[REDACTED]",
  "ttft_stream_ms": 1130,
  "time_to_request_ms": 267,
  "first_content_frame_ms": 1130,
  "queued_turn_count": 0,
  "result_index": 0
 }
]