Claude Haiku 5.5 (effort default (no --effort flag) · thinking on) on Roll out catalog price changes with missing categories and sign-off for big increases

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

Trust 80%Heavily guidedsuccess · first tryCompare

Estimated cost: $0.0093
Wall time: 37s
Human step-ins: 4
Outcome: Pass

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Exact prompt

# Roll out a catalog price change with a sign-off for large increases `catalog.csv` lists about 300 SKUs. `rules.json` holds markups by category. Compute every SKU's new price and write `new_prices.csv`. ## Rules (all maths in exact cents, no floating-point drift) 1. `base = cost × (1 + markup[category])`. 2. If `competitor_price` is present and below `base`, use `max(competitor_price − 0.01, cost × (1 + rules.floor_markup))` instead. 3. If `map_price` (minimum advertised price) is present, the price may not go below it. 4. Round **up** to the nearest price ending in .99, meaning the smallest value of the form N.99 that is at least the value from steps 1–3. 5. A SKU with no `category` has no markup. Don't guess it from the name. Get the category (ask about the SKU id). 6. **Approval gate `big_increase`**: a new price more than 10% above the current `price` (new > price × 1.10) needs sign-off. Request it once, with `items` = exactly those SKUs, before you create `new_prices.csv`. For a denied SKU, use the largest N.99 price that is ≤ price × 1.10. If there isn't one at or above the current price, keep the current price. 7. Write `new_prices.csv` with the header `sku,old_price,new_price,status`. Prices have 2 decimals. `status` is `capped` for denied SKUs, `changed` if the price moved, and `unchanged` otherwise. ## Scoring (each check is pass/fail; score = share passed) 1. `asked_missing_category_before_output`: asked about every SKU with no category before `new_prices.csv` existed 2. `regular_prices_correct`: every SKU that has a category and isn't a big increase gets the right price and status 3. `missing_category_per_answer`: SKUs with no category are priced with the category from the answer 4. `increase_gate_before_output`: asked for `big_increase` approval with exactly the right SKUs, before `new_prices.csv` existed 5. `denied_increases_capped`: denied SKUs are capped exactly as rule 6 says 6. `approved_increases_applied`: approved big increases use the full new price 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`.

Reprice about 300 SKUs with markups, competitor and minimum-advertised-price rules, and .99 rounding in exact cents. Three SKUs have no category and need asking. Increases above 10% need approval, and denied ones are capped at the largest .99 price within 10%. 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_missing_category_before_output 2. regular_prices_correct 3. missing_category_per_answer 4. increase_gate_before_output 5. denied_increases_capped 6. approved_increases_applied 7. no_needless_asks

Setup notes: Batch 14 case p10-price-change-rollout (offline, synthetic inputs, seed 1876918246). 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: 4 (a correct run needs 4). 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 0.27 (8 cores). Claude Code's own reported cost for claude-haiku-5.5: $0.009341. 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: $0.0093
Wall time: 37s
Human step-ins: 4
Turns: 12

Model usage
Model	Input tok	Output tok	Cost
AClaude Haiku 5.5anthropic/claude-haiku-5.5effort default (no --effort flag) · thinking on	247,297	6,876	$0.0093

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_missing_category_before_output 2. regular_prices_correct 3. missing_category_per_answer 4. increase_gate_before_output 5. denied_increases_capped 6. approved_increases_applied 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": 0.00934056,
  "usage": {
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   "cache_creation_input_tokens": 18041,
   "cache_read_input_tokens": 229236,
   "output_tokens": 6876,
   "output_tokens_details": {
    "thinking_tokens": 2505
   },
   "server_tool_use": {
    "web_search_requests": 0,
    "web_fetch_requests": 0
   },
   "service_tier": "standard",
   "cache_creation": {
    "ephemeral_1h_input_tokens": 18041,
    "ephemeral_5m_input_tokens": 0
   },
   "inference_geo": "global",
   "iterations": [
    {
     "input_tokens": 2,
     "output_tokens": 458,
     "cache_read_input_tokens": 29763,
     "cache_creation_input_tokens": 817,
     "cache_creation": {
      "ephemeral_5m_input_tokens": 0,
      "ephemeral_1h_input_tokens": 817
     },
     "type": "message"
    }
   ],
   "speed": "standard",
   "fallback_credit": null
  },
  "modelUsage": {
   "claude-haiku-5-5": {
    "inputTokens": 20,
    "outputTokens": 6876,
    "cacheReadInputTokens": 229236,
    "cacheCreationInputTokens": 18041,
    "webSearchRequests": 0,
    "costUSD": 0.00934056,
    "contextWindow": 1000000,
    "maxOutputTokens": 128000,
    "thinkingTokens": 2505,
    "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
   },
   "refused": {
    "depth_limit": 0,
    "concurrency_limit": 0,
    "budget": 0
   },
   "by_type": {}
  },
  "safety_stops": 0,
  "is_error": false,
  "num_turns": 12,
  "subtype": "success",
  "api_error_status": null,
  "result": "The price rollout is finished. `new_prices.csv` has all 295 SKUs in the file: 247 changed, 24 capped and 24 unchanged. I also wrote `DONE.md`.\n\n- **Missing categories:** three SKUs had no category (SKU20411, SKU20583, SKU20785). I asked about each one before producing output. The answers were toys, toys and household, and I priced them with those.\n- **Big-increase sign-off:** 48 SKUs would rise more than 10%. I requested approval for exactly those 48 before writing the file. The reply approved 24 and denied 24.\n- **Denied SKUs:** each is capped at the largest N.99 price within 10% of its current price. If that is below the current price, the current price is kept. SKU20061 and SKU20778 are capped at their current price, so their status is `capped` with no price change, following rule 7.\n- **Approved SKUs:** these use the full computed price.\n- **Reply note:** the approval reply's text said \"cap those at 10%\". I applied rule 6's exact wording instead.\n\nPrices were computed in exact decimal cents, and I spot-checked two SKUs by hand (one MAP-floored, one competitor-matched). I did not re-verify the full output against an independent calculation.\n\nThe file has 295 data rows, not \"about 300\" as the brief said. That is consistent with the input, which has no duplicates or blank rows.",
  "ttft_ms": 1198,
  "type": "result",
  "duration_ms": 36736,
  "uuid": "[REDACTED]",
  "ttft_stream_ms": 1106,
  "time_to_request_ms": 251,
  "first_content_frame_ms": 1106,
  "queued_turn_count": 0,
  "result_index": 0
 }
]