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Claude Haiku 5.5 (effort default (no --effort flag) · thinking on) on Turn weekly ad-platform exports into an exact wasted-spend report

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

Trust 80%Fully autonomoussuccess · first trySuperseded
Estimated cost
$0.02
Wall time
2m
Human step-ins
0
Outcome
Pass

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Superseded. This run was rescored on 2026-10-20 and replaced by a newer run. It no longer counts toward rankings, use-case tables or the data export; it is kept here for the record.

Summary

Exact prompt

# Weekly ads review: wasted-spend report from Google-style and Meta-style exports The files are synthetic exports for one week: `google_ads.csv`, `meta_ads.csv` and `config.json`. Write `solve.py` (Python 3 standard library only, no network). `python solve.py input.json answer.json` must write `answer.json` (the first argument is unused; read the three files from the current directory). Fresh exports are generated when scoring. ## Parsing rules 1. `google_ads.csv`: skip lines before the header line that starts with `Day,`. Rows whose `Day` is not a date like `Oct 12, 2026` (e.g. `Total: ...` rows) are non-data rows. `Cost` and `Conv. value` may contain thousands commas or be `--` (= 0). Spend currency is the row's `Currency code` (USD or EUR). 2. `meta_ads.csv`: may start with a UTF-8 BOM. The spend column is named `Amount spent (USD)` or `Amount spent (EUR)`; that currency applies to every row. Use `Reporting starts` as the row date. Empty numeric cells = 0. 3. Meta `Results` count as conversions **only** when `Result indicator` is `actions:offsite_conversion.fb_pixel_purchase`; otherwise the row has 0 conversions (its results are clicks, not conversions). `Purchases conversion value` is the conversion value. 4. Convert EUR to USD by multiplying each row's amount by `eur_to_usd`. Keep full precision while summing; round only final reported money values to 2 decimals, half-up. 5. Drop rows dated outside `week_start`..`week_end` (inclusive). Drop exact duplicate lines within a file (keep the first). Count drops in `rows_dropped`: `{"duplicates", "out_of_range", "non_data"}` (a duplicate is counted as a duplicate even if also out of range; check duplicates first, then non-data, then range). 6. A unit is (platform `google`|`meta`, campaign, ad group/ad set). Names are matched after trimming, collapsing internal whitespace and ignoring case; report the first-seen form in file order (trimmed and whitespace-collapsed). ## Metrics and waste rules (per unit, over the week) 7. `spend` (USD), `clicks` (int), `conversions`, `conv_value` (USD; Google values follow the row currency, Meta values the file currency). 8. `cpa` = spend / conversions rounded to 2 dp, `null` when conversions = 0. `roas` = conv_value / spend rounded to 4 dp, `null` when spend = 0. 9. `reason` = `"zero_conversions"` when conversions = 0 and spend >= `zero_conv_min_spend_usd`, then `wasted` = spend. `reason` = `"high_cpa"` when conversions > 0 and cpa (unrounded) > `cpa_multiple` x `target_cpa_usd`, then `wasted` = spend - conversions x `target_cpa_usd`. Otherwise `reason` = null, `wasted` = 0. 10. `units` order: wasted descending, then spend descending, then platform, campaign, ad group ascending (case-insensitive). ## answer.json `{"units": [{"platform", "campaign", "ad_group", "spend", "clicks", "conversions", "conv_value", "cpa", "roas", "reason", "wasted"}], "totals": {"spend", "wasted", "wasted_pct", "by_platform": {"google": {"spend", "wasted"}, "meta": {"spend", "wasted"}}}, "top5": [[platform, campaign, ad_group] for the first 5 units with wasted > 0], "rows_dropped": {...}}`. `wasted_pct` = 100 x wasted / spend, 1 dp. Totals are sums of the rounded unit values. ## Optional visual: chart.svg If you write `chart.svg`: a static, self-contained SVG (root `<svg>`, no scripts, images, links, external URLs or `on*` handlers) with an element `id="wasted-bars"` and one element `id="bar-1"` .. `id="bar-N"` for the top-5 units in order. It is not required; if present it must follow these rules. ## Scoring Hidden checks (partial credit; all must pass; money within 0.005): `parse_dedupe_and_drops`, `unit_keys_and_spend_usd`, `conversions_value_and_result_indicator`, `waste_rules`, `totals_ranking_top5`, `chart_svg_if_present` on the given exports; `fresh_run_1`, `fresh_run_2` (whole answer on new exports). 30-second limit per run.

Every messy-export trap moves real dollars between units, so a quick pandas-style sum gets the shape right and most numbers wrong. 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. parse_dedupe_and_drops 2. unit_keys_and_spend_usd 3. conversions_value_and_result_indicator 4. waste_rules 5. totals_ranking_top5 6. chart_svg_if_present 7. fresh_run_1 8. fresh_run_2

Setup notes: Use Case Scout batch 13 case n05-ads-wasted-spend (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 0.10 (8 cores). chart.svg rendered offline with google-chrome headless (CDP pipe, network blocked): PNG 1280x800 at ~3 s after load. Claude Code's own reported cost for claude-haiku-5.5: $0.023372. Score: 8/8. 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.

Output

Metrics

Estimated cost
$0.02
Wall time
2m
Human step-ins
0
Turns
6
Model usage
ModelInput tokOutput tokCost
AClaude Haiku 5.5anthropic/claude-haiku-5.5effort default (no --effort flag) · thinking on182,00926,804$0.02

Reproduce this run

  1. Use the use case prompt on the use case page.
  2. 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.
  3. It counts as a success only if it meets every success criterion: 1. parse_dedupe_and_drops 2. unit_keys_and_spend_usd 3. conversions_value_and_result_indicator 4. waste_rules 5. totals_ranking_top5 6. chart_svg_if_present 7. fresh_run_1 8. fresh_run_2
  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

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

Evidence log
[
 {
  "duration_api_ms": 109061,
  "stop_reason": "end_turn",
  "session_id": "[REDACTED]",
  "total_cost_usd": 0.02337247,
  "usage": {
   "input_tokens": 10,
   "cache_creation_input_tokens": 42892,
   "cache_read_input_tokens": 139107,
   "output_tokens": 26804,
   "output_tokens_details": {
    "thinking_tokens": 20159
   },
   "server_tool_use": {
    "web_search_requests": 0,
    "web_fetch_requests": 0
   },
   "service_tier": "standard",
   "cache_creation": {
    "ephemeral_1h_input_tokens": 42892,
    "ephemeral_5m_input_tokens": 0
   },
   "inference_geo": "global",
   "iterations": [
    {
     "input_tokens": 2,
     "output_tokens": 1213,
     "cache_read_input_tokens": 53481,
     "cache_creation_input_tokens": 1950,
     "cache_creation": {
      "ephemeral_5m_input_tokens": 0,
      "ephemeral_1h_input_tokens": 1950
     },
     "type": "message"
    }
   ],
   "speed": "standard",
   "fallback_credit": null
  },
  "modelUsage": {
   "claude-haiku-5-5": {
    "inputTokens": 10,
    "outputTokens": 26804,
    "cacheReadInputTokens": 139107,
    "cacheCreationInputTokens": 42892,
    "webSearchRequests": 0,
    "costUSD": 0.02337247,
    "contextWindow": 1000000,
    "maxOutputTokens": 128000,
    "thinkingTokens": 20159,
    "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": 6,
  "subtype": "success",
  "api_error_status": null,
  "result": "`solve.py` is written in `./usecase-runner/runs/n05-claude-haiku/work/`. It runs cleanly with `python3 -I solve.py input.json answer.json` and writes `answer.json` plus `chart.svg`. I can't see the hidden scoring checks, so correctness rests on reading the spec and hand-checking the output.\n\n**Results on the given exports**\n- Totals: spend $3,636.06, wasted $2,178.61 (59.9%). Google spend $1,301.91 / wasted $603.93; Meta spend $2,334.15 / wasted $1,574.68.\n- `rows_dropped`: `duplicates` 5 (3 Google, 2 Meta), `out_of_range` 25, `non_data` 2 (the `Total:` rows).\n- Top 5 wasted units: meta Competitor/35-54, meta Brand/Broad, meta Competitor/Remarketing 30d, meta Competitor/Broad, meta Video/Top Funnel.\n- A small synthetic export with a BOM, EUR Meta spend, a quoted `\"1,000.50\"` cost, and an out-of-range row gave the expected EUR\u2192USD conversions and drop counts.\n- `chart.svg` parses as XML and has `wasted-bars` and `bar-1`..`bar-5`.\n\n**Choices I made where the spec was open**\n- **Meta value and conversions:** a row only contributes conversions and `conv_value` when its `Result indicator` is the purchase indicator. Otherwise both are 0, so a non-purchase row's value is dropped even if the file has one.\n- **Display names:** the first-seen form comes from rows that survive dedupe and the date filter, not from every line in the file.\n- **Bad numeric cells:** anything unparseable other than blank or `--` is treated as 0 rather than crashing the run.\n- **Duplicates:** matched on the exact raw line, per file.\n- **Chart:** `chart.svg` is written next to `answer.json`, and it includes the standard SVG `xmlns` URL. If the checker treats any `http` string as an external URL, that is the line to change.\n- **Rounding:** all arithmetic is in `Decimal` (no float error); money is rounded half-up only at reporting.\n\nI did not run the scoring checks, and I did not test against generated fresh exports beyond the one synthetic case.",
  "ttft_ms": 1313,
  "type": "result",
  "duration_ms": 109634,
  "uuid": "[REDACTED]",
  "ttft_stream_ms": 1224,
  "time_to_request_ms": 259,
  "first_content_frame_ms": 1225,
  "queued_turn_count": 0,
  "result_index": 0
 }
]

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