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Claude Opus 5.5 (effort default (no --effort flag) · thinking on) on Water-quality sensor anomaly flags and daily means

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

Trust 80%Fully autonomoussuccess · first try
Estimated cost
$0.17
Wall time
35s
Human step-ins
0
Outcome
Pass

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Summary

Exact prompt

# Water-quality sensor anomaly flags `input.json` (synthetic buoy data) names `readings.csv` (`ts` "YYYY-MM-DDTHH:MM", `sensor`, `value`; rows are shuffled) and gives `stuck_n`, `window`, `spike_k`, and per-sensor `config` (`min`, `max`, `spike_floor`, `max_step`). Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json` writes `answer.json` and `chart.svg`. Fresh inputs are used too. ## Rules (per sensor, rows in ascending ts) 1. **missing**: value is blank or not a finite number (e.g. `err`, `nan`). It is skipped entirely (it does not break or extend a stuck run). 2. For other values, track the run of consecutive non-missing readings with exactly the same value (all flags included). Then the first match wins: - **range**: value < min or > max. - **stuck**: the current run length >= `stuck_n`. - **spike**: at least `window` ok values exist; with the last `window` ok values, med = median and MAD = median of |x - med|; flag if |v - med| > max(spike_k * MAD, spike_floor). - **rate**: |v - last ok value| > max_step. - else **ok**. Only ok values are used as "ok values", in daily means and in the chart. 3. `daily_means[sensor][date]` = mean of ok values that day (3 decimals); `counts[sensor]` has keys ok, missing, range, stuck, spike, rate; `flags` lists every non-ok reading `{"ts", "sensor", "flag"}` sorted by ts then sensor. ## Visual output: chart.svg Static, self-contained SVG (root `<svg>`, no scripts, images, links or external assets; renders headless) with `<polyline id="series-<sensor>">` for each sensor (`series-temp_c`, `series-do_mgl`, `series-ph`) and an element `id="flag-summary"`. The drawing itself is not graded. ## answer.json `{"counts": {...}, "daily_means": {...}, "flags": [...]}` ## Scoring Hidden checks (partial credit; all must pass): `missing_and_range`, `stuck_detection`, `spike_and_rate` (with ok counts), `flag_list`, `daily_means` (within 0.0011), `chart_svg_static` (chart.svg exists, follows the static rules, has the ids/tags above), `fresh_run_1`, `fresh_run_2`. 60-second limit per run.

Shuffled rows, a stuck run that ignores missing values but includes flagged ones, a robust median/MAD spike test with a floor that only uses ok history, a rate test against the last ok value, and flag precedence. 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).

Benchmark code for comparing AI models, not navigation or safety software.

Success criteria: 1. missing_and_range 2. stuck_detection 3. spike_and_rate 4. flag_list 5. daily_means 6. chart_svg_static 7. fresh_run_1 8. fresh_run_2

Setup notes: Use Case Scout batch 10 case k12-water-quality-anomalies (offline fixture). Prepared with batch10/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 KEYDIR`. Tools: Read/Write/Edit/Glob/Grep/Bash, web disabled. Single run, no retries. Visual output (chart.svg) rendered offline to PNG with google-chrome headless, network blocked, 1280x800 at ~3 s after load. Claude Code's own reported cost for claude-opus-5.5: $0.167107. 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
$0.17
Wall time
35s
Human step-ins
0
Turns
4
Model usage
ModelInput tokOutput tokCost
AClaude Opus 5.5anthropic/claude-opus-5.5effort default (no --effort flag) · thinking on60,4013,422$0.17

Reproduce this run

  1. Use the use case prompt on the use case page.
  2. 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.
  3. It counts as a success only if it meets every success criterion: 1. missing_and_range 2. stuck_detection 3. spike_and_rate 4. flag_list 5. daily_means 6. chart_svg_static 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

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Evidence log
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  "result": "I've written `solve.py`, and running `python solve.py input.json answer.json` on the sample data works: it produces `answer.json` and `chart.svg`. I haven't tested it on any other inputs, and the hidden checks haven't been run.\n\n**Results on the sample data (432 readings):**\n\n| sensor | ok | missing | range | stuck | spike | rate |\n|---|---|---|---|---|---|---|\n| temp_c | 134 | 3 | 1 | 2 | 4 | 0 |\n| do_mgl | 131 | 3 | 1 | 3 | 3 | 3 |\n| ph | 135 | 2 | 1 | 3 | 2 | 1 |\n\nThat gives 32 flagged readings in `flags`, sorted by time and then sensor.\n\n**Where the rules left room for choice, I did this:**\n- **Missing values:** anything that doesn't parse as a finite number counts as missing. That includes blanks, `err`, `nan` and `inf`.\n- **Stuck runs:** values are compared as numbers, so `7.0` and `7.00` count as the same value. Range-flagged readings still count toward the run.\n- **Spike check:** it uses the last `window` ok values *before* the current reading. The reading being checked is not part of its own window.\n- **Rate check:** it only runs once the sensor has at least one earlier ok value.\n- **Same timestamp:** if a sensor has two readings at one timestamp, they're processed in file order.\n- **Unknown sensors:** rows for sensors not listed in `config` are ignored. Sensors in `config` with no readings get all-zero counts.\n- **File path:** `readings` is looked up relative to the folder `input.json` is in. `chart.svg` is written next to `answer.json`.\n\n**The chart:** `chart.svg` has one panel per sensor with an ok-values-only line using the required `series-<sensor>` ids. At the bottom, a text element with `id=\"flag-summary\"` lists every sensor's counts. The file has no scripts, images, links or external references.",
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