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Claude Sonnet 5.5 (effort default (no --effort flag) · thinking on) on Best sailing angle and leg times from a polar table

AClaude Sonnet 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
$0.06
Wall time
26s
Human step-ins
0
Outcome
Pass

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Summary

Exact prompt

# Best sailing angle from a polar table `input.json` (synthetic) has a boat polar (`polar.tws` wind speeds in knots, `polar.twa` true wind angles in degrees ascending, `polar.speed[j][k]` = boat speed at `twa[j]`, `tws[k]`), the current true wind speed `tws`, and a list of `legs` each with `bearing` (degrees true to the mark), `distance_nm` and `twd` (true wind direction, degrees the wind blows FROM). Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json` writes `answer.json` and `polar.svg`. Fresh inputs are used too, so no hard-coding. ## Rules 1. Boat speed at (tws, a): for each table row, interpolate linearly in tws (clamped to the first/last column outside the range); then interpolate linearly in twa between rows (clamped to 180 at the end). For a < `twa[0]` (no-go zone) speed is 0. 2. `upwind`: over integer a in 0..90, maximise VMG = speed*cos(a); `downwind`: over integer a in 90..180, maximise -speed*cos(a). Compare VMG rounded to 4 decimals; on a tie take the smaller angle. Report `{"twa": a, "vmg": value rounded to 3}`. 3. Each leg: over integer courses c = 0..359, a = smallest angle between c and `twd` (0..180), s = speed(tws, a), VMC = s*cos(c - bearing). Take the c with the greatest VMC compared at 6 decimals; ties keep the smaller c. Report `course`, `twa` (a), `boat_speed` (3 dp), `vmc` (3 dp), `hours` = distance / VMC (3 dp) or null if VMC <= 0. 4. `total_hours` = sum of non-null leg hours (3 dp). ## Visual output: polar.svg Static, self-contained SVG (root `<svg>`, no scripts, images, links, fonts or other external assets; renders headless), containing an element `id="polar-curve"` (the polar curve at the given tws) and `<line id="best-upwind">` and `<line id="best-downwind">` drawn from the origin along the best angles. The drawing itself is not graded. ## answer.json `{"upwind": {"twa", "vmg"}, "downwind": {"twa", "vmg"}, "legs": [{"course", "twa", "boat_speed", "vmc", "hours"}, ...], "total_hours"}` ## Scoring Hidden checks (partial credit = fraction passed; all must pass): `best_upwind_downwind`, `upwind_and_downwind_legs` (legs 0-1), `reaching_legs_and_total` (legs 2-3 and total), `polar_svg_static` (polar.svg exists, follows the static rules, has the ids/tags above), `fresh_run_1`, `fresh_run_2`. Numbers within 0.002; integers exact. 60-second limit per run.

Bilinear polar lookup with clamping and a no-go zone, integer angle searches with stated rounding and tie rules, and course-vs-mark VMC that forces tacking or gybing angles on beats and runs. 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. best_upwind_downwind 2. upwind_and_downwind_legs 3. reaching_legs_and_total 4. polar_svg_static 5. fresh_run_1 6. fresh_run_2

Setup notes: Use Case Scout batch 10 case k02-polar-vmg-optimizer (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 (polar.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-sonnet-5.5: $0.060806. Score: 6/6. 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
$0.06
Wall time
26s
Human step-ins
0
Turns
3
Model usage
ModelInput tokOutput tokCost
AClaude Sonnet 5.5anthropic/claude-sonnet-5.5effort default (no --effort flag) · thinking on59,3702,129$0.06

Reproduce this run

  1. Use the use case prompt on the use case page.
  2. Same setup: Claude Sonnet 5.5 (anthropic/claude-sonnet-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. best_upwind_downwind 2. upwind_and_downwind_legs 3. reaching_legs_and_total 4. polar_svg_static 5. fresh_run_1 6. 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 wrote `solve.py`, and `python solve.py input.json answer.json` now writes both `answer.json` and `polar.svg`. I ran it once on the supplied `input.json` and it completed. I haven't checked the numbers against any reference, rendered the SVG, or tried a fresh input.\n\nThe results I looked at (the first two legs and the best angles; I didn't read the rest of the output):\n\n| | Angle | Value |\n|---|---|---|\n| Best upwind | 39\u00b0 | VMG 5.085 |\n| Best downwind | 165\u00b0 | VMG 7.385 |\n\n| Leg | Course | Angle to wind | Boat speed | VMC | Hours |\n|---|---|---|---|---|---|\n| 0 | 49 | 39\u00b0 | 6.543 | 5.156 | 1.629 |\n| 1 | 61 | 165\u00b0 | 7.645 | 7.505 | 1.106 |\n\nLeg 2 comes out at course 30, 91\u00b0 to the wind, VMC 7.972 and 0.665 hours. I didn't read legs 3 onward or `total_hours`.\n\nThe script uses only the standard library and reads the polar, wind speed and legs from the input, so nothing is hard-coded to this file. It follows your rules for interpolation, the no-go zone, tie-breaking and rounding.\n\n`polar.svg` is static and self-contained, with no scripts, images or external assets. It contains the `polar-curve` polyline and the `best-upwind` and `best-downwind` lines drawn from the origin. The script overwrote the `polar.svg` that was already in the folder.",
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