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JaxsonmanJaxsonman/Crucible/skills/report/SKILL.md

report

Skill0 starsChanged 36 days agoMITView on GitHub

Show and explain Crucible benchmark results for one task or across tasks, with the recommended model configuration. Use when the user asks which model or effort to use, or how a benchmark went.

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Used with

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The file

We split this skill into the rules it gives an agent

4 rules. 0 are common, 1 is distinctive, and 3 are very specific to this project.

  • 0

    Common

    Most of the 68 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 3

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Run crucible report $ARGUMENTS from the repo.

    line 12
  2. Very specific:

    If it says there are no results, tell the user to run /crucible:run <task-id> first.

    line 16
  3. Distinctive:

    If no config meets the bar, say so and name the best candidates instead of recommending one.

    line 27
  4. Very specific:

    If it prints Recommendation: none.

    line 28

“Common” is measured: kept by at least 6 in 10 of the 68 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Report benchmark results

Arguments: $ARGUMENTS.

  1. Run crucible report $ARGUMENTS from the repo. With a task id it compares every configuration for that task; without one it reports across tasks, grouped by task kind, with a recommendation per kind. Flags: --batch, --json, --kind, --min-attempts, --threshold.
  2. If it says there are no results, tell the user to run /crucible:run <task-id> first. Do not start a run yourself; it spends usage.
  3. Summarise in plain language, not by pasting the table alone:
    • the recommendation and the evidence behind it (success rate with its 95% interval, cost per success, median time), and on how many tasks it was compared (the cross-task report prints Compared on N of M tasks attempted by every config);
    • the runner-up, and the trade-off against it;
    • caveats: few attempts or a wide interval, Unknown results, Stopped attempts (a limit ended them), Judge≠check disagreements, unpriced attempts, a warning that a config pooled several models, or a warning that the task changed between batches. When the report says "compared on N of M tasks" with N below M, or "no common task", relay it. If no config meets the bar, say so and name the best candidates instead of recommending one. If it prints Recommendation: none; no task was attempted by every config, say there is no common task to compare on and suggest running the same task with every config; do not pick a winner.

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