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DarthJonathanDarthJonathan/guac-skills/skills/arena/SKILL.md

arena

Skill0 starsChanged 18 days agoMITView on GitHub

Compare multiple attempts at the same design, implementation, or artifact, choose a base, and combine the strongest coherent parts. Use for arena requests.

84 other projects use a version of this.

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Used with

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Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
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The file

We split this skill into the rules it gives an agent

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

  • 0

    Common

    Most of the 83 edited versions we read keep these.

  • 3

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Read the Codex runtime adapter once per session before using this skill.

    line 6
  2. Distinctive:

    After candidates finish, read every artifact.

    line 14
  3. Distinctive:

    Verify the final synthesized artifact.

    line 17

“Common” is measured: kept by at least 6 in 10 of the 83 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

Read the Codex runtime adapter once per session before using this skill. Its tool, model, history, and authorization mappings apply to all linked instructions.

Arena

Compare alternatives under one goal rather than averaging incompatible designs.

  1. Frame the deliverable and a rubric with three to six concrete criteria. Give candidates the same task and grounding. Read the arena runners panel from pstack configuration; absent a panel, use two inherited-model candidates when delegation is available.
  2. Give each candidate its own worktree or output directory. Use permitted native subagents with self-contained briefs, output paths, and a short rationale. Queue work to respect the host's concurrency limit.
  3. After candidates finish, read every artifact. When available, use a separate read-only judge chosen from the arena cross-judge pool to score path-labeled candidates against the rubric. Prefer an explicitly configured different model only when supported and authorized. A sequential self-review is labeled as such.
  4. Choose the base that best meets the rubric and gives future maintainers the clearest boundaries. Investigate disagreements rather than treating a vote as proof.
  5. Review losing candidates for worthwhile ideas and integrate only those that fit the chosen design. If candidates already converge, record that and omit unnecessary grafts. If they diverge because the task is ambiguous, improve the framing.
  6. Verify the final synthesized artifact. A passing original candidate does not prove that the combined version works.

Return the artifact and a concise synthesis note: rubric, candidates actually completed, base, judge result or limitation, grafts, rejected tradeoffs, and verification. A missing candidate remains a gap. With no delegation capability, compare sequential alternatives and disclose that limitation.

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What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.