A

arena

by @backnotpropv
4.4(120)

この Skill は、同じタスクを複数の候補に並列で投げ、独立した審判エージェントが評価基準に沿って採点し、最も強い候補を土台に選び、敗れた候補の優れた部分を手作業で移植したうえで検証まで行います。非自明な成果物を一発の試行で誤った形に固定してしまうリスクを避け、最も安全な構造と最良のアイデアを同時に得たい場面で有効です。

multi-agentorchestrationparallel-generationcross-judgingworkflowGitHub
インストール方法
npx skills add https://github.com/backnotprop/pstack --skill arena
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Before / After 効果比較

2 组
使用前

非自明な設計やコードを一度書いただけで確定してしまい、方向を誤ると以降の修正はほぼ書き直しで、開発者は成果を丸ごと捨てることになります。

使用後

この Skill は複数の候補を並列生成して交叉評価し、拡張しやすい土台を選んでから敗れた候補の長所を手作業で移植するため、初版の方向が外れる確率が大きく下がります。

SKILL.md

Arena

Fan out N parallel attempts at the same task. Read every candidate end to end. Pick the strongest as the base. Graft the best ideas from the others into it. Verify the synthesized result.

Start

Open a todolist with one entry per phase before launching anything.

  1. Frame
  2. Fan out
  3. Cross-judge
  4. Pick
  5. Graft
  6. Verify

Phase A: Frame

The N candidates will receive the same prompt, so the prompt is the contract.

  1. State the artifact each candidate is producing.
  2. Derive the rubric. State what success looks like for this task, then turn it into 3-6 concrete gradeable criteria. The rubric is the picker's tool in Phase D. Candidates only see the task.
  3. Pick the runners. Use arena runners from the pstack settings file when present (~/.cursor/rules/pstack-models.mdc in Cursor, ~/.agents/pstack-models.md in other harnesses). Otherwise default to one each on claude-fable-5-1-thinking-max, gpt-5.6-sol-max, grok-4.6-fast-xhigh, claude-opus-5-thinking-xhigh. Spawn more when the arena covers multiple design directions. Same model N times when the work is generation-bound rather than judgment-sensitive.
  4. Assign output paths. Each candidate writes to its own location (a git worktree where possible, otherwise /tmp/arena-<slug>/candidate-<n>/), per the separate-before-serializing-shared-state principle skill.

Phase B: Fan out

Spawn all N subagents in one message with run_in_background: true, each with the task, the path to the shared grounding, its own output path, and instructions to produce both the artifact and a short rationale.

Each rationale names the alternatives the candidate considered and what it rejected.

If a candidate fails to produce output, proceed with N-1 and note the dropout in the synthesis record.

Phase C: Cross-judge

After all Phase B candidates complete, choose one model from the arena cross-judge pool in the pstack settings file when present. Otherwise use claude-fable-5-1-thinking-max, gpt-5.6-sol-max, grok-4.6-fast-xhigh, claude-opus-5-thinking-xhigh. Prefer a different model family from the parent's. Spawn one readonly judge subagent on that model. It sees the rubric and the candidates by path label, scores each criterion, and recommends a base with rationale. It runs in parallel with the parent's reading in Phase D, not with the candidates themselves. Don't spawn the judge while candidates are still writing.

Phase D: Pick a base

Read every candidate end to end before picking.

Score each candidate against the rubric criterion by criterion, not on holistic feel. Compare against the cross-judge. Agreement on the base confirms the pick. Disagreement means one of you is biased or the rubric was ambiguous. Read both rationales before deciding.

Pick the base on which candidate a future maintainer can extend most easily without breaking invariants. Prefer the cleaner boundary or smaller API when two feel tied, per the Laziness Protocol.

Record the pick and the reason in a short synthesis note alongside the base artifact, including the cross-judge's verdict.

Phase E: Graft

Walk each losing candidate once more and identify what is worth porting into the base. The signal is usually one or two things per candidate, not most of it.

Fold each graft in by hand, per the redesign-from-first-principles principle skill. Don't paste mechanically. The result has to remain coherent under one mental model.

Record what was grafted, from which candidate, and what was rejected and why.

When N candidates converge on the same shape, that is a strong agreement signal. Note the convergence in the record and ship the consensus shape. No graft is needed. When N candidates wildly diverge, Phase A was under-specified. Reframe and re-run rather than averaging the divergence.

Phase F: Verify

The synthesized artifact has to hold up under the same scrutiny as any other output, per the prove-it-works principle skill.

If verification surfaces a problem the arena did not catch, either Phase A was wrong (re-frame and re-run) or one candidate caught it and you missed the graft (go back to Phase E). Don't paper over.

Outputs

One synthesized artifact. One short synthesis note alongside, naming the base, the grafts (with source candidate), the rejections, the dropouts if any, and the verification result.

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統計データ

インストール数4.9K
評価4.4 / 5.0
バージョン
更新日2026年10月23日
比較事例2 件

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4.4(120)
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5%
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対応プラットフォーム

🤖claude-code

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作成2026年10月4日
最終更新2026年10月23日
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