A

arena

by @backnotpropv
4.4(120)

该 Skill 让同一任务并行分发给多个模型生成多份候选成果,再由独立裁判按评分标准交叉评审,选出最稳的基座并把落选方案的亮点手工嫁接进去,最后验证综合成品。适合非平凡产物,避免一次尝试就锁死错误方向,一次拿到最靠谱的结构加最全的创意。

multi-agentorchestrationparallel-generationcross-judgingworkflowGitHub
安装方式
npx skills add https://github.com/backnotprop/pstack --skill arena
compare_arrows

Before / After 效果对比

2 组
使用前

非平凡的设计或代码只跑一次就定稿,方向一旦选错,后面几轮返工几乎等于重写,开发者常常整块成果推倒重来。

使用后

该 Skill 并行产出多份候选并交叉评审,先选可扩展性最好的基座再手工嫁接落选方案的亮点,首版方向就跑偏的概率大幅下降。

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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统计数据

安装量4.9K
评分4.4 / 5.0
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更新日期2026年10月23日
对比案例2 组

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4.4(120)
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37%
4
43%
3
13%
2
5%
1
2%

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0.0

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创建2026年10月4日
最后更新2026年10月23日
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