Arena

作者 backnotprop3a604672c46c无许可证1.3K 个星标收录于 2026年10月8日更新于 2026年10月8日仓库今天更新

Spawn N parallel candidates at the same task, pick a base, graft the strongest parts of the losers into it. Use for /arena, 'arena this', 'throw it in the arena', or when one attempt at a non-trivial artifact would lock in the wrong shape.

仅含说明AI & Agents
AI 生成的概览

对同一任务并行生成多个候选方案,选出最佳作为基础,并把其余方案中最强的部分移植进去。

功能
Arena 针对同一任务并行展开 N 个候选尝试,每个候选产出一份成果物和一段简短理由。独立的交叉评审子代理按评分标准为候选打分,主代理则逐份通读候选以选出基础方案。落选候选中最有价值的想法会被手工融入基础方案,最终结果再经过验证。产出为一份综合成果物和一份简短的综合说明,记录基础方案、移植内容、被否决项、掉队情况与验证结果。
适用场景
当非平凡成果物只做一次尝试可能锁定错误形态时,或需要并行探索多个设计方向时使用。可通过 /arena、'arena this' 或 'throw it in the arena' 触发。
运行要求
需要能够并行启动后台子代理和只读评审子代理的子代理工具,以及模型配置文件(pstack 设置)或回退模型标识。候选输出写入 git worktree 或临时目录。该技能不附带脚本。

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 the arena runners line in the pstack settings file (~/.cursor/rules/pstack-models.mdc in Cursor, ~/.agents/pstack-models.md in other harnesses). If the file or that line is missing, default to one each on claude-opus-5-5-xhigh and grok-4.7-xhigh-fast. An auto or inherit-parent entry in this line or the cross-judge line means the parent model, so omit model for it. If your subagent tool rejects a configured entry, run that seat on its family's default and say so. Families go by prefix: claude-* and grok-*. With no family match, use claude-opus-5-5-xhigh. If it rejects a default, use the closest valid slug of the same family from its error message or your harness's model list. 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 line in the pstack settings file. If the file or that line is missing, choose from claude-opus-5-5-xhigh and grok-4.7-xhigh-fast. 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.

来源与署名

来源:backnotprop/pstack位于skills/arena提交3a60467

许可证: 无许可证

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