Arena

作者 cursorccb5507cec15无许可证10K 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,选出最强的一个作为基础,并把其余方案中的优点移植进去。

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的理由说明。它会依据评分标准对候选进行交叉评审,选出一个作为基础,并手动把落选候选中最强的想法移植进去。最后它会验证综合后的结果,产出综合成果物以及一份简短的综合说明,记录基础方案、移植内容、被否决的内容、中途失败的候选以及验证结果。
适用场景
当对非平凡成果物只做一次尝试就可能固化错误形态时,或需要在定稿前探索多个设计方向时使用。它由诸如“/arena”“arena this”或“throw it in the arena”之类的请求触发。
运行要求
仅为指令,不附带脚本。它需要能够生成子代理并后台运行,以及只读的评审子代理;需要为每个候选指定输出路径,例如 git worktree 或 /tmp 目录;还需要从规则文件读取模型配置,并在该配置缺失时给出了默认模型标识。

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 ~/.cursor/rules/pstack-models.mdc. If the rule 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 the Task 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. 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 ~/.cursor/rules/pstack-models.mdc. If the rule 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:cursor/plugins位于pstack/skills/arena提交ccb5507

许可证: 无许可证

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