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best-of-n

Generate independent options for design, implementation, explanations, or debugging, then compare them using one shared rubric. Code candidates use separate working copies and undergo independent verification before a winner is presented for review.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

Generate a small set of independent candidate solutions in worktrees, judge them against one explicit rubric, and apply the winner only after PASS verification.

설치 방법 보기

이럴 때 유용합니다

  • Comparing several plausible design approaches
  • Evaluating independent implementations of the same task
  • Comparing explanations or debugging approaches
  • Reviewing a verified candidate before adoption

포함된 파일(1)

  • SKILL.md4.6 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Best of N

Use this skill when a consequential design, implementation, explanation, or debugging task has several plausible solutions and comparison is worth the extra model work. In Operate mode this is the preferred ensemble pattern for high-stakes or ambiguous approaches. Do not use it for a tiny change or when the user has already chosen the approach.

Set The Tournament

  1. Define one task, one evidence packet, and one explicit scoring rubric before launching candidates. Include correctness, fit to the request, simplicity, risk, and verification.
  2. Choose N from 2 to 4 for a quick comparison (default 3). For an explicit experimental search, use the Workflow search option: 2–16 live candidates, with larger validated populations queued at the Workflow host's 16-worker concurrency gate rather than launched at once.
  3. Give every candidate the same task and rubric. Add only a candidate number; do not steer candidates toward different conclusions unless diversity is an explicit part of the request.
  4. Prefer a session goal (create_goal or active /goal) when the tournament spans more than one parent turn.

Generate Independently

Start the candidates as parallel background agent workers and return agent_ids immediately so the parent stays free. For proposals, reviews, or research, keep them read-only:

{
  "action": "start",
  "name": "candidate_1",
  "prompt": "Produce candidate 1 for the task below. Return the proposal, evidence, risks, and rubric self-score. Do not edit files.\n\n<TASK AND RUBRIC>",
  "type": "worker",
  "model_strength": "same",
  "write_authority": "read_only"
}

Launch the remaining candidates with the same contract, then use agent wait or completion events to collect every result. Do not show one candidate another candidate's answer before generation finishes.

When candidates must implement code, give each one:

  • type: "builder"
  • worktree: true
  • write_authority: "worktree_write"
  • the same bounded write_roots or exact_files

Never run parallel writers in the parent checkout. Each builder must return the structured candidate contract (candidate id, hypothesis, paths, commands, self-verdict, risks, and artifact references). A self-verdict is evidence to inspect, not a hard-gate result.

Optional diversity: pin different model / Fleet fleet_profile values when the project has multiple capable routes; otherwise keep model strength same.

Judge Once

Use one read-only reviewer worker, or the parent when the result is small, to score all candidates against the original rubric. The judge must:

  • cite evidence from each candidate rather than vote by style;
  • reject candidates that violate authority, scope, or verification gates;
  • treat candidate-reported commands and PASS claims as untrusted until replay;
  • name the winner and the decisive reasons;
  • identify useful pieces worth combining, if any;
  • say when the candidates are tied or all fail.

Do not ask candidates to vote for themselves. Do not silently merge incompatible approaches into a new unreviewed solution.

Integrate Only After PASS

For proposal-only work, return the winning answer with a compact score summary. For code work:

  1. Freeze the baseline, evaluator, hard gates, score rule, and authority before a larger search admits candidates. Any evaluator change starts a revision.
  2. After a worker loses write authority, apply its patch to a clean baseline and let the runtime—not that worker—run hard gates and scoring.
  3. Inspect the winning worktree diff and independently replay it on the clean baseline. A different read-only model may look for gaming, but deterministic tests remain the authority.
  4. Present the verified winner for review. Applying or merging is a separate, explicit user action; NONE is valid when every candidate fails.
  5. Preserve losing and failed candidate receipts as useful negative results.

The checked-in operate_best_of_n.workflow.js recipe supports strategy: "search" for structured 2–16 candidate generation and review. It does not yet turn prompt-listed commands into hidden runtime gates. Do not advertise those gates until a runtime evaluator host consumes a frozen search spec.

Stop early when one candidate reveals a hard constraint that invalidates the tournament. Report the negative result rather than spending the remaining budget to manufacture variety.

리뷰

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