跳到正文
cccskills
免费在 GitHub 公开

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.

中文简介正在准备中。当前显示英语简介。

原文查看原文说明

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

查看安装方法

适用场景

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

包含的文件(1)

  • SKILL.md4.6 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

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.

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

batch

免费

Split a large goal into clearly bounded tasks that can run in parallel. Coordinate existing agent and worktree tools, then combine and verify the results. The skill starts only when explicitly requested.

中文简介正在准备中。当前显示英语简介。

  • Splitting a large goal into parallel tasks
  • Coordinating work across multiple agents
  • Running tasks in separate worktrees
codewhale-hq/Codewhale4.1万2026年10月19日 更新

Check Codewhale release readiness through ordered tests, builds, version checks, and manual terminal checks. Record command results and observed behavior, including responsiveness, route isolation, and follow-up input handling, while explicitly identifying any skipped checks.

中文简介正在准备中。当前显示英语简介。

  • Run automated checks on the actual release branch
  • Check whether a PR merges into the release branch
  • Manually test terminal responsiveness and input behavior
codewhale-hq/Codewhale4.1万2026年10月19日 更新

Operate desktop apps and isolated browsers on registered computers by inspecting interface controls, entering information, and checking results. The skill also covers screenshots and recording, with separate routing for the user's signed-in Chrome through Chromewhale.

中文简介正在准备中。当前显示英语简介。

  • Entering information through a desktop app interface
  • Working in an isolated browser session
  • Capturing screenshots or recordings for a task
codewhale-hq/Codewhale4.1万2026年10月19日 更新

Check a local Codewhale checkout before contributing: inspect branch and sync status, build the CLI, and run the repository’s CI checks. Present recent commits and changelog entries from local files, with clear status for each step.

中文简介正在准备中。当前显示英语简介。

  • Checking a checkout before contributing
  • Reviewing local changes and sync status
  • Running the build and CI verification commands
codewhale-hq/Codewhale4.1万2026年10月19日 更新

Builds and installs Codewhale with a source revision identifier, then verifies the installed version in a fresh shell. It guides hands-on runtime checks for interface behavior, routing, approvals, and other changes that automated tests may miss.

中文简介正在准备中。当前显示英语简介。

  • Verifying terminal interface changes in the actual product
  • Installing and checking a local release build
  • Checking model routing and approval behavior
codewhale-hq/Codewhale4.1万2026年10月19日 更新

cw-gates

免费

Verify Codewhale changes with checks matched to their scope and risk. Collect actual test counts, automated limit checks, and command output to show what passed, what failed, and what remains unverified.

中文简介正在准备中。当前显示英语简介。

  • Verify a fix before marking it complete
  • Choose tests for the changed area
  • Check limits and consistency rules enforced by CI
codewhale-hq/Codewhale4.1万2026年10月19日 更新

查看 codewhale-hq 的全部技能

报告此技能的问题