reviewing-skills

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Reviewing Skills

技能评审

Objective

目标

Evaluate a skill directory as if you are an AI agent encountering it for the first time. Produce a read-only review with:
  • A weighted score + letter grade
  • Spec violations (blockers)
  • Prioritized findings (P1/P2/P3) with concrete, minimal fixes
  • Optional rewritten sections (only when needed to reach the quality bar)
This skill is intended to act as the critic in a generator<->critic loop (e.g., with
$writing-skills
).
以AI Agent首次接触技能目录的视角对其进行评估。生成一份只读评审报告,包含:
  • 加权分数 + 字母等级
  • 规范违反项(阻塞问题)
  • 按优先级划分的问题(P1/P2/P3)及具体、精简的修复方案
  • 可选的重写章节(仅在需要达到质量标准时提供)
本技能旨在充当生成器<->评审器循环中的评审角色(例如与
$writing-skills
配合使用)。

When to use / When not to use

适用场景 / 非适用场景

Use when:
  • The user asks to review, grade, or audit a skill folder containing
    SKILL.md
    .
  • The user wants rubric-based scoring and actionable edits (not just general advice).
Do not use when:
  • The user wants you to write a skill from scratch (use a writing skill instead).
  • The request is not about a skill directory or does not involve
    SKILL.md
    .
适用场景:
  • 用户要求评审、评级或审计包含
    SKILL.md
    的技能文件夹时。
  • 用户需要基于评分标准的打分可落地的修改建议(而非仅通用建议)时。
非适用场景:
  • 用户要求从零开始编写技能时(请使用写作类技能)。
  • 请求内容与技能目录无关或不涉及
    SKILL.md
    时。

Inputs

输入要求

You need a path to a skill directory that contains
SKILL.md
(and optionally
agents/openai.yaml
,
scripts/
,
references/
,
assets/
).
If the user did not provide a path:
  1. Look for directories in CWD that contain
    SKILL.md
    .
  2. If multiple, ask the user to choose.
需要提供包含
SKILL.md
的技能目录路径(可选包含
agents/openai.yaml
scripts/
references/
assets/
)。
若用户未提供路径:
  1. 查找当前工作目录(CWD)中包含
    SKILL.md
    的目录。
  2. 若存在多个目录,请让用户选择目标目录。

Outputs

输出内容

A read-only Markdown report with weighted grade, findings, and copy/paste patch text (see workflow step 6 for format rules).
一份只读Markdown报告,包含加权等级、问题项,以及可直接复制粘贴的补丁文本(格式规则见工作流第6步)。

Safety / Constraints (non-negotiable)

安全/约束条件(不可协商)

  • Read-only: do not edit, create, delete, or move files.
  • Do not execute untrusted code: do not run repo scripts/binaries unless the user explicitly asks and you can justify the risk.
  • Secrets: do not open or quote secrets (e.g.,
    .env
    , API keys, credentials). If encountered, redact and warn.
  • Network: do not browse the web or call external systems unless the user explicitly requests it.
  • No fabrication: if you cannot verify something, say so and recommend a verification step.
  • No deep reference chasing: read only what is needed to score accurately (one level deep).
  • 只读限制:不得编辑、创建、删除或移动文件。
  • 禁止执行不可信代码:除非用户明确要求且风险可控,否则不得运行仓库中的脚本/二进制文件。
  • 保密处理:不得打开或引用敏感信息(如
    .env
    、API密钥、凭证)。若遇到此类内容,需进行脱敏并发出警告。
  • 网络限制:除非用户明确要求,否则不得浏览网页或调用外部系统。
  • 禁止编造内容:若无法验证某内容,需明确说明并建议验证步骤。
  • 限制深度引用:仅读取为准确打分所需的内容(最多一层深度)。

Verification Rules

验证规则

Follow the verification protocol in references/skills-rubric.md. Budget: ~20 reads max.
遵循references/skills-rubric.md中的验证协议。读取次数上限:约20次。

Workflow (decision-complete)

工作流(决策闭环)

  1. Resolve the target skill directory
    • Confirm the path contains
      SKILL.md
      . If it does not, stop and ask for the correct folder.
  2. Read the minimum necessary context (in order)
    1. <skill>/SKILL.md
    2. <skill>/agents/openai.yaml
      (if present)
    3. Any files under
      <skill>/scripts/
      referenced by
      SKILL.md
      (only those)
    4. Any files under
      <skill>/references/
      referenced by
      SKILL.md
      (only those)
  3. (If in a git repo) gather change context
    • Prefer the repo’s base branch; if unknown, check
      git remote show origin
      for “HEAD branch”, otherwise try
      main
      then
      master
      (and state what you chose).
    • git diff <base> -- <skill>/
    • git log --oneline -20 -- <skill>/
    • For non-trivial diffs:
      git log -p -5 -- <skill>/SKILL.md
    • If
      <skill>/
      is new/untracked (so
      git diff <base>
      shows nothing), state that explicitly and treat contents as “new.”
    • If a score or finding is driven by a recent change, cite the relevant diff hunk or commit short-hash.
  4. Score using the rubric
    • Use
      references/skills-rubric.md
      (single source of truth).
    • Give each dimension a 1.0–5.0 score (0.5 increments allowed).
    • Compute weighted score as:
      sum(weight_i * score_i) / 100
      .
  5. Identify issues and merge duplicates
    • First list spec violations (blockers).
    • Then produce prioritized findings (max ~15 total), merging near-duplicates.
    • Every P1/P2 finding includes concrete patch text.
    • Patch rules: keep patches small/local; prefer "replace X with Y"; rewrite only the smallest section needed to clear P1/P2.
  6. Produce the report
    • Default to
      references/review-template.md
      structure.
    • If the user requires a different structure, preserve the same content (grade, dimension scores, blockers, prioritized findings with patch text, token efficiency notes).
    • If the user requests a forensic or diff-centric review, add a hunk-by-hunk analysis for meaningful changes (
      +/-
      context), and classify each as improvement/regression/neutral.
    • Only include “Rewritten sections” when score < 4.5, any P1 exists, or the author requests a rewrite.
Do not read assets unless explicitly relevant.
  1. 确定目标技能目录
    • 确认路径中包含
      SKILL.md
      。若未找到,需停止操作并询问正确的文件夹路径。
  2. 按顺序读取必要的最少上下文
    1. <skill>/SKILL.md
    2. <skill>/agents/openai.yaml
      (若存在)
    3. SKILL.md
      中引用的
      <skill>/scripts/
      下的文件(仅引用到的文件)
    4. SKILL.md
      中引用的
      <skill>/references/
      下的文件(仅引用到的文件)
  3. (若在Git仓库中)收集变更上下文
    • 优先使用仓库的基准分支;若未知,通过
      git remote show origin
      查看“HEAD branch”,否则依次尝试
      main
      master
      分支(需说明所选分支)。
    • 执行
      git diff <base> -- <skill>/
    • 执行
      git log --oneline -20 -- <skill>/
    • 对于非微小差异:执行
      git log -p -5 -- <skill>/SKILL.md
    • <skill>/
      为新增/未跟踪目录(
      git diff <base>
      无输出),需明确说明并将内容视为“新增”。
    • 若分数或问题项由近期变更导致,需引用相关差异片段或提交短哈希值。
  4. 基于评分标准打分
    • references/skills-rubric.md
      为唯一评分依据。
    • 为每个维度给出1.0–5.0的分数(允许0.5的增量)。
    • 加权分数计算公式:
      sum(weight_i * score_i) / 100
  5. 识别问题并合并重复项
    • 首先列出规范违反项(阻塞问题)。
    • 然后生成按优先级划分的问题项(最多约15个),合并近似重复项。
    • 每个P1/P2问题项需包含具体的补丁文本。
    • 补丁规则:保持补丁小而局部;优先使用“将X替换为Y”的形式;仅重写解决P1/P2问题所需的最小章节。
  6. 生成报告
    • 默认采用
      references/review-template.md
      的结构。
    • 若用户要求不同结构,需保留相同核心内容(等级、维度分数、阻塞问题、带补丁文本的优先级问题项、Token效率说明)。
    • 若用户要求取证或基于差异的评审,需对有意义的变更添加逐段分析(
      +/-
      上下文),并将其分类为改进/退化/中性。
    • 仅当分数<4.5、存在P1问题或作者要求重写时,才包含“重写章节”。
除非明确相关,否则不得读取assets目录下的内容。

Review Guidelines

评审指南

What to reward

加分项

  • High signal per token: dense, directive, minimal prose.
  • Correct triggering: description precisely indicates what and when.
  • Decision-complete workflow: the skill leaves no key decisions ambiguous.
  • Guardrails: destructive actions gated; secrets handled safely; constraints explicit.
  • Portability: avoids tool-vendor lock-in; uses capability language with optional adapters.
  • 高Token信号比:内容密集、指令明确、文字精简。
  • 触发条件准确:描述精准说明做什么何时做
  • 决策闭环工作流:技能未留下关键决策模糊点。
  • 防护机制完善:破坏性操作受限制;敏感信息处理安全;约束条件明确。
  • 可移植性强:避免工具供应商锁定;使用能力描述语言并支持可选适配器。

What to penalize

扣分项

  • Vague directives (“as appropriate”, “best practices”, “use standard approach”).
  • Over-broad scope (one skill trying to do too many disjoint jobs).
  • Reference chains (SKILL.md → reference → another reference).
  • Missing or non-actionable validation loops.
  • “Cute” verbosity that costs tokens without improving outcomes.
  • 模糊指令(如“视情况而定”、“最佳实践”、“使用标准方法”)。
  • 范围过宽(一个技能试图完成多个不相关的任务)。
  • 引用链过长(SKILL.md → 参考文档 → 另一参考文档)。
  • 缺失或不可落地的验证循环。
  • 无意义的冗余表述,浪费Token且未提升效果。

Additional checks (inform findings; not scored as a separate dimension)

额外检查项(用于补充问题项;不单独作为评分维度)

  • Terminology consistency for core concepts across sections.
  • Presence and usefulness of concrete examples/templates when output style matters.
  • Anti-pattern scan: Windows-style paths, too many options without a default, time-sensitive claims, deep reference chains, and assumed package installs.
  • 核心概念术语在各章节中的一致性。
  • 当输出风格重要时,是否存在具体示例/模板且实用。
  • 反模式扫描:Windows风格路径、无默认值的过多选项、时效性声明、深层引用链、未说明的依赖包安装假设。

Edge cases (common failure modes)

边缘情况(常见失败模式)

  • No git / no base branch: state what you could not verify; review file contents only.
  • Large skills: stick to the tight-budget read order; do not “read everything” by default.
  • Missing referenced files: treat as a spec violation or P1 (broken workflow), depending on severity.
  • Secrets in context: redact and warn; do not quote.
  • 无Git环境/无基准分支:说明无法验证的内容;仅评审文件本身。
  • 大型技能目录:严格遵循限定的读取顺序;默认不得“读取全部内容”。
  • 缺失引用文件:根据严重程度,将其视为规范违反项或P1问题(工作流中断)。
  • 上下文包含敏感信息:脱敏并发出警告;不得引用。

Examples

示例

  • “Use $reviewing-skills to review
    ./some-skill/
    and provide a weighted grade, spec blockers, and prioritized patch text.”
  • “Use $reviewing-skills to do a forensic/diff-centric review of
    ./some-skill/
    focusing on recent changes.”
  • For a worked example format, see references/example-review.md.
  • “使用$reviewing-skills评审
    ./some-skill/
    ,并提供加权等级、规范阻塞问题及按优先级划分的补丁文本。”
  • “使用$reviewing-skills对
    ./some-skill/
    进行取证/基于差异的评审,重点关注近期变更。”
  • 关于示例格式,可参考references/example-review.md