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Skill scorer

Skill AndrewNgGirl/SkillLens/skills/skill-scorer

Open-source self-hosted web tool for evaluating Agent Skills with rubric scores, Deep Review, and improvement suggestions.

Install
npx -y skills add AndrewNgGirl/SkillLens --skill skill-scorer

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What its author says it does

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Evaluates Agent Skills (Cursor / Claude / OpenClaw compatible) and produces a quantitative, rubric-based score with actionable improvement suggestions. Use when the user asks to review, rate, audit, grade, lint, or improve a SKILL.md file, a skill folder, or a skill archive, or says things like "给这个 skill 打分", "评估一下 skill 质量", "audit this skill", "rate my agent skill".

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

9.0 KB, as published. Nobody here has run it

skill-scorer

一个"评测 Skill 的 Skill"。接收任意 Agent Skill 的源文件，必须依据本仓库的官方评分入口和 rubric/rubric.yaml 给出 5 大支柱的 100 分制评分、等级、证据引用与改进建议。Rubric 内置三类型差异化（atomic / pipeline / composite），子维度数随 skill 结构自动启用，由 applies_to 字段控制。同时兼容 Cursor / Claude / OpenClaw 三套规范。

When to use

  • 用户提供 SKILL.md / skill 文件夹 / .zip / GitHub URL，并请求评分、审计或改进建议。
  • 用户询问"我这个 skill 写得怎么样"、"怎么提升我的 skill 质量"、"帮我对齐官方最佳实践"。
  • 不适用于：评价非 Skill 类文档（普通 README / 博客 / prompt 模板）。

Code Agent Quick Start

如果你是 Cursor、WorkBuddy、Hermes、小龙虾或类似 code agent，先读 USAGE.md。

推荐先运行 CLI 向导，让用户选择通用评测或金融专家版；如果选择金融专家版，向导会继续确认金融子场景，并输出后续官方命令：

python3 skills/skill-scorer/scripts/score.py --agent-wizard <path-to-skill-zip-dir-or-SKILL.md>

规则分预览：

python3 skills/skill-scorer/scripts/score.py <path-to-skill-zip-dir-or-SKILL.md>

完整 agent-side Deep Review（使用 code agent 自己的模型套餐，不消耗 SkillLens 服务端 key）：

python3 skills/skill-scorer/scripts/score.py --agent-prompt <path-to-skill-zip-dir-or-SKILL.md> > agent-deep-review-prompt.md
# 将 agent-deep-review-prompt.md 完整交给当前 code agent 的模型，保存严格 JSON 为 agent-llm-results.json
python3 skills/skill-scorer/scripts/score.py --llm-results agent-llm-results.json <path-to-skill-zip-dir-or-SKILL.md>

不得临时生成自定义评分脚本替代官方 CLI；最终分数必须来自最后一步官方 CLI 输出。

金融专家版（可选）应优先通过 --agent-wizard 选择；手动执行时，必须在 --agent-prompt 和 --llm-results 两步都加入相同的 --domain finance --scenario <scenario-id>。支持的场景详见 USAGE.md。

Inputs

  • 一个 SKILL.md 文本，或
  • 一个 skill 目录（含 scripts/ references/ assets/ 等），或
  • 一个 .zip 打包的 skill，或
  • 一个指向 skill 仓库/子目录的 GitHub URL（Web 工具侧支持）。

Outputs

{
  "spec": "claude | openclaw",
  "language": "zh | en",
  "score": 0-100,
  "grade": "S | A | B | C | D",
  "pillars": [
    {
      "id": "business_value",
      "score": 0-25,
      "dimensions": [
        {
          "id": "...",
          "checks": [
            {
              "id": "...",
              "status": "pass|partial|fail|n_a",
              "evidence":    "<primary-language alias>",
              "evidence_zh": "中文现状",
              "evidence_en": "English diagnosis",
              "fix":    "<primary-language alias>",
              "fix_zh": "中文改法",
              "fix_en": "English fix"
            }
          ]
        }
      ]
    }
  ],
  "bonus": 0-5,
  "suggestions": [
    {
      "title": "Top 改进项",
      "title_zh": "中文 Top 改进项",
      "title_en": "English Top Improvement",
      "why":    "现状",
      "why_zh": "中文现状",
      "why_en": "English why",
      "how":    "改法",
      "how_zh": "中文改法",
      "how_en": "English how"
    }
  ],
  "deepReviewCertificate": {
    "status": "verified"
  }
}

evidence_zh + evidence_en (and fix_zh + fix_en, why_zh + why_en, how_zh + how_en, title_zh + title_en) are the canonical bilingual fields ≥ engineVersion 0.4.1. The unsuffixed evidence / fix / why / how / title are preserved as back-compat aliases pointing at the primary language so older readers keep working. The HTML report's ZH/EN toggle uses the suffixed fields to switch body content; falls back to the bare field when the JSON predates the bilingual schema.

Workflow

  1. Locate SkillLens root：先定位包含 skills/skill-scorer/rubric/rubric.yaml 的 SkillLens 仓库根目录。

  2. Run official scorer：运行官方 CLI，不得临时生成替代评分脚本：

    python3 skills/skill-scorer/scripts/score.py <path-to-skill-zip-dir-or-SKILL.md>

  3. Choose review mode：优先运行 --agent-wizard。如手动执行，必须确认是否启用领域专家版；当前 MVP 支持 finance，并必须确认具体 --scenario。

  4. Agent-side Deep Review when requested：如需完整深度评测，必须先运行 --agent-prompt 生成官方提示词，用当前 code agent 的模型返回严格 JSON，再运行 --llm-results 合并。领域专家版必须在两步命令都带上相同的 --domain / --scenario。

  5. Use official JSON only：总分、等级、pillar/dimension/check 分数必须来自官方 CLI 最终 JSON 输出，不能由 Agent 自己重算或补满。

  6. Verify certificate：完整 Deep Review 必须包含 deepReviewCertificate.status="verified"；金融专家版还必须包含 domainExpert 和 deepReviewCertificate.domain；没有证书只能称为规则分预览或非官方结果。

  7. Render：按用户阅读语言（zh / en）从 JSON 取双语字段（evidence_zh + evidence_en, fix_zh + fix_en, why_zh + why_en, how_zh + how_en）渲染报告；Top 改进项必须来自 JSON 的 suggestions，旧版单语 JSON 可回退到 evidence / fix / why / how。

Official Tool Contract

  • MUST call skills/skill-scorer/scripts/score.py for local tool use, or call the deployed SkillLens Web/API endpoint when the user explicitly提供该服务地址。
  • SHOULD start with --agent-wizard for agent-side Deep Review so the user explicitly chooses general vs. finance expert review.
  • MUST use the official --agent-prompt → model JSON → --llm-results flow for agent-side Deep Review.
  • MUST ask before enabling domain expert review when not using the wizard; for finance, pass the same --domain finance --scenario <scenario-id> in prompt generation and merge.
  • MUST NOT paste or synthesize a new python3 <<'PYEOF' ... scoring script to replace the official scorer.
  • MUST NOT claim "全面检测"、"Deep Review 完成"、"43 项全部通过" 或 "100/100" unless those exact values appear in official SkillLens output.
  • MUST NOT call a result official full Deep Review unless deepReviewCertificate.status is exactly verified.
  • MUST preserve llmComplete=false / llmCoverage in the rendered report. If LLM checks are skipped, say so clearly.
  • MUST include the scoring source in every report, for example: source: official SkillLens CLI or source: SkillLens Web Deep Review.
  • MUST treat rubric/rubric.yaml as read-only scoring data. Do not alter weights, thresholds, or pass/partial/fail mapping during evaluation.

Guardrails

  • 规则分必须确定性且 跨语言一致（TS 前端与 Python CLI 行为等价）。
  • LLM 评审仅用于 type: llm 的细则，不得覆盖或改写规则分结果。
  • 报告语言始终跟随被测 skill 的主语言，除非用户在 Web 端手动切换。
  • 不在报告中回显原 skill 中可能的密钥/凭证字符串。
  • 如果无法运行官方 CLI 或访问官方 Web/API，必须停止并说明原因；不得退回到自制评分器。

Files

  • rubric/rubric.yaml — 评分细则（Web 端与 CLI 共用的单一事实源）
  • domains/finance/rubric.yaml — 金融专家版评分细则（通用分之外的附加专家报告）
  • scripts/score.py — 官方本地 CLI 打分脚本（规则分预览；不会伪造 LLM Deep Review）
  • USAGE.md — 给 Cursor / WorkBuddy / Hermes / 小龙虾等 code agent 的官方调用契约
  • references/best-practices.md — Skill 写作最佳实践（供 LLM few-shot 与人类阅读）

Report Rendering Rules

Render the official JSON into a concise report. Do not use a fixed sample score. Use this shape:

# SkillLens Report

source: official SkillLens CLI | SkillLens Web Deep Review
mode: rule-only preview | full deep review
llmComplete: true | false

**Total**: <score from JSON> / 100 · **Grade**: <grade from JSON>

## Pillars
| Pillar | Score | LLM coverage |
|---|---:|---:|
| <pillar.name_zh/name_en> | <pillar.score>/<pillar.weight> | <evaluated>/<total> |

## Top Improvements
1. <suggestion.title from JSON>
   - 现状/Why: <suggestion.why>
   - 改法/How: <suggestion.how>

If the CLI output says llmComplete=false, explicitly call the result a rule-only preview. Never upgrade it to a full deep review.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.