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skill-scorer技能得分手

Agent Skill

skill-scorer 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

4,235

周安装

173

GitHub Stars

公开资料未说明

下载量

1,356
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:skill-scorer(技能得分手)
来源仓库:https://github.com/dingtom336-gif/skill-scorer
安装命令:
openclaw skills install skill-scorer
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install skill-scorer

简介

skill-scorer 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。

  • 可结合来源仓库、安装命令和原始 README 继续核验具体用法。
  • 安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。
  • 对任何 SKILL.md(或 skill 文件夹)进行质量评估和打分,基于行业最佳实践生成 8 维度 100 分制质检报告。
  • 当用户要求评审、审计、评分、检测、质检任何 skill 时触发。

SKILL.md

name
skill-scorer
description
对任何 SKILL.md(或 skill 文件夹)进行质量评估和打分,基于行业最佳实践,生成 8 维度 100 分制的结构化质检报告,精准定位问题并提供可执行的优化建议。当用户要求评审、审计、评分、检测、质检任何 skill 时使用——哪怕只是说「这个 skill 写得怎么样?」也会触发。也支持:skill质检、skill评分、检测skill。 | Evaluate and score any SKILL.md (or skill folder) against industry best practices. Generates a structured quality report with a 100-point score across 8 dimensions, pinpoints issues, and provides actionable optimization suggestions. Use this skill whenever the user asks to review, audit, evaluate, grade, score, lint, or quality-check a skill — even if they just say 'is this skill any good?' or 'help me improve this skill'. Also triggers on: 'skill review', 'rate my skill'.
version
1.6.0
compatibility
Claude Code, Claude.ai, Cowork, and all SKILL.md-compatible agents
changelog
|

Skill: skill-scorer

Overview

A meta-skill that evaluates the quality of other skills. Given a SKILL.md file (or a complete skill folder), it performs a systematic audit across 8 dimensions, assigns a score out of 100, identifies issues by severity, and generates actionable optimization suggestions.

This skill synthesizes quality criteria from Anthropic's official skill authoring best practices, the Skill Engineering Standard (v1.4.3), and community-tested patterns from production skill ecosystems.

When to Activate

User provides a skill and asks any of:

  • "帮我评分/打分/检测/质检 这个 skill"
  • "review/audit/score/grade/lint this skill"
  • "这个 skill 写得怎么样?" / "is this skill any good?"
  • "帮我优化这个 skill" (evaluate first, then suggest improvements)
  • Provides a SKILL.md and expects quality feedback

Do NOT activate for: creating a new skill from scratch → use skill-creator. This skill is for evaluation, not generation.

Core Workflow

Step 0: Load the Skill Under Test

Determine what the user has provided:

InputAction
Single SKILL.md fileEvaluate that file
Skill folder (with references/)Evaluate all files, cross-reference consistency
URL / GitHub linkFetch and evaluate
Pasted markdown contentTreat as SKILL.md

If the user has not provided a skill → ask: "请提供要评估的 SKILL.md 文件或 skill 文件夹路径。"

Input validation — before proceeding to Step 1, verify the input is actually a skill:

CheckConditionAction
Binary / garbled contentFile is not valid text, or text is unreadable gibberishSTOP. Report: "This file does not appear to be a valid SKILL.md — it contains binary or unreadable content. Please provide a markdown-based skill file." Do NOT attempt to score.
No skill markers at allText is valid but contains zero skill indicators (no YAML frontmatter ---, no markdown headings resembling skill sections, no workflow/instructions)STOP. Report: "This appears to be a {detected_type} file (e.g., Python script, JSON config, plain prose), not a SKILL.md. skill-scorer evaluates SKILL.md files only." Do NOT force-fit 8 dimensions onto non-skill content.
Partial skill structureHas some skill-like elements (e.g., YAML frontmatter exists but body is minimal, or has headings but no workflow)PROCEED with caveats. Evaluate normally, but note in the report header: "⚠️ This file has incomplete skill structure — scores reflect what is present." Score missing sections as 0 in relevant dimensions rather than guessing.

Step 1: Parse Skill Structure

Extract and inventory:

  • YAML frontmatter fields (name, description, version, compatibility)
  • Section headings and their order
  • References to external files (references/, scripts/, assets/)
  • Total line count and estimated token count of SKILL.md body

Step 2: Run 8-Dimension Evaluation

Read references/rubric.md for the complete scoring rubric.

Evaluate the skill across these 8 dimensions (each scored 0-100, then weighted):

#DimensionWeightWhat It Measures
1Metadata & Triggering15%Name clarity, description quality, trigger coverage
2Structure & Architecture15%File organization, section order, progressive disclosure
3Instruction Clarity15%Actionability, conciseness, examples, tone
4Workflow & Logic15%Step completeness, parameter handling, validation
5Error Handling10%Fallbacks, edge cases, failure recovery
6Context Efficiency10%Token budget, redundancy, information density
7Portability & Compatibility10%Self-containment, cross-platform support
8Safety & Robustness10%No injection risk, no hallucination traps, identity lock

Step 3: Identify Issues

For each issue found, classify severity:

SeverityMeaningScore Impact
🔴 CriticalSkill will malfunction or not trigger-10 to -15 per issue
🟡 WarningSkill works but suboptimally-3 to -8 per issue
🟢 SuggestionNice-to-have improvement-1 to -2 per issue

Step 4: Generate Report

Read references/report-template.md for the output format.

The report includes:

  1. Score Card — Overall score + per-dimension breakdown
  2. Issue List — All findings sorted by severity
  3. Top 3 Quick Wins — Highest-impact fixes with before/after examples
  4. Optimization Roadmap — Prioritized improvement plan

Step 5: Offer Follow-Up

After presenting the report, ask:

  • "需要我帮你自动修复这些问题吗?" (auto-fix mode)
  • "需要对某个维度深入分析吗?" (deep-dive mode)
  • "需要生成优化后的 SKILL.md 吗?" (rewrite mode)

Output Rules

  1. Bilingual report — Chinese first, English after, no interleaving. Always output the complete report in Chinese, then a --- separator, then the complete report in English. Never mix languages within a section. Both versions must contain identical scores, issues, and suggestions — only the language differs.
  2. Score must be justified. Every deducted point must trace to a specific issue.
  3. Suggestions must be actionable. Include before/after code snippets, not vague advice.
  4. Be constructive, not destructive. Lead with what the skill does well before listing issues.
  5. ❌ Never inflate scores to be polite — honest assessment helps the user improve.
  6. ❌ Never evaluate based on domain correctness of the skill's content (e.g., whether hotel recommendations are good) — only evaluate skill engineering quality.

References

FilePurposeWhen to read
references/rubric.mdDetailed scoring criteria for all 8 dimensionsStep 2: scoring
references/report-template.mdOutput format and report structureStep 4: generating report
references/anti-patterns.mdCommon skill mistakes and how to detect themStep 3: finding issues

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

71.28%
按下载量换算967

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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