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Agent Skill · 研究检索

skill-scorer(技能得分手)

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。

skill-scorer 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为调用外部服务;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 23,568。

总安装 4,235周安装 173GitHub Stars 公开资料未说明下载 1,356核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

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

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

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。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install skill-scorer;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要调用外部服务,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

skill-scorer 怎么安装?

在终端执行 openclaw skills install skill-scorer。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

skill-scorer 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

skill-scorer 需要什么权限?

权限级别是调用外部服务。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。

skill-scorer 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

skill-scorer 用的人多吗?

公开数据是累计安装 4,235 次,近一周安装 173 次,下载 1,356 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

skill-scorer 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
skill-scorer

技能得分手 · 本页

4,235—调用外部服务MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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

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

调用外部服务

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。目前只有一个公开来源,建议同时核对源仓库。

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