Agent skill

Skill Reviewer

by LeoYeAI in LeoYeAI/openclaw-master-skills

审核/审查 Skill 代码质量的专业工具。当用户说"检查 skill"、"审核 skill"、"review {名称} skill"、"skill 写得怎么样"、"帮我看看这个 skill 有什么问题"时使用。依据 Anthropic 官方指南进行结构验证、YAML 前置信息检查、描述质量评估、指令完整性审查,并输出详细的问题报告和改进建议。

MITAuto-check passed

Install Skill Reviewer

skills CLI
$ npx skills add LeoYeAI/openclaw-master-skills --skill skill-reviewer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install LeoYeAI/openclaw-master-skills skill-reviewer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/skill-reviewer-2 .claude/skills/skill-reviewer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
skill-reviewer
GitHub stars
2.2k
Token cost
~2.3k tokens
SKILL.md length
312 words
Files
5 (incl. references)
Skills in repo
1,235
Repo updated
First seen
Licence
MIT

At a glance

审核/审查 Skill 代码质量的专业工具。当用户说"检查 skill"、"审核 skill"、"review {名称} skill"、"skill 写得怎么样"、"帮我看看这个 skill 有什么问题"时使用。依据 Anthropic 官方指南进行结构验证、YAML 前置信息检查、描述质量评估、指令完整性审查,并输出详细的问题报告和改进建议。

  • Works in 8 steps: 结构验证 - 检查文件夹结构、文件命名规范 → YAML 审核 - 验证前置信息格式和必填字段 → 描述评估 - 检查触发条件是否清晰 → …
  • SKILL.md covers 核心职责, 工作流程, 评分总结 and 常见缺陷, plus 5 more sections
  • Calls npx

What it does

Skill Reviewer is an agent skill from LeoYeAI/openclaw-master-skills. 审核/审查 Skill 代码质量的专业工具。当用户说"检查 skill"、"审核 skill"、"review {名称} skill"、"skill 写得怎么样"、"帮我看看这个 skill 有什么问题"时使用。依据 Anthropic 官方指南进行结构验证、YAML 前置信息检查、描述质量评估、指令完整性审查,并输出详细的问题报告和改进建议。

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `_meta.json`, `references/anthropic-skills-development-guide.md` and `references/checklist.md`).

The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.

Example prompts

  • “检查 skill”
  • “审核 skill”
  • “review {名称} skill”
  • “/skill-reviewer”

Requirements

  • Python 3

Workflow steps

8 steps, taken from the first numbered list in SKILL.md.

  1. 结构验证 - 检查文件夹结构、文件命名规范
  2. YAML 审核 - 验证前置信息格式和必填字段
  3. 描述评估 - 检查触发条件是否清晰
  4. 组织评分 - 评估技能是否按任务组织、常见操作优先
  5. 指令审查 - 评估主体指令的质量和完整性
  6. 示例质量 - 评估示例密度和可执行性
  7. Tips 评分 - 评估技巧部分的质量和价值
  8. 最佳实践 - 对照官方指南检查合规性

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • npx

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use npx, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Skill Reviewer loads about 2.3k tokens when it runs, and up to ~15k if it reads all its reference files. Until then it costs about 47 tokens; SKILL.md has 312 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~47
When it runs · the whole SKILL.md, loaded when a task matches
~2.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~15k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 312 words, ~2,261 tokens.

Download SKILL.mdSave it as .claude/skills/skill-reviewer/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
skill-reviewer
description
审核/审查 Skill 代码质量的专业工具。当用户说"检查 skill"、"审核 skill"、"review {名称} skill"、"skill 写得怎么样"、"帮我看看这个 skill 有什么问题"时使用。依据 Anthropic 官方指南进行结构验证、YAML 前置信息检查、描述质量评估、指令完整性审查,并输出详细的问题报告和改进建议。

Skill Reviewer

说明:这是一个Skill审核工具,用于审核其他技能的质量。文档中包含的"错误示例"(如无效 YAML、错误命名等)仅用于教学演示,展示不应该怎么写。不存在任何恶意或混淆代码。

依据 Anthropic 官方 Skills 开发指南,对技能进行全面审核。提供结构化审核框架、评分系统、缺陷检查清单和改进建议。

核心职责

  1. 结构验证 - 检查文件夹结构、文件命名规范

    示例:ls skills/my-skill/ 检查 SKILL.md 是否存在

  2. YAML 审核 - 验证前置信息格式和必填字段

    示例:head -20 SKILL.md 检查 name/description

  3. 描述评估 - 检查触发条件是否清晰

    示例:description 应包含"当用户...时"触发条件

  4. 组织评分 - 评估技能是否按任务组织、常见操作优先

    示例:"## 编码和解码" ✅ vs "## 理论" ❌

  5. 指令审查 - 评估主体指令的质量和完整性

    示例:是否有工作流程、示例、错误处理

  6. 示例质量 - 评估示例密度和可执行性

    示例:每 5-30 行 1 个代码块为最佳密度

  7. Tips 评分 - 评估技巧部分的质量和价值

    示例:5-10 条非显而易见的实用技巧

  8. 最佳实践 - 对照官方指南检查合规性

    示例:检查是否按任务组织而非抽象概念

工作流程

第 1 步:接收审核请求

当用户请求审核 skill 时:

  1. 确认 skill 文件夹路径或读取 SKILL.md 内容
  2. 判断审核严格程度:

严格模式(必须读取完整版官方指南): 当用户表达以下意图时,必须先读取 references/anthropic-skills-development-guide.md 完整版指南:

  • 明确要求"严格检查"、"仔细审核"、"全面审查"
  • 提到"高质量要求"、"生产级别"、"发布前检查"
  • 表达"不想有任何遗漏"、"按最高标准"
  • 用于团队/组织/公司项目
  • 准备公开发布或分享给他人

常规模式:

  • 优先读取 references/checklist.md(快速检查清单)
  • 遇到疑问或边缘案例时读取完整版官方指南

第 2 步:结构检查

文件夹结构验证:

text
[ ] 技能文件夹使用 kebab-case 命名(如 my-skill)
[ ] SKILL.md 存在且命名精确(区分大小写)
[ ] 无 README.md 在技能文件夹内
[ ] 可选文件夹(如存在)命名规范

可选文件夹命名规范(如存在):

  • scripts/ - 全小写复数,无空格/下划线
  • references/ - 全小写复数,无空格/下划线
  • assets/ - 全小写复数,无空格/下划线

❌ 错误示例: Scripts、script、scripts_backup、References、refs、Assets、asset_files

示例:检查文件夹结构

bash
# 查看技能文件夹结构
ls -la skills/china-holidays/

预期输出:

text
skills/china-holidays/
├── SKILL.md          # ✅ 正确:精确命名
└── references/       # ✅ 正确:全小写复数
    └── calendar-guide.md

评分:__/4


第 3 步:YAML 前置信息检查

必填字段验证:

text
[ ] name 字段存在,kebab-case,无空格大写
[ ] description 字段存在,非空
[ ] YAML 分隔符完整(--- 开头和结尾)
[ ] 无 XML 标签(< >)
[ ] name 不以 claude/anthropic 开头

示例:验证 YAML 前置信息

bash
# 读取前 20 行检查 YAML
head -20 skills/china-holidays/SKILL.md

预期输出(正确示例):

yaml
---
name: china-holidays
description: 获取中国国家法定节假日安排。当用户询问"放假安排"、"节假日"时使用。
---

错误示例(应该拒绝):

yaml
name: ChinaHolidays        # ❌ 大写,应该 kebab-case
name: claude-scheduler     # ❌ 以 claude 开头
description: ""            # ❌ 空描述
<skill>                    # ❌ 包含 XML 标签

Description 质量评分(满分 8 分):

text
[2] 开头说明做什么(主动动词)
    好:"分析 Figma 设计文件并生成开发者交接文档"
    差:"这是关于 Figma 的技能"

[2] 包含触发条件("当用户...时")
    好:"当用户上传 .fig 文件或询问设计规格时使用"
    差:无触发条件

[2] 具体范围(提及具体工具、操作或场景)
    好:"Figma 设计文件、.fig、组件文档、设计交接"
    差:"帮助处理项目"

[2] 合理长度(50-200 字符,不超过 1024)
    太短:无搜索价值
    太长:被截断

评分:__/8


第 4 步:组织评分

按任务/场景组织(而非按抽象概念):

text
[2] 按任务/操作组织章节
    好:"## 编码和解码" → "## 检查字符" → "## 转换格式"
    差:"## 理论" → "## 类型" → "## 高级"

[2] 常见操作优先
    好:基础用法 → 变体 → 高级 → 边界情况
    差:配置说明 → 理论背景 → 最后才是基础用法

[1] 章节自包含(可独立使用)

[1] 深度一致(不混用 h2 与 h4 随机跳转)

评分:__/6


第 5 步:主体指令检查

必须包含:

text
[ ] 清晰的工作流程或步骤说明
[ ] 至少一个使用示例
[ ] 错误处理或故障排查指南

推荐包含(不扣分):

text
[ ] 预期输出说明
[ ] 多个场景示例
[ ] 参考文档链接
[ ] "使用场景" 或 "When to Use" 部分

评分:__/6


第 6 步:示例质量评估

示例密度计算:

text
总行数:___
代码块数量:___
密度:每 ___ 行 1 个代码块

参考目标:每 5-30 行 1 个代码块
< 5 行/块:可能过于碎片化(短命令集或多命令速查除外)
> 40 行/块:需要更多示例

示例密度评分:

text
[3] 密度在 5-30 行/块范围内
[2] 密度略低(30-40 行/块)或略高(3-5 行/块)
[0] 密度严重不足(>40 行/块)或过高(<3 行/块)

每个示例质量评分(0-3 分):

text
[ ] 语言标签正确(```bash, ```python 等)
[ ] 语法正确,命令可执行
[ ] 展示了预期输出或结果说明
[ ] 使用真实值(非 foo/bar/baz)
[ ] 无占位符(TODO, FIXME, xxx)
[ ] 自包含或有设置说明

0 分:broken 或有误导性
1 分:可用但简陋(无输出/上下文)
2 分:良好(正确,有输出或说明)
3 分:优秀(可直接复制,真实,覆盖边界)

示例质量得分 = 所有示例平均分 × 密度分 / 3 = __/9


第 7 步:可执行性评估

核心问题:Agent 能否按照这些指令产生正确结果?

text
[3] 使用祈使句("运行 X"、"创建 Y")
    非:"可以考虑..."或"建议..."或"You might..."

[3] 步骤顺序合理(前置条件在行动之前)

[2] 错误处理(说明失败时怎么做)

[2] 输出/结果描述(如何验证成功)

评分:__/10


第 8 步:渐进式披露检查
text
[ ] SKILL.md 控制在 500 行以内
    500-600 行:给出提醒,不扣分
    超过 600 行:扣分
[ ] 详细文档是否在 `references/` 中
[ ] 大文件(>300 行)有目录或结构说明

评分:__/3


第 9 步:Tips 评分

技巧部分质量评估:

text
[2] 5-10 条技巧
    少于 5 条:覆盖不足
    多于 10 条:可能不够精炼

[2] 技巧非显而易见
    好:"Makefile 头号陷阱:缩进必须用 Tab,不能用空格"
    差:"确保测试你的代码"

[2] 技巧具体可执行
    好:"使用 flock 防止 cron 任务重叠执行"
    差:"小心并发执行"

[1] 技巧不矛盾主体内容

[1] 技巧覆盖特定主题的陷阱/踩坑点

评分:__/8


(可选)Metadata 参考

仅当 skill 包含 metadata 字段时检查,仅供参考,不计分

text
[ ] emoji 与技能主题相关
[ ] requires.anyBins 列出技能实际使用的工具(非 generic 如 bash)
[ ] os 数组准确(不包含不支持的平台)
[ ] JSON 格式有效

评分总结

text
SKILL 审核评分卡
═══════════════════════════════════════
技能名称:{name}
审核者:{agent/human}
日期:{date}
审核模式:{严格/常规}

类别                  得分     满分
─────────────────────────────────────
结构检查              __       4
Description 质量       __       8
组织评分              __       6
主体指令              __       6
示例质量              __       9
可执行性              __      10
渐进式披露            __       3
Tips 评分             __       8
─────────────────────────────────────
总分                  __      54

转换为百分制:总分 × 1.85 = ___/100

评级:
  85+  优秀 — 可直接发布
  70-84 良好 — 需要小改进
  50-69 一般 — 需要明显改进
  < 50  较差 — 需要重大修改

结论:{PUBLISH / REVISE / REWORK}

常见缺陷

严重缺陷(阻止发布)
text
缺陷:YAML 前置信息无效
检测:YAML 解析错误、缺少必填字段
修复:验证 YAML 格式,确保 name/description 都存在

缺陷:代码示例损坏
检测:语法错误、未定义变量、错误参数
修复:在干净环境中测试每个命令

缺陷:工具要求不匹配(如存在 metadata)
检测:requires 列出内容未在内容中使用
修复:grep 内容提取命令名,更新 requires 匹配

缺陷:误导性描述
检测:描述承诺的内容实际未覆盖
修复:使描述与实际内容一致,或补充缺失内容
主要缺陷(发布前应该修复)
text
缺陷:缺少"使用场景"部分
影响:Agent 不知道何时激活技能
修复:添加 4-8 条触发场景说明

缺陷:大段文字无示例
检测:任何超过 10 行无代码块的章节
修复:为每个概念添加具体示例

缺陷:示例缺少语言标签
检测:\`\`\` 后无语言标识符
修复:为每个代码块添加 bash/python/javascript/yaml 等

缺陷:缺少 Tips/技巧部分
影响:缺少使技能有价值的经验总结
修复:添加 5-10 条非显而易见的实用技巧

缺陷:按抽象概念组织
检测:章节名为"理论"、"概述"、"背景"、"介绍"
修复:按任务/操作重组:用户要做什么
次要缺陷(建议修复)
text
缺陷:占位符值
检测:foo, bar, baz, example.com, TODO, FIXME
修复:替换为真实值

缺陷:格式不一致
检测:混合标题级别、代码块样式不一致
修复:统一标题层次和格式

缺陷:缺少交叉引用
检测:提到其他技能覆盖的工具/概念但未引用
修复:添加"参见 X 技能了解更多"注释

缺陷:过时的命令
检测:旧语法或已弃用工具
修复:更新为当前工具版本和语法

快速审核模板

当不需要完整评分时的快速审核:

markdown
## 快速审核:{skill-name}

**结构**:[通过/问题:...]
**Description**:[强/弱:原因]
**示例**:[X 个代码块,共 Y 行 — 密度 正常/低/高]
**可执行性**:[Agent 可以/不可以 遵循这些指令,因为...]
**首要缺陷**:[最应该修复的单一问题]
**评分**:__/100
**结论**:[PUBLISH / REVISE / REWORK]

审核工作流

发布前自查
bash
# 1. 验证 YAML 前置信息
head -20 skills/my-skill/SKILL.md
# 目视确认 YAML 有效

# 2. 统计代码块数量
grep -c '```' skills/my-skill/SKILL.md
# 总行数除以这个数得密度

# 3. 检查占位符
grep -n -i 'todo\|fixme\|xxx\|foo\|bar\|baz' skills/my-skill/SKILL.md

# 4. 检查缺失语言标签
grep -n '^```$' skills/my-skill/SKILL.md
# 每个代码块都应该有语言标签

# 5. 验证工具要求匹配内容(如存在 metadata)
# 提取 requires,然后 grep 内容检查每个工具

# 6. 测试命令(抽样 3-5 个)
# 在干净 shell 中运行验证

# 7. 运行评分卡
# 目标:良好 35+,优秀 45+

示例:完整审核报告输出

markdown
## 审核报告:china-holidays

**结构**:✅ 通过 - kebab-case 命名,SKILL.md 存在
**Description**:✅ 强 - 包含触发条件和具体场景
**示例**:18 个代码块,524 行 — 密度 正常 (29 行/块)
**可执行性**:✅ 可以遵循 - 9 步清晰工作流程

**评分总结**:
─────────────────────────────────────
结构检查              4        4
Description 质量       8        8
组织评分              6        6
主体指令              6        6
示例质量              7        9
可执行性              10       10
渐进式披露            3        3
Tips 评分             8        8
─────────────────────────────────────
总分                  52       54
百分制:96/100

**结论**:PUBLISH - 可直接发布
审核他人技能
bash
# 安装技能(如适用)
npx molthub@latest install skill-name

# 阅读内容
cat skills/skill-name/SKILL.md

# 运行快速审核模板
# 如分数 < 25,考虑卸载并寻找替代

参考文件

按使用场景分层:

文件用途何时读取
references/checklist.md快速检查清单常规审核流程
references/official-guide-summary.md精简指南快速查阅常用规范
references/anthropic-skills-development-guide.md完整版官方指南严格模式下必须读取;常规模式下遇到疑问时查阅

严格模式触发条件(满足任一即触发):

  • 用户明确要求"严格检查"、"全面审查"、"仔细审核"
  • 提到"高质量"、"生产级别"、"发布前"
  • 表达"不想有遗漏"、"按最高标准"
  • 用于团队/组织/公司项目
  • 准备公开发布或分享

审核时先判断模式:严格模式必须先读取完整版指南再进行审核;常规模式按需查阅。


示例

示例 1:完整审核

用户说: "帮我审核一下这个 skill,路径在 ./skills/china-holidays"

操作:

  1. 读取 ./skills/china-holidays/SKILL.md
  2. 判断审核模式(默认常规)
  3. 按照评分卡逐项检查
  4. 生成审核报告
  5. 解读结果并提供改进建议

结果: 输出完整审核报告,包含问题列表、修改建议和最终评分

示例 2:严格模式审核

用户说: "这个 skill 准备发布到团队内部使用,需要严格检查,不能有任何问题"

操作:

  1. 触发严格模式
  2. 必须先读取 anthropic-skills-development-guide.md
  3. 对照官方指南逐项严格检查
  4. 输出详细报告,确保无遗漏
示例 3:快速检查 description

用户说: "这个 skill 的 description 写得怎么样?" + 附上内容

操作:

  1. 聚焦 description 字段分析
  2. 使用 8 分评分标准
  3. 提供改进建议

结果: 指出问题并提供修改建议


技巧

  • Description 最重要 — 它占实际影响力的 40% 以上。完美的 skill 配上糟糕的 description 也不会被找到。

  • 先数代码块 — 少于 8 个代码块的 skill 几乎总是过于抽象而无用。

  • 在干净环境测试 3-5 个命令 — 如超过 1 个失败,说明 skill 发布前未测试。

  • 按任务组织 vs 按概念组织 — 这是最关键的结构质量差异。好技能回答"如何做 X",坏技能解释"X 是什么"。

  • 有好 Tips 但示例弱的 skill,比有好示例但没 Tips 的更有价值 — Tips 编码了示例无法传达的专业知识。

  • 检查 requires 与实际使用是否匹配 — 常见缺陷是列出 bash(所有都有)而不是实际工具如 docker、curl、jq。

  • 过短的 skill(<150 行)通常不值得发布 — 它们提供的价值不如快速网络搜索。如果 skill 太短,可能是更大 skill 的一个章节。

  • 最佳标准:你自己会收藏使用吗 — 如果你自己不会用,就不要发布。

© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (references) in skills/skill-reviewer-2 of LeoYeAI/openclaw-master-skills.

  • SKILL.md
  • _meta.json
  • references/anthropic-skills-development-guide.md
  • references/checklist.md
  • references/official-guide-summary.md

Open the folder on GitHubat commit e5199b5

Compare with similar skills

Skill Reviewer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Skill Reviewer compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Skill Reviewer this skillLeoYeAI/openclaw-master-skills2.2k—~2.3kAutomated safety check: PassMIT
Review Pending PR Reviewsnrwl/nx29k—~3.9kAutomated safety check: PassMIT
Reviewthedaviddias/Front-End-Checklist74k—~556Automated safety check: PassMIT
ReviewClickHouse/ClickHouse50k—~8kAutomated safety check: NotesApache-2.0
Docling Pull Request Reviewdocling-project/docling69k—~1kAutomated safety check: PassMIT
Pre-Landing PR Reviewgarrytan/gstack136k—~19kAutomated safety check: NotesMIT

Similar skills

  • Review, grill, edit, and post pending PR review drafts saved by /review-pr (or its batch/cron runners).

    29k GitHub stars~3.9k tokensUpdated yesterday
    DevelopmentAuto-check passed
  • Review

    thedaviddias/Front-End-Checklist

    A skill your agent uses when applies to product pages, local business pages, recipes, apps, books, and any page that aggregates user reviews.

    74k GitHub stars~556 tokensUpdated 4 days ago
    Marketing & SEOAuto-check passed
  • Review

    ClickHouse/ClickHouse

    Review a ClickHouse Pull Request for correctness, safety, performance, and compliance.

    50k GitHub stars~8k tokensUpdated yesterday
    DatabasesAuto-check: notes
  • Docling Pull Request Review

    docling-project/docling

    Reviews or re-reviews a Docling pull request in fixed stages, with findings that can be reproduced and an explicit record of every check that was run.

    69k GitHub stars~1k tokensUpdated yesterday
    DevelopmentAuto-check passed
  • Pre-Landing PR Review

    garrytan/gstack

    Reviews your diff against the base branch before merge, looking for SQL safety problems, LLM trust boundary violations, conditional side effects and other structural issues.

    136k GitHub stars~19k tokensUpdated yesterday
    DevelopmentAuto-check: notes
  • Code Review Checklist

    shareAI-lab/learn-claude-code

    Reviews code against a five-part checklist covering security, correctness, performance, maintainability and testing, and reports findings in a fixed format.

    78k GitHub starsUsed in 4 repos~1.1k tokens
    DevelopmentAuto-check passed

More from LeoYeAI/openclaw-master-skills

All 1,235 skills in this repo
  • DevOps Pipeline Management

    LeoYeAI/openclaw-master-skills

    Manages pipelines on a DevOps quality and efficiency platform through its OpenAPI: list workspaces and templates, create, update, run and cancel pipelines, and read run records.

    2.2k GitHub stars~4.2k tokensUpdated 2 mo ago
    Auto-check: notes
  • Feishu Document Collaboration

    LeoYeAI/openclaw-master-skills

    Patches OpenClaw's Feishu extension so an edited document triggers an isolated agent session that reads the doc and replies inline, turning it into a live chat space.

    2.2k GitHub stars~2k tokensUpdated 2 mo ago
    Auto-check passed
  • Files Memory System

    LeoYeAI/openclaw-master-skills

    Multi-context memory management system for OpenClaw agents with group-isolated storage, global shared memory, workspace organization, and group-specific skills isolation.

    2.2k GitHub stars~3.8k tokensUpdated 2 mo ago
    Auto-check passed
  • GEO-Claw AI Visibility Agent

    LeoYeAI/openclaw-master-skills

    Runs a brand's AI-search visibility work end to end: diagnosing how AI platforms represent it, repositioning it, producing AI-optimized content and monitoring ongoing mentions.

    2.2k GitHub stars~4.7k tokensUpdated 2 mo ago
    Auto-check passed
  • Google Workspace CLI

    LeoYeAI/openclaw-master-skills

    Installs and authenticates the gws CLI, then automates Gmail, Drive, Sheets, Calendar, Docs, Chat and Tasks with ready-made recipes, persona bundles and security audits.

    2.2k GitHub stars~2.6k tokensUpdated 2 mo ago
    Auto-check: notes
  • HealthFit Health Advisors

    LeoYeAI/openclaw-master-skills

    Runs four advisor roles, a fitness coach, nutritionist, data analyst and TCM practitioner, to build a health profile and track workouts, diet and wellness over time.

    2.2k GitHub stars~4.4k tokensUpdated 2 mo ago
    Auto-check passed

Questions about Skill Reviewer

What does Skill Reviewer do?

审核/审查 Skill 代码质量的专业工具。当用户说"检查 skill"、"审核 skill"、"review {名称} skill"、"skill 写得怎么样"、"帮我看看这个 skill 有什么问题"时使用。依据 Anthropic 官方指南进行结构验证、YAML 前置信息检查、描述质量评估、指令完整性审查,并输出详细的问题报告和改进建议。. Skill Reviewer is an agent skill from LeoYeAI/openclaw-master-skills.

How do I install Skill Reviewer in Claude Code?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill skill-reviewer -a claude-code`. Or copy the skill folder (skills/skill-reviewer-2 in LeoYeAI/openclaw-master-skills) into .claude/skills/skill-reviewer in your project. Claude Code loads it when a task matches its description.

How do I install Skill Reviewer in Codex?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill skill-reviewer -a codex`. Or copy the skill folder (skills/skill-reviewer-2 in LeoYeAI/openclaw-master-skills) into .agents/skills/skill-reviewer in your project. Codex loads it when a task matches its description.

Can I use Skill Reviewer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill skill-reviewer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/skill-reviewer, .gemini/skills/skill-reviewer, .github/skills/skill-reviewer and .opencode/skills/skill-reviewer in your project.

What does Skill Reviewer need to run?

Going by SKILL.md and its folder, Skill Reviewer needs the command-line tools its instructions call (npx). Our summary lists: Python 3.

Does Skill Reviewer access the network?

SKILL.md contains no URLs. Its commands use npx, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Skill Reviewer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Skill Reviewer use?

Skill Reviewer is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Skill Reviewer use?

About 2.3k tokens (SKILL.md is roughly 9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 13k tokens, read only when the agent opens those files.

What are the alternatives to Skill Reviewer?

Skills that share tags, products or a category with Skill Reviewer: Review Pending PR Reviews (nrwl/nx, 29k stars), Review (thedaviddias/Front-End-Checklist, 74k stars), Review (ClickHouse/ClickHouse, 50k stars) and Docling Pull Request Review (docling-project/docling, 69k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Skill Reviewer?

LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,161 GitHub stars. The repository holds 1,235 skills in this directory. The repository was last updated on July 20, 2026.

Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.