OpenClaw/clawhub-security-signals huggingface dataset:
Skill: dinghaibin/date-tool (version 1.0.0)

--- name: date-tool description: Display and format date and time values from the command line. Use when you need to get the current date, format timestamps, or calculate date differences. --- # Date Tool - Date and Time Utilities Display current date/time in various formats, convert between timezones, and perform date arithmetic calculations. ## Quick Start ```bash date-tool +"%Y-%m-%d %H:%M:%S" ``` ## Features - Display current date/time in custom formats - Convert between timezones - Calculate date differences - Parse timestamps from various formats ## Examples ```bash # Current date in ISO format date-tool +"%Y-%m-%d" # Current time in 24-hour format date-tool +"%H:%M:%S" # Unix timestamp date-tool +"%s" ```

path: scripts/date.py
content: #!/usr/bin/env python3
"""Date Tool - Show date/time."""
import argparse, datetime
parser = argparse.ArgumentParser()
parser.add_argument('format', nargs='?', default='%Y-%m-%d %H:%M:%S')
args = parser.parse_args()
print(datetime.datetime.now().strftime(args.format))


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: pingjiang/seap-shopping (version 1.0.0)

--- name: seap-shopping version: 1.0.0 trigger: "用户触发购买、帮我买、预约购买等厂家" description: 用户在场/不在场购买skill --- # SEAP Shopping Skill 这是一个用于处理购物需求的 skill，支持用户在场购买和不在场预约购买两种场景。 ## 触发条件 当用户对话中包含"买"字时，自动触发此 skill。 ## 配置文件 配置文件位于 `seap.config.json`，包含以下配置项： ```json { "authCode": "your_auth_code_here", "secretToken": "your_secret_token_here", "address": "your_delivery_address_here" } ``` - **authCode**: 用于调用云侧接口传输的凭证 - **secretToken**: 当用户为不在场购买时，需要将支付token存储，用于后续支付时传入 - **address**: 用户购买后的收货地址 ## 依赖工具 依赖 `scripts/seap-cli.js` 执行相关命令。 ## 工作流程 ### 场景一：用户在场购买 1. 用户发送对话，例如"我想买一瓶红酒" 2. 提取用户意图关键词原文为"queryGoodsIntention" 3. 执行命令： ```bash node scripts seap-cli search --sessionId=${openclaw当前sessionId} --intent=${queryGoodsIntention} --format md ``` 4. 将查询后的 markdown 通过对话信息返回商品数据并按顺序标号，供用户进行选择 5. 用户对话确认指定序号商品后，读取 `${openclaw当前sessionId}.json` 文件对应序号的商品，获取商品 "skuId" 6. 若获取到 skuId 则执行以下命令进行购买，否则返回商品不存在提示用户重新选择： ```bash node scripts seap-cli aipay --sessionId=${openclaw当前sessionId} --skuId=${skuId} ``` 7. 将购买后的结果信息返回给用户进行提示，根据 success 区分"购买成功"或"购买失败" ### 场景二：用户不在场购买 1. 用户发送对话，例如"三十分钟后帮我买一瓶红酒" 2. 提取用户意图关键词原文为"queryGoodsIntention"，以及定时任务 cron 3. 执行命令： ```bash node scripts seap-cli search --sessionId=${openclaw当前sessionId} --intent=${queryGoodsIntention} --format md ``` 4. 将查询后的 markdown 通过对话信息返回商品数据并按顺序标号，供用户进行选择 5. 用户对话确认指定序号商品后，读取 `${openclaw当前sessionId}.json` 文件对应序号的商品，获取商品 "skuId" 6. 若获取到 skuId 后根据 cron 创建定时任务并提醒用户"已创建预约购买任务"，否则返回商品不存在提示用户重新选择 7. 待定时任务启动完成后，则执行以下命令进行购买： ```bash node scripts seap-cli aipay --sessionId=${openclaw当前sessionId} --skuId=${skuId} ``` 8. 将购买后的结果信息返回给用户进行提示"预约购买任务已完成"，根据 success 区分"购买成功"或"购买失败" ## 实现说明 ### 意图识别 当检测到"买"字时，skill 需要分析用户意图： - **在场购买**：用户直接表达购买意图，如"我想买..." - **不在场购买**：用户表达定时购买意图，如"三十分钟后帮我买..."、"明天上午买..." ### 商品选择流程 1. 调用 search 接口获取商品列表 2. 将商品列表格式化为带序号的 markdown 3. 等待用户选择序号 4. 从 json 文件中读取对应的商品信息 ### 定时任务创建 对于不在场购买，需要： 1. 解析时间表达式（如"三十分钟后"、"明天上午"） 2. 转换为 cron 表达式 3. 使用 OpenClaw 的 cron 工具创建定时任务 4. 在定时任务中执行购买操作 ### 购买执行 1. 调用 aipay 接口执行购买 2. 解析返回结果 3. 根据 success 字段判断购买是否成功 4. 向用户反馈购买结果 ## 状态管理 Skill 需要维护以下状态： - 当前会话的 sessionId - 用户选择的商品序号 - 购买模式（在场/不在场） - 定时任务 ID（如果是预约购买） 状态可以存储在临时文件中，文件名为 `${sessionId}_state.json`。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: tanis90/document-to-markdown (version 1.0.0)

--- name: document_to_markdown description: "Document to Markdown converter - convert DOCX, PPTX, Excel files to Markdown. Use when extracting content from Word documents, PowerPoint presentations, or Excel spreadsheets." homepage: https://mineru.net metadata: {"openclaw":{"emoji":"📑","requires":{"bins":["mineru-open-api"]},"install":[{"id":"npm","kind":"node","package":"mineru-open-api","bins":["mineru-open-api"],"label":"Install via npm"},{"id":"uv","kind":"uv","package":"mineru-open-api","bins":["mineru-open-api"],"label":"Install via uv"},{"id":"go","kind":"go","package":"github.com/opendatalab/MinerU-Ecosystem/cli/mineru-open-api","bins":["mineru-open-api"],"label":"Install via go install","os":["darwin","linux"]}]}} --- # Document to Markdown - Convert DOCX, PPTX, Excel to Markdown Convert Word, PowerPoint, and Excel files to Markdown using MinerU Open API. No API key required. ## Quick Start ```bash # Convert a Word document mineru-open-api flash-extract report.docx # Convert a PowerPoint presentation mineru-open-api flash-extract slides.pptx # Convert an Excel spreadsheet mineru-open-api flash-extract data.xlsx # Convert from URL (no download needed) mineru-open-api flash-extract https://example.com/report.docx # Save to file mineru-open-api flash-extract report.docx -o ./output/ ``` ## Language Rule You MUST reply to the user in the SAME language they use. This is non-negotiable. ## Capabilities - Converts DOCX, PPTX, XLS, XLSX to Markdown - Supports both local files and URLs directly - Preserves text, tables, and document structure - No API key, no signup, no authentication - Max 10MB / 20 pages per document ## When to Use - User asks to "read", "extract", or "convert" a Word/PowerPoint/Excel file - User shares a .docx, .pptx, or .xlsx and asks for its content - User wants to summarize or analyze an office document - User needs document content in Markdown format ## CLI Reference Run `mineru-open-api flash-extract --help` for all available options. ## Data Privacy - `flash-extract` uploads the document to MinerU's cloud API for processing and returns the result. No account or API key is required. - Documents are processed in real-time and are not stored after extraction. - For details, see https://mineru.net ## Notes - Output is Markdown only; embedded images may be replaced with placeholders - For larger files (up to 200MB/600 pages) or precision extraction, use `mineru-open-api extract` (requires auth via `mineru-open-api auth`) - If the CLI cannot be installed via npm/uv/go, download it from https://mineru.net/ecosystem?tab=cli


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: liuruibin/maxkb (version 1.0.1)

--- name: maxkb_agents description: 查询已发布智能体列表供 LLM 选择，再按指定智能体名称发起对话并返回回答。 --- # chat_to_agents — 智能路由与调用 ## 功能描述 本 Skill 提供两个工具函数，配合宿主智能体的 LLM 完成路由与调用： | 函数 | 作用 | |-----------------|----------------------------------------------------------------------| | `list_agents` | 返回所有已发布智能体的 name 和 desc，供 LLM 判断选择 | | `chat_to_agent` | 按 LLM 指定的 `agent_name` 调用对应智能体，返回回答 | ## 推荐调用流程 ``` 用户提问 │ ▼ LLM 调用 list_agents() ← 获取智能体列表 │ 返回 [{"name":..., "desc":...}, ...] ▼ LLM 根据问题选出最合适的 agent_name │ ▼ LLM 调用 chat_to_agent(question, agent_name) ← 发起对话 │ 返回 {"agent_name":..., "answer":...} ▼ 将 answer 返回给用户 ``` ## list_agents ```bash python3 scripts/main.py ``` 无需参数，返回 JSON 数组： ```json [ {"name": "客服助手", "desc": "处理用户常见问题"}, {"name": "代码助手", "desc": "辅助编写和审查代码"} ] ``` ## chat_to_agent ```bash python3 scripts/main.py <question> <agent_name> ``` | 参数 | 类型 | 说明 | |--------------|--------|--------------------------------------| | `question` | string | 用户的问题文本 | | `agent_name` | string | 由 LLM 从 list_agents 结果中选定的名称 | 返回 JSON 字符串： | 字段 | 类型 | 说明 | |--------------|--------|--------------------| | `agent_name` | string | 实际调用的智能体名称 | | `answer` | string | 智能体的回答内容 | 示例： ```json { "agent_name": "客服助手", "answer": "您好，有什么可以帮助您的？" } ``` ## 环境变量 | 变量 | 说明 | 默认值 | |-----------------------|--------------------------------|-------------------------| | `MAXKB_DOMAIN` | MaxKB 服务地址 | `<maxkb_domain>` | | `MAXKB_TOKEN` | Bearer Token（管理员 API Key） | — | | `MAXKB_WORKSPACE_ID` | 工作空间 ID | `default` | | `MAXKB_USERNAME` | 登录用户名（优先于 TOKEN） | — | | `MAXKB_PASSWORD` | 登录密码（优先于 TOKEN） | — |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: tianyn1990/feishu-audio (version 1.0.1)

--- name: feishu-audio description: 将音频文件转换为飞书可播放的语音消息。先用 ffmpeg 转为 opus 格式，再上传到飞书，最后发送 audio 消息。适用于用户想要在飞书中收到可播放的语音消息的场景。 --- # feishu-audio 将本地音频文件转换为飞书可播放的语音消息。 ## 适用场景 - 用户说"朗读 xxx"、"语音播报" - 需要发送可播放的语音消息到飞书 ## 前置条件 1. **ffmpeg** - 音频格式转换 ```bash brew install ffmpeg ``` 2. **飞书应用配置** - 需要以下环境变量： - `FEISHU_APP_ID` - `FEISHU_APP_SECRET` 3. **接收者** - 飞书用户 Open ID（从上下文获取或手动指定） ## 使用方法 ### 方式 1：使用已有音频文件 ```bash bash scripts/send_audio.sh <音频文件路径> [接收者OpenID] ``` ### 方式 2：结合 TTS 使用 先生成音频，再用本技能发送： ```bash # 1. 用 edge-tts 生成音频 edge-tts -t "你好，我是小曦" -v zh-CN-XiaoxiaoNeural --write-media /tmp/voice.mp3 # 2. 转为 opus 并发送到飞书 bash scripts/send_audio.sh /tmp/voice.mp3 ``` ## 脚本说明 ### send_audio.sh 主脚本，完整的音频消息发送流程。 **参数：** - `$1` - 音频文件路径（必需） - `$2` - 接收者 Open ID（可选，默认从环境变量 FEISHU_RECEIVER 获取） **环境变量：** - `FEISHU_APP_ID` - 飞书应用 ID - `FEISHU_APP_SECRET` - 飞书应用密钥 - `FEISHU_RECEIVER` - 接收者 Open ID（可选） **流程：** 1. 检查 ffmpeg 是否可用 2. 检查音频文件是否存在 3. 用 ffmpeg 转换为 opus 格式（飞书要求） 4. 获取飞书 tenant_access_token 5. 上传到飞书（file_type=opus） 6. 发送 audio 消息 ## 音频格式要求 飞书语音消息要求： - **格式**: opus (OGG 容器) - **编码**: libopus - **采样率**: 24000 Hz - **声道**: 单声道 ## 故障排查 ### ffmpeg 未安装 ```bash brew install ffmpeg ``` ### 上传失败 检查飞书应用权限： - `im:message` - `im:message:send_as_bot` ### 消息发送成功但无法播放 确认： 1. 上传时使用了 `file_type=opus` 2. 上传时传递了 `duration` 参数


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: sf0799/shell-safe-exec (version 0.1.0)

--- name: shell-safe-exec description: Run non-destructive repo-local commands with explicit safety rules. Use when the user asks to build, test, lint, format, inspect status, or install project dependencies in the current workspace, and the task can be completed without risky system operations. Do not use for long-running services, background process management, or generic command execution frameworks. Chinese triggers: 执行命令、运行测试、构建项目、安装依赖、跑 lint、跑 format，但要求安全执行. --- # 安全执行 只执行与项目相关、且明确安全的命令。 ## 允许执行 - build、test、lint、format、type-check、health-check - 使用项目默认包管理器安装依赖 - 验证改动所需的状态和检查命令 ## 禁止执行 - 递归删除或其他大范围破坏性文件操作 - 磁盘、分区、内核、服务或系统配置修改 - 提权操作 - 网络扫描、远程执行、端口转发 - 读取与项目无关的系统敏感信息 ## 工作流 1. 优先使用仓库已经定义好的命令，不临时拼复杂管道。 2. 把用户提供的路径和参数当作不可信输入，按字面值传递，避免命令注入。 3. 只运行完成当前验证所需的最小命令。 4. 一旦失败立即停止，直接返回真实错误，不做危险重试。 5. 所有命令都限定在项目工作区内。 ## 输出 - 执行了什么命令 - 成功或失败 - 如果失败，返回关键错误信息


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: derekdong-star/daily-reflection-journal (version 1.0.0)

--- name: daily-reflection description: 每日反思日记引导技能，基于《认知觉醒》的反思框架。当用户说"做每日反思"、"写反思日记"、"今天反思"、"daily reflection"、"回顾今天"等触发。引导用户通过事实-感受-启发三层面深度反思，将日常经历转化为成长素材。 metadata: author: 洪伟 version: 1.0.0 tags: [reflection, journaling, personal-growth, cognitive-awakening] --- # 每日反思日记 反思 ≠ 流水账。每日反思的核心是**捕捉触动你的瞬间，深挖背后的原因**，而非记录"今天做了什么"。 ## 反思流程 ### 第一步：选择事件 问用户：**"今天哪件事让你印象最深、情绪波动最大，或有所触动？"** - 只需 1-3 件事，不求全 - 选择那些有"感觉"的时刻，而非流水账 ### 第二步：三层面反思 对每个事件，依次引导： **1. 事实层面** - 具体发生了什么？（场景、人物、对话） - 尽量还原当时的情况 **2. 感受层面** - 当时的情绪是什么？ - 为什么会有这种感受？ - 至少问自己 3 次"为什么"找到深层原因 **3. 启发层面** - 这件事揭示了我怎样的思维模式或行为习惯？ - 如果重来，我会怎么做？ - 我从中学到了什么？ - 以后如何避免或复现？ ### 第三步：提炼行动 - **一个明确的改变**：明天开始我会... - 避免"知道了但不改变" ## 引导问题 当用户不知如何开始时，使用 [prompts-library.md](references/prompts-library.md) 中的问题启发。 核心原则： - 随机选 2-3 个问题即可 - 根据回答继续追问"为什么" - 问题是启发思考，不是评判 ## 理论依据 详见 [cognitive-awakening.md](references/cognitive-awakening.md) 核心要点： - **元认知**：反思是锻炼元认知的最佳方式 - **触动点 = 成长点**：抓住情绪波动的瞬间 - **深度学习**：将经历转化为经验 ## 输出格式 将反思内容保存到 `daily-reflection/YYYY-MM-DD.md`，格式： ```markdown # YYYY-MM-DD 每日反思 ## 事件一：[简短标题] **事实**：... **感受**：... **启发**：... **行动**：... ## 事件二：[简短标题] ... ## 今日金句 > [一句总结今天的感悟] ## 明日一个小改变 [具体的、可执行的一个改变] ``` ## 避免的误区 - ❌ 记成流水账（"今天早上起床，吃了早餐..."） - ❌ 只记录不反思（没有深挖原因） - ❌ 反思后无行动（知道了但不改变） - ❌ 求多不求深（一天记录太多事件）


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: rdk14/instagram-caption-scraper (version 1.0.0)

--- name: instagram-caption description: Scrapes and returns the caption from any public Instagram post or reel URL. Use this when the user shares an Instagram link and wants to extract, read, summarise, translate, or analyse the caption of that post or reel. version: 1.0.0 author: Raj openclaw: emoji: "📸" requires: bins: - python3 pip: - instaloader==4.14.1 --- # Instagram Caption Scraper ## Purpose Extract the caption text from a public Instagram post or reel URL provided by the user. ## When to Use This Skill Trigger this skill when the user: - Shares an Instagram URL containing `/p/` or `/reel/` - Says "get the caption", "what does this post say", "read this reel", "summarise this Instagram post" - Wants to translate, rewrite, or analyse an Instagram caption ## How to Run Execute the following shell command, replacing `{{url}}` with the Instagram URL: ```bash python3 {{skill_dir}}/instagram_scraper.py {{url}} ``` The script will return one of: - `CAPTION: <text>` — successfully extracted caption with metadata - `ERROR: <reason>` — failure with explanation ## Example Triggers - "Get the caption from https://www.instagram.com/p/DRiFkfoiIuC/" - "What does this Instagram reel say? https://www.instagram.com/reel/ABC123/" - "Summarise this post: https://www.instagram.com/p/XYZ/" - "Translate this Instagram caption: https://www.instagram.com/p/ABC/" ## Output Format On success the script returns: ``` CAPTION: <full caption text> --- Owner : @username Type : GraphImage / GraphVideo / GraphSidecar Likes : 1234 Shortcode: ABC123 ``` ## Rules - Only trigger when a valid Instagram URL (`/p/` or `/reel/`) is present - Never fabricate or guess caption content if the script returns an error - If the post is private, inform the user and suggest they check the account's privacy settings - Always display the full caption before offering any summary or analysis - If the user asks to summarise or translate, do so AFTER displaying the raw caption ## Setup Notes Requires Python 3.8+ and the `instaloader` library: ```bash pip3 install instaloader==4.14.1 ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: brucegutman/pipeworx-nhtsa (version 1.0.0)

--- name: pipeworx-nhtsa description: Decode VINs and look up vehicle makes/models via the NHTSA Vehicle Product Information Catalog version: 1.0.0 metadata: openclaw: requires: bins: - curl emoji: "🚗" homepage: https://pipeworx.io/packs/nhtsa --- # NHTSA Vehicle Data Decode Vehicle Identification Numbers (VINs) into make, model, year, body type, engine, and safety features. Also browse all registered vehicle makes and look up available models by make and year. ## Tools - **`decode_vin`** — Decode a 17-character VIN into structured vehicle attributes - **`get_makes`** — All vehicle makes (brands) registered with NHTSA - **`get_models`** — Vehicle models for a specific make and model year (e.g., Toyota 2022) ## When to use - A user provides a VIN and wants to know the car details - Building a vehicle lookup feature for insurance or auto parts - Browsing what models a manufacturer offers in a given year - Verifying vehicle information from a bill of sale or registration ## Example: decode a VIN ```bash curl -s -X POST https://gateway.pipeworx.io/nhtsa/mcp \ -H "Content-Type: application/json" \ -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"decode_vin","arguments":{"vin":"1HGBH41JXMN109186"}}}' ``` Returns: make, model, year, body class, drive type, engine displacement, fuel type, plant city, and more. ## MCP config ```json { "mcpServers": { "pipeworx-nhtsa": { "command": "npx", "args": ["-y", "mcp-remote@latest", "https://gateway.pipeworx.io/nhtsa/mcp"] } } } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: cat-xierluo/md2word (version 1.0.1)

--- name: md2word homepage: https://github.com/cat-xierluo/legal-skills author: 杨卫薪律师（微信ywxlaw） version: "1.0.1" license: MIT description: Markdown转Word文档技能。将Markdown文档转换为符合中文排版标准的专业格式Word文档，支持多种预设格式。适用于正式文档、论文、报告等需要规范排版的文档转换。 --- # Markdown转Word文档Skill ## 概述 将 Markdown 文档转换为符合中文排版标准的 Word 文档。支持完整的 Markdown 语法，自动应用专业格式设置。 ## 依赖要求 ### Python 依赖 ```bash pip install python-docx Pillow beautifulsoup4 PyYAML ``` ### 可选依赖 ```bash npm install -g @mermaid-js/mermaid-cli ``` ## 快速开始 主转换脚本：`scripts/md2word.py` ```bash # 基本转换 python scripts/md2word.py input.md output.docx # 使用预设格式 python scripts/md2word.py input.md --preset=academic # 使用自定义配置 python scripts/md2word.py input.md --config=my-config.yaml ``` ## 配置系统 ### 内置预设 预设信息从 YAML 文件动态读取，运行以下命令查看完整列表： ```bash python scripts/config.py --list ``` 常用预设： - **legal** — 法律文书格式（默认） - **service-plan** — 法律服务方案（含分层配色） - **minimal** — 极简格式 - **academic** — 学术论文格式 - **report** — 工作报告格式 > 完整配置见 `assets/presets/*.yaml`，设计说明见 `assets/theme-notes/` ### 自定义配置 复制配置模板并修改： ```bash cp assets/config-template.yaml my-config.yaml ``` ### Word 模板文件 将 `.docx` 模板放入 `assets/templates/` 目录，或使用 `--template` 指定。 **Word 模板 vs 配置文件**： - **Word 模板**：控制视觉元素（页眉、页脚、Logo） - **配置文件**：控制格式参数（字号、行距、页边距） ## 参考文档 - **配置参考**: [references/config-reference.md](references/config-reference.md) - **样式映射**: [references/style-mappings.md](references/style-mappings.md) - **使用示例**: [references/examples.md](references/examples.md) ## 错误处理 - **文件编码**：自动检测 UTF-8 和 GBK - **模板找不到**：使用默认格式创建新文档 - **Mermaid 失败**：降级为文本描述 - **图片过大**：自动压缩和调整尺寸 ## 目录结构 ``` md2word/ ├── SKILL.md # 本文档 ├── CHANGELOG.md # 版本记录 ├── references/ # 参考文档 │ ├── config-reference.md │ ├── style-mappings.md │ └── examples.md ├── scripts/ # 转换脚本 │ ├── md2word.py # 主脚本 │ ├── config.py # 配置模块（含 --list 查看预设） │ ├── extract_template_config.py # 从 Word 模板提取配置 │ ├── formatter.py # 文本格式化模块 │ ├── table_handler.py # 表格处理模块 │ └── chart_handler.py # 图表渲染模块 └── assets/ # 资源文件 ├── presets/ # YAML 预设配置 ├── theme-notes/ # 预设设计说明文档 ├── templates/ # Word 模板文件 └── config-template.yaml ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: bill492/unblocklabs-skill-audit (version 1.0.0)

--- name: skill-audit description: Audit all installed skills for quality, duplicates, structural issues, and best-practice compliance. Use when asked to review, audit, lint, or check skills for problems. Triggers on "audit skills", "skill quality", "check my skills", "skill duplicates", "skill hygiene". compatibility: - openclaw --- # skill-audit Scans all skill locations (global, workspace, project) and produces a structured audit report. ## What It Checks ### Structural Quality (per skill) 1. **Description quality** — Is the `description` field trigger-oriented (tells the model *when* to use it) vs a vague summary? 2. **Gotchas section** — Does the SKILL.md include a Gotchas/Pitfalls/Common Issues section? (Highest-signal content per Anthropic) 3. **Progressive disclosure** — Does the skill use subdirectories (scripts/, references/, assets/, examples/) or is it a flat SKILL.md? 4. **File structure** — Are there scripts, templates, or reference files the agent can discover? 5. **YAML frontmatter** — Does it have `name`, `description`, and optionally `compatibility`? 6. **Category fit** — Does it map cleanly to one of the 9 skill categories (Library/API, Verification, Data, Automation, Scaffolding, Code Quality, CI/CD, Runbooks, Infrastructure)? ### Cross-Skill Issues 7. **Duplicates** — Same skill name or overlapping functionality across global/workspace/project dirs 8. **Orphan files** — Stale `.skill` files, empty dirs, leftover copies 9. **Category gaps** — Which of the 9 categories have no skills at all? 10. **Stale skills** — Skills that reference missing tools, dead paths, or deprecated APIs ## How to Run Tell the agent: "audit my skills" or "run skill-audit" The agent will: 1. Run `scripts/audit.sh` to scan all skill locations and collect metadata 2. Score each skill (0-10) based on the checks above 3. Produce a summary report with: - Per-skill scorecard - Top issues to fix (sorted by impact) - Category coverage map - Duplicate/orphan findings ## Output Results are written to `.sub-agent-results/skill-audit-report.md` and summarized in chat. ## Scoring | Points | Criteria | |--------|----------| | +2 | Has YAML frontmatter with name + description | | +2 | Description is trigger-oriented (contains "use when", "trigger", action verbs) | | +2 | Has a Gotchas/Pitfalls/Common Issues section | | +2 | Uses progressive disclosure (has subdirs with scripts/references/assets) | | +1 | Has at least one script or executable file | | +1 | SKILL.md is between 200-5000 chars (not too sparse, not bloated) | Scores: 8-10 = Good, 5-7 = Needs work, 0-4 = Poor ## References - [Anthropic: Lessons from Building Claude Code Skills](https://x.com/trq212/status/2033949937936085378) — Thariq's 9 categories, gotchas sections, progressive disclosure - [Ole Lehmann: Auto-improve Skills](https://x.com/itsolelehmann/status/2033919415771713715) — Autoresearch loop (future enhance mode)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: clawkk/home-moving (version 0.1.0)

--- name: "Home Moving" description: "提供跨城搬家、同城拉货、大件打包及透明计费预约指引。" --- # 搬家服务 ## 筛选项 - 服务半价（时薪/计件/整屋/一口价） - 服务范围（室内/室外/高空/深度） - 紧急程度（即时响应/预约到家/周期维护） ## 返回字段 - 服务工时预估/人员资质/服务保险覆盖 - 工具自带/耗材清单/二次上门保障 - 历史星级/实名认证状态/用户追评 ## 示例请求 - “如何快速使用 搬家服务 处理 [具体场景任务] ？” - “搬家服务 的 [核心功能] 有哪些最新玩法/优惠信息？” ## 更新频率 - 依据季节需求波动与区域运力状态每周进行评估更新 ## 注意事项 - 建议上门服务期间留人陪同；贵重物品请提前收起或告知服务人员。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ckchzh/customer-service-reply (version 2.0.0)

--- version: "2.0.0" name: Customer Service Reply description: "客服话术和回复模板生成器。售前咨询、售后问题、差评回复、退换货、升级处理、行业FAQ、满意度挽回。. Use when you need customer service reply capabilities. Triggers on: customer service reply." 客服回复模板。售前咨询、售后处理、退换货、投诉回复、好评引导、升级处理、行业FAQ、满意度挽回。Customer service reply templates for pre-sale, after-sale, returns, complaints, escalation, FAQ generation, satisfaction recovery. 客服话术、电商客服、售后模板、投诉处理、满意度管理。Use when responding to customer inquiries. author: BytesAgain homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills --- # customer-service-reply 客服话术和回复模板生成器。售前咨询、售后问题、差评回复、退换货、升级处理、行业FAQ、满意度挽回。 ## Usage This skill provides a script `cs.sh` for generating customer service reply templates. ### Commands | Command | Description | |---------|-------------| | `cs.sh presale "产品" "客户问题"` | 售前咨询回复 | | `cs.sh complaint "投诉内容"` | 投诉处理话术 | | `cs.sh bad-review "差评内容"` | 差评回复（诚恳+解决方案） | | `cs.sh refund "退款原因"` | 退换货话术 | | `cs.sh escalate "问题描述"` | 升级处理方案（L1→L2→L3→L4分级话术） | | `cs.sh faq "行业"` | 行业FAQ自动生成（20个高频问题+标准回答） | | `cs.sh satisfaction "评分" "反馈"` | 满意度挽回（根据1-5分输出对应策略） | | `cs.sh help` | 显示帮助信息 | ### How to run ```bash bash scripts/cs.sh <command> [args...] ``` ### Examples ```bash # 基础功能 bash scripts/cs.sh presale "蓝牙耳机" "能防水吗" bash scripts/cs.sh complaint "发货太慢了，等了一周还没收到" bash scripts/cs.sh bad-review "质量太差，用了两天就坏了" bash scripts/cs.sh refund "尺码不合适想换货" # 新增功能 bash scripts/cs.sh escalate "客户要求退一赔三，威胁投诉12315" bash scripts/cs.sh faq "美妆" bash scripts/cs.sh faq "数码" bash scripts/cs.sh satisfaction "2" "产品有质量问题" bash scripts/cs.sh satisfaction "5" "非常满意" ``` 查看 `tips.md` 获取电商客服实战技巧（响应速度、投诉处理、满意度管理等）。 ## Notes - 纯本地生成，不依赖外部API - Python 3.6+ 兼容 - 话术风格：专业、诚恳、有温度 --- 💬 Feedback & Feature Requests: https://bytesagain.com/feedback Powered by BytesAgain | bytesagain.com


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)