OpenClaw/clawhub-security-signals huggingface dataset:
Skill: zaviwayne/qq-mail-reader (version 1.0.5)

--- name: qq-mail-reader description: 读取QQ邮箱邮件。使用IMAP协议连接QQ邮箱，支持多种编码解析。触发场景：用户要求查看邮箱、读取邮件、查看最近邮件、筛选特定邮件等。 homepage: https://service.mail.qq.com/cgi-bin/help?subtype=1&&id=28&&no=1001256 metadata: { "openclaw": { "emoji": "📧", "requires": { "env": ["MAIL_USER", "MAIL_PASS"], "config": ["~/.openclaw/secrets/mail_qq.env"] }, "install": [] } } --- # QQ邮箱读取 通过 IMAP 协议读取 QQ 邮箱邮件。 ## 触发条件 用户提到以下场景时使用： - "查看邮箱" - "读取邮件" - "查看最近邮件" - "查看当天邮件" - "搜索邮件" - "有新的邮件吗" ## 配置 需要提前配置 QQ 邮箱 IMAP： 1. 在 QQ 邮箱设置中开启 IMAP 服务 2. 创建授权码（不是登录密码） 3. 配置 secrets 文件：`~/.openclaw/secrets/mail_qq.env` ``` MAIL_USER=[REDACTED_SECRET] MAIL_PASS=your_auth_code ``` ### 安全注意事项 - 凭据文件必须设置权限为 `600`（仅所有者可读），防止其他用户读取敏感信息 - `~/.openclaw/secrets/` 目录默认已经被 gitignore，不会被提交到版本控制 - 不要将授权码提交到代码仓库或分享给他人 ## 使用方法 ### 1. 连接邮箱 ```python import imaplib import email from email.header import decode_header IMAP_HOST = 'imap.qq.com' IMAP_PORT = 993 def connect_mail(user, password): mail = imaplib.IMAP4_SSL(IMAP_HOST, IMAP_PORT) mail.login(user, password) mail.select('INBOX') return mail ``` ### 2. 搜索邮件 ```python # 搜索最近7天的邮件 week_ago = (datetime.now() - timedelta(days=7)).strftime('%d-%b-%Y') typ, msg_ids = mail.search(None, f'SINCE {week_ago}') ``` ### 3. 解析邮件 ```python def decode_header_value(header_value): """兼容多种编码的解码""" if not header_value: return "" decoded_parts = [] for content, encoding in decode_header(header_value): if isinstance(content, bytes): try: decoded = content.decode(encoding if encoding else 'utf-8') except: # 尝试其他常见编码 for enc in ['gbk', 'gb2312', 'utf-8', 'big5', 'latin1']: try: decoded = content.decode(enc) break except: decoded = content.decode('utf-8', errors='ignore') else: decoded = str(content) decoded_parts.append(decoded) return ''.join(decoded_parts) ``` ### 4. 提取正文 邮件正文可能包含 HTML，需要清理： ```python import re def get_text_body(msg): body = '' if msg.is_multipart(): for part in msg.walk(): if part.get_content_type() == 'text/html': payload = part.get_payload(decode=True) encoding = part.get_content_charset() or 'utf-8' body = payload.decode(encoding, errors='ignore') break else: payload = msg.get_payload(decode=True) encoding = msg.get_content_charset() or 'utf-8' body = payload.decode(encoding, errors='ignore') # 清理HTML标签 text = re.sub(r'<[^>]+>', ' ', body) text = text.replace('&nbsp;', ' ') text = text.replace('&amp;', '&') text = re.sub(r'\s+', ' ', text) return text ``` ## 邮件过滤 根据用户需求过滤邮件，常见关键词类型： - 通知类：通知、提醒、公告 - 业务类：订单、发货、支付 - 职位类：面试、邀请、邀约（仅当用户明确需要时） ## 注意事项 1. QQ 邮箱必须使用授权码登录，非登录密码 2. 邮件编码可能是 GBK/GB2312/UTF-8 等，需要多种编码尝试 3. IMAP 搜索不支持中文，使用 SINCE 获取所有邮件后本地过滤


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 0xrikt/claw-werewolf (version 0.1.7)

--- name: claw-werewolf version: 0.1.7 description: AI Bot werewolf variety show. Register your bot and stream the match as a read-only live viewer. homepage: https://claw-werewolf-6u50hoq3u-riks-projects-ff86846d.vercel.app metadata: {"openclaw": {"emoji": "🐺", "category": "games"}} --- # Claw Werewolf AI Bot 综艺狼人杀。安装本技能让你的 Bot 自动报名，网页端提供清晰的观战体验。 **Web Viewer**: https://claw-werewolf-6u50hoq3u-riks-projects-ff86846d.vercel.app **ClawHub**: `clawdhub install claw-werewolf` ## Skill Files | File | URL | |------|-----| | **SKILL.md** (this file) | https://github.com/0xrikt/claw-werewolf | ## Installation (Recommended) ```bash clawdhub install claw-werewolf --site https://www.clawhub.ai --registry https://www.clawhub.ai/api ``` ## How Registration Works (MVP) 1. Install this skill in ClawHub. 2. Visit the Web Viewer and click “让我的 Bot 上场”。 3. Your Bot is placed into the hourly lobby queue. 4. When 9 bots fill, the match starts at the next top-of-hour. ## Notes - The MVP experience is read-only for human viewers. - Voice playback is optional and can be enabled later.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sky-lv/aippt-skip (version 1.0.0)

--- name: aippt description: "当用户明确要求使用 AI 智能设计生成 PPT 演示文稿时使用本技能（智绘高迪/AIPPT）。本技能调用远程 Design Agent API，根据文字描述自动生成专业 PPT 演示文稿，支持指定模板和自定义模型。当用户说'AI生成PPT'、'智绘PPT'、'自动生成演示文稿'、'帮我做个PPT'时触发。注意：本技能仅用于生成全新的 PPT，不用于读取、解析或编辑已有的 .pptx 文件——编辑已有文件请使用 pptx 技能。" --- # AIPPT Skill — AI 智能 PPT 生成 通过智绘高迪 Design Agent API，根据用户的文字描述自动生成专业 PPT。 ## 使用方式 > **Windows 用户**：所有 `bash` 命令请替换为对应的 `powershell -ExecutionPolicy Bypass -File` 版本，详见下方双版本示例。 > Windows 下使用 `python` 而非 `python3`。 ### ⚠️ 调用前必须输出提示（MANDATORY） **在执行脚本之前**，必须先向用户输出以下温馨提示： ```markdown > 💡 **温馨提示** > PPT 后台生成过程会持续消耗积分；如需终止任务，请点击下方链接进入智绘高迪平台操作。每张PPT平均消耗约0.5积分。 ``` ### 生成 PPT **macOS / Linux：** ```bash # 用法 — 只需 prompt PROMPT="制作一个关于人工智能发展趋势的商业演示" bash __SKILL_DIR__/scripts/generate.sh ``` **Windows (PowerShell)：** ```powershell # 用法 — 只需 prompt $env:PROMPT="制作一个关于人工智能发展趋势的商业演示"; chcp 65001 >nul; powershell -ExecutionPolicy Bypass -File "__SKILL_DIR__/scripts/generate.ps1" ``` ### 参数说明 | 环境变量 | 必填 | 说明 | |----------|------|------| | `PROMPT` | ✅ | PPT 内容描述，越详细越好 | ### 输出格式 脚本调用 Gateway 代理，Gateway 等待智绘 API 返回 `started` 事件后立即返回 JSON 结果： ``` 📝 开始生成 PPT... 🚀 任务已启动，PPT 正在后台生成中 ✅ PPT 生成任务已提交！ 结果： { "projectId": "69def187a7e1519b00d4708e", "workspaceUrl": "https://goatdee.qq.com/ppt/69def187a7e1519b00d4708e", "message": "PPT 任务已启动，正在后台生成中。你可以通过以下链接查看和编辑：..." } 📊 编辑地址: https://goatdee.qq.com/ppt/69def187a7e1519b00d4708e ``` ### 结果展示 将生成结果以 Markdown 格式展示给用户： ```markdown ✅ **PPT 生成任务已提交！** PPT 正在后台生成中，预计需要 5-15 分钟。 📊 **在线编辑地址**: [点击打开智绘高迪](https://goatdee.qq.com/ppt/{projectId}) 你可以点击链接查看生成进度，完成后直接在编辑器中修改。如需终止任务，也请在该页面中操作。 ``` ## 工作原理 1. Skill 脚本发送 POST 请求到 Auth Gateway 的 `/proxy/aippt/agent/run` 2. Gateway 注入模型凭证，转发到智绘高迪 Design Agent API（SSE 协议） 3. Gateway 等待上游返回 `started` 事件，提取 `projectId` 拼接编辑地址 4. Gateway 立即返回 JSON 结果给脚本，**不等待 PPT 生成完成** 5. PPT 在后台异步生成，用户通过编辑地址查看进度和结果 ## ⚠️ 编码要求（MANDATORY） **所有传递给接口的数据必须确保是 UTF-8 编码。** 这是强制要求，违反将导致服务端收到乱码。 - `PROMPT` 环境变量中的中文内容必须是 UTF-8 编码 - Windows 下必须在脚本执行前通过 `chcp 65001` 切换控制台代码页为 UTF-8 - HTTP 请求 body 必须以 UTF-8 编码发送，Content-Type 必须声明 `charset=utf-8` - **禁止**使用系统默认编码（如 GBK/GB2312）传递任何包含非 ASCII 字符的数据 ## ⚠️ 关于页数的严格规则（MANDATORY） **除非用户在描述中明确指定了页数（如"生成10页PPT"、"做一个5页的演示"），否则绝对不要在 PROMPT 中自行添加、编造或暗示任何页数相关的内容。** - ❌ 错误做法：用户说"帮我做个关于AI的PPT"，你在 PROMPT 里写"生成一个15页的AI主题PPT" - ✅ 正确做法：用户说"帮我做个关于AI的PPT"，PROMPT 直接写"制作一个关于AI的演示文稿"，不提及页数 - ✅ 正确做法：用户说"帮我做一个8页的AI PPT"，PROMPT 写"制作一个8页的关于AI的演示文稿" 页数由智绘高迪平台根据内容自动决定，不需要你来指定。擅自添加页数要求可能导致生成效果不符合预期。 ## 注意事项 1. 该 skill 调用远程 API，需要网络连接 2. Gateway 只等待 `started` 事件（通常几秒），不会因 PPT 生成耗时而超时 3. PPT 生成过程在后台进行，可能需要 5-15 分钟 4. 如果用户需要**编辑已有的 .pptx 文件**，请使用 `pptx` skill 而非本 skill 5. 如果 API 返回错误，检查网络连接并提示用户稍后重试 ## 依赖 | 依赖 | macOS / Linux | Windows | |------|--------------|---------| | HTTP 请求 | `curl`（系统自带） | PowerShell `Invoke-RestMethod`（内置） | | JSON 解析 | `python3`（系统自带） | PowerShell `ConvertFrom-Json`（内置） |


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: hzlawliet/wechat-sticker-publisher (version 1.0.3)

--- name: wechat-sticker-publisher description: Publish WeChat Official Account image-first sticker posts (图片消息 / 发贴图) into the draft box via the official API. Use when the user wants to create公众号“发贴图”草稿、图片消息草稿、image-first posts, poster-style posts, or a visual post that is primarily images with short supporting text instead of a long article. This skill uploads permanent image materials and creates `newspic` drafts only; final publication remains manual in the WeChat backend. metadata: openclaw: emoji: "🖼️" category: publishing clawdbot: emoji: "🖼️" requires: bins: [] env.optional: ["WECHAT_APP_ID", "WECHAT_APP_SECRET"] --- # WeChat Sticker Publisher Create **draft-only** WeChat Official Account sticker/image-message posts using the official API. ## Workflow 1. Use `scripts/publish_sticker.py`. 2. Provide one or more **absolute image paths**. 3. Provide a title and short text. 4. The script uploads permanent image materials. 5. The script creates a `newspic` draft in the WeChat draft box. 6. Do **not** auto-publish; final publish is manual in the WeChat backend. ## Rules - Keep this skill limited to **draft creation**. - Do not modify the existing daily article publishing workflow unless explicitly requested. - Require absolute local image paths. - Support up to 20 images; the first image becomes the cover image. - Send JSON request bodies explicitly as **UTF-8** (`application/json; charset=utf-8`). - This is important: using a different request-body path can lead to garbled Chinese text. ## Credentials The script reads credentials from environment variables: - `WECHAT_APP_ID` - `WECHAT_APP_SECRET` It can also load a local `wechat.env` file placed in the skill root. Use `wechat.env.example` as the starting template. If packaging/publishing this skill publicly, do **not** include any real secrets or local credential files. ## Primary command ```bash python3 scripts/publish_sticker.py \ --image /abs/path/to/image.jpg \ --title "标题 / Title" \ --text "配文 / Caption" ``` ## Multi-image example ```bash python3 scripts/publish_sticker.py \ --image /abs/1.jpg \ --image /abs/2.jpg \ --title "多图贴图测试" \ --text "这是一个多图图片消息草稿。" ``` ## Outputs Each run writes a JSON record to `outputs/` containing: - uploaded image material responses - draft request body - draft creation response Use those files for debugging and verification. ## Files - `scripts/publish_sticker.py` — main deterministic implementation - `references/api-notes.md` — concise implementation notes and known behavior - `README.md` — bilingual human-facing overview for repository/ClawHub readers - `LICENSE` — MIT license ## When to read references Read `references/api-notes.md` if you need: - the exact API mapping (`add_material` + `draft/add`) - encoding gotchas - the distinction between `news` and `newspic`

path: requirements.txt
content: requests>=2.31.0


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: joaodriessen/health-training-frontdoor (version 1.0.0)

--- name: health-training-frontdoor description: Narrow first-class front door for live Fitbit/training retrieval via stable JSON actions. --- # Health/Training Front Door Use this when OpenClaw needs **live Fitbit/health/training data** in a stable, low-ambiguity way. This is a thin, typed front door over the canonical Fitbit connector tooling. ## Why this exists `fitbit_tools.py` is the canonical operational backend, but it still requires low-level CLI composition. This front door provides a narrow action contract so agents can call one stable interface instead of assembling raw shell commands every time. ## Contract Run: - `node skills/health-training-frontdoor/scripts/request.js '{...json...}'` Input JSON: ```json { "action": "latest_recovery" } ``` Supported actions: - `auth_status` - `latest_recovery` - `quality_flags` - `training_status` - `training_window` - `unified_latest` Optional fields: - `days` (integer) - `ensureFresh` (boolean) - `source` (for `unified_latest`, default `best`) ## Default behavior by action - `latest_recovery`: fetches latest days of `hrv_rmssd,resting_hr,sleep_minutes,sleep_score,data_quality`; defaults `days=3`, `ensureFresh=true` - `quality_flags`: defaults `days=7` - `training_status`: defaults `days=14`, `ensureFresh=true` - `training_window`: defaults `days=14`, `ensureFresh=true` - `unified_latest`: defaults `days=14`, `source=best` ## Notes - Output is compact JSON. - This surface is read-only. - Interpretation/coaching remains outside this skill. ## Training Programming Reference All programming decisions must be grounded in **Practical Programming for Strength Training** (Rippetoe & Baker, 3rd Ed.). **Always load before giving any programming advice:** 1. `reference/practical-programming/INDEX.md` — who Joao is, relevant chapters, reading guide 2. `memory/training-continuity.md` — current lifts, recent sessions, health signals For any programming question, read the relevant sections of `reference/practical-programming/book.md` directly. The book is the authority. ## Backend mapping This front door maps directly to: - `fitbit-connector/scripts/fitbit_tools.py` Do not use this to bypass auth or write-capable operations.


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: cinience/aliyun-sas-manage (version 1.0.0)

--- name: aliyun-sas-manage description: Use when managing Alibaba Cloud Security Center (Sas) via OpenAPI/SDK, including the user needs Security Center resource operations, configuration updates, status queries, and troubleshooting Sas API or security workflow issues. version: 1.0.0 --- Category: service # Security Center Use Alibaba Cloud OpenAPI (RPC) with official SDKs or OpenAPI Explorer to manage resources for Security Center. ## Workflow 1) Confirm region, resource identifiers, and desired action. 2) Discover API list and required parameters (see references). 3) Call API with SDK or OpenAPI Explorer. 4) Verify results with describe/list APIs. ## AccessKey priority (must follow) 1) Environment variables: `ALICLOUD_ACCESS_KEY_ID` / `ALICLOUD_ACCESS_KEY_SECRET` / `ALICLOUD_REGION_ID` Region policy: `ALICLOUD_REGION_ID` is an optional default. If unset, decide the most reasonable region for the task; if unclear, ask the user. 2) Shared config file: `~/.alibabacloud/credentials` ## API discovery - Product code: `Sas` - Default API version: `2021-01-14` - Use OpenAPI metadata endpoints to list APIs and get schemas (see references). ## High-frequency operation patterns 1) Inventory/list: prefer `List*` / `Describe*` APIs to get current resources. 2) Change/configure: prefer `Create*` / `Update*` / `Modify*` / `Set*` APIs for mutations. 3) Status/troubleshoot: prefer `Get*` / `Query*` / `Describe*Status` APIs for diagnosis. ## Minimal executable quickstart Use metadata-first discovery before calling business APIs: ```bash python scripts/list_openapi_meta_apis.py ``` Optional overrides: ```bash python scripts/list_openapi_meta_apis.py --product-code <ProductCode> --version <Version> ``` The script writes API inventory artifacts under the skill output directory. ## Output policy If you need to save responses or generated artifacts, write them under: `output/aliyun-sas-manage/` ## Validation ```bash mkdir -p output/aliyun-sas-manage for f in skills/security/host/aliyun-sas-manage/scripts/*.py; do python3 -m py_compile "$f" done echo "py_compile_ok" > output/aliyun-sas-manage/validate.txt ``` Pass criteria: command exits 0 and `output/aliyun-sas-manage/validate.txt` is generated. ## Output And Evidence - Save artifacts, command outputs, and API response summaries under `output/aliyun-sas-manage/`. - Include key parameters (region/resource id/time range) in evidence files for reproducibility. ## Prerequisites - Configure least-privilege Alibaba Cloud credentials before execution. - Prefer environment variables: `ALICLOUD_ACCESS_KEY_ID`, `ALICLOUD_ACCESS_KEY_SECRET`, optional `ALICLOUD_REGION_ID`. - If region is unclear, ask the user before running mutating operations. ## References - Sources: `references/sources.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: wangzh999/xiaohongshu-publish-wangzh (version 1.0.0)

# 小红书发布器 Skill ## 描述 专注于小红书长文笔记发布的浏览器自动化技能。该技能提供标准化的操作流程来发布已准备好的内容到小红书平台。 ## 使用场景 - 发布预先准备好的内容到小红书 - 自动化小红书长文发布流程 - 批量内容发布工作流集成 ## 依赖 - 浏览器自动化支持（OpenClaw browser control） - 小红书创作者账号已登录 - 已准备好的标题和正文内容 ## 输入参数 - `title`: 笔记标题（必需） - `content`: 笔记正文内容（必需） - `tags`: 话题标签数组（可选） ## 示例调用 ```json { "task": "发布小红书笔记", "runtime": "subagent", "agentId": "xiaohongshu-publisher", "params": { "title": "今日AI热点速递🔥 | 2026年3月6日", "content": "今天AI圈又有什么新鲜事？快来看看这些超酷的进展！...", "tags": ["AI热点", "人工智能", "科技前沿"] } } ``` ## 操作流程 1. 导航到小红书创作平台 2. 点击"写长文"选项 3. 输入标题和正文内容 4. 应用一键排版优化 5. 点击"下一步"进入发布设置 6. 确认内容并点击"发布"按钮 7. 返回发布成功状态 ## 注意事项 - 确保小红书创作者账号已登录 - 内容长度限制为1000字以内 - 标题长度限制为64字以内 - 发布前会自动保存草稿


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: comfyui-controller (version 2.1.1)

# ComfyUI Controller 技能 ## 功能 支持本地和服务器 ComfyUI 的 smart 控制器： - 🤖 智能模型扫描（本地/服务器） - 🎯 自动工作流选择 - 📸 文生图/图生图 - 🎬 视频生成支持 - 📁 批量任务处理 - 🔄 双模式支持（本地 + 服务器） ## 配置 编辑 `config.json`: ```json { "local": { "enabled": true, "comfyui_path": "/path/to/ComfyUI", "host": "127.0.0.1", "port": 8188 }, "server": { "enabled": true, "host": "wp08.unicorn.org.cn", "port": 29592, "protocol": "https" } } ``` ## 使用 ### 命令行 ```bash # 使用本地 ComfyUI python3 comfyui_smart_controller.py --subject "一个美丽的女孩" --local # 使用服务器 ComfyUI python3 comfyui_smart_controller.py --subject "赛博朋克城市" --server # 自动选择（优先本地，失败则服务器） python3 comfyui_smart_controller.py --subject "风景" --auto ``` ### Python API ```python from comfyui_smart_controller import ComfyUIIntelligentController # 自动模式 controller = ComfyUIIntelligentController(mode="auto") # 或指定模式 controller = ComfyUIIntelligentController(mode="local") controller = ComfyUIIntelligentController(mode="server") # 生成图片 result = controller.auto_generate( subject="一个美丽的女孩", style="portrait", width=512, height=768 ) ``` ## 安装 ```bash cd /home/admin/openclaw/workspace/skills/comfyui-controller pip install -r requirements.txt ``` ## 文件结构 ``` comfyui-controller/ ├── comfyui_smart_controller.py # 主控制器 ├── config.json # 配置文件 ├── requirements.txt # 依赖 └── SKILL.md # 说明文档 ```

path: requirements.txt
content: requests>=2.28.0
websocket-client>=1.5.0


path: scheduler_config.json

Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: ivangdavila/question (version 1.0.0)

--- name: Question description: Ask better questions in any context with Socratic method, follow-up generation, and bias detection. --- ## Core Behaviors 1. **Detect leading/biased questions** — Flag loaded language, suggest neutral alternatives 2. **Generate contextual follow-ups** — Based on response, suggest deeper probes 3. **Surface hidden assumptions** — Identify what's being taken for granted 4. **Know when to pause** — Sometimes silence extracts more than another question --- ## Context Guides | Context | Load | Key Techniques | |---------|------|----------------| | User research / Interviews | `contexts.md#research` | Open-ended, avoid leading, probe behaviors | | Sales discovery | `contexts.md#sales` | SPIN, pain discovery, qualification | | Coaching / 1:1s | `contexts.md#coaching` | Socratic, powerful questions, reflection | | Journalism / Investigation | `contexts.md#journalism` | Evasion detection, contradiction probing | | Learning / Understanding | `contexts.md#learning` | Decomposition, assumption testing | --- ## Question Quality Checklist Before asking, verify: - [ ] **Open vs closed** — Is open-ended appropriate here? - [ ] **Neutral framing** — No embedded assumptions or desired answers? - [ ] **One thing at a time** — Not a compound question? - [ ] **Appropriate depth** — Right level for the relationship/context? --- ## Quick Reference | Need | See | |------|-----| | Question patterns and frameworks | `techniques.md` | | Common mistakes to avoid | `anti-patterns.md` | --- ## Session State <!-- Active context: research | sales | coaching | journalism | learning --> <!-- Current topic: what we're exploring --> <!-- Key assumptions to test: list -->


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: smallkeyboy/ie-expert (version 1.0.1)

--- name: ie-expert description: 工业工程专家智体 - 提供生产优化、精益生产、六西格玛、质量管理、供应链优化、设施规划等工业工程领域的专业咨询和分析建议。适用于生产线优化、流程改进、质量控制、库存管理等场景。 metadata: openclaw: version: "1.0.1" author: "User" tags: - 工业工程 - 精益生产 - 六西格玛 - 质量管理 - 供应链优化 - 生产优化 --- # 工业工程专家 一位资深的工业工程专家，提供专业领域的咨询和建议。 ## 专业领域 - **生产系统优化与调度** - 生产线平衡、产能规划、排程优化 - **精益生产与六西格玛** - 消除浪费、持续改进、DMAIC方法论 - **质量管理与质量控制** - SPC统计过程控制、质量体系、缺陷分析 - **供应链管理与物流优化** - 库存管理、配送网络、供应商管理 - **设施规划与布局设计** - 工厂布局、仓储设计、物料流优化 - **人因工程与工作设计** - 工作站设计、人机工程、作业测量 - **运营管理与决策分析** - KPI设计、绩效管理、决策支持 - **工业工程经济分析** - 成本分析、投资评估、ROI计算 ## 使用场景 当你需要以下帮助时，调用此智体： 1. 分析生产线效率问题，提出改进方案 2. 设计或优化质量管理体系 3. 进行库存管理策略分析 4. 评估工厂布局方案 5. 实施精益生产改善项目 6. 进行六西格玛项目分析 7. 供应链网络优化建议 ## 交互方式 直接向智体描述你的问题或场景，例如： - "我们的装配线平衡率只有75%，如何提升到85%以上？" - "如何建立一个有效的SPC控制系统？" - "帮我们分析一下安全库存的设置策略" - "新厂房布局应该考虑哪些因素？" 智体会根据你的具体情况，提供系统性的分析框架和可操作的建议。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: charlie-morrison/pr-description-generator (version 1.0.1)

--- name: pr-description-generator description: > Auto-generate pull request descriptions from git diffs and commit history. Parses conventional commits, categorizes changes (features, fixes, refactoring), analyzes file impact, generates reviewer hints, and produces structured descriptions. Supports minimal, standard, and detailed templates with markdown or JSON output. Use when asked to generate PR descriptions, create pull request summaries, describe git changes, summarize a branch, or prepare a PR body. Triggers on "PR description", "pull request description", "generate PR", "describe changes", "PR summary", "what changed", "PR body", "PR template". --- # PR Description Generator Auto-generate structured PR descriptions from git diffs and commit history. Pure Python + git CLI. ## Quick Start ```bash # Standard description (current branch vs main) python3 scripts/generate_pr_description.py # Compare against specific base branch python3 scripts/generate_pr_description.py --base develop # Minimal template (just bullet points) python3 scripts/generate_pr_description.py --template minimal # Detailed template (file breakdown + reviewer hints) python3 scripts/generate_pr_description.py --template detailed # JSON output (for automation) python3 scripts/generate_pr_description.py --format json # Different repo path python3 scripts/generate_pr_description.py --repo /path/to/repo # Save to file python3 scripts/generate_pr_description.py --output pr-body.md # Copy to clipboard python3 scripts/generate_pr_description.py --copy ``` ## Features - **Conventional commit parsing** — groups commits by type (feat, fix, refactor, etc.) - **Impact analysis** — rates changes as high/medium/low based on files, size, and risk - **File categorization** — groups by code, tests, docs, infra, deps, config, database, styles - **Reviewer hints** — warns about missing tests, DB migrations, infra changes, deletions - **Auto test plan** — generates relevant test checklist based on changed file types - **Auto base detection** — detects main vs master branch ## Templates | Template | Use Case | |----------|----------| | `minimal` | Quick summary, internal PRs | | `standard` | Default, most PRs | | `detailed` | Large PRs, cross-team reviews | ## Conventional Commits Best results when commits follow conventional format: ``` feat(auth): add OAuth2 login fix(api): handle null response from payment gateway refactor: extract validation into shared utility docs: update API reference for v2 endpoints ``` Non-conventional commits are grouped under "Other Changes".


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