OpenClaw/clawhub-security-signals huggingface dataset:
Skill: drkavner/omi-integration (version 1.0.0)

--- name: omi description: Sync recordings from Omi AI wearables (Omi, Limitless, etc.) via API and webhooks. Auto-sync transcripts, process recordings, and organize by device/date. homepage: https://github.com/BasedHardware/omi metadata: {"clawdbot":{"emoji":"🎙️","requires":{"bins":["curl","jq"]}}} --- # Omi Integration Sync and manage recordings from Omi AI wearables (Omi, Limitless pendant, etc.). ## Features - Auto-sync recordings from Omi backend - Webhook support for real-time transcripts - Multi-device support (tag by device) - Local storage with metadata - Summary and action item extraction ## Setup 1. Get your Omi API key from https://omi.me/developer or your self-hosted backend 2. Store it securely: ```bash mkdir -p ~/.config/omi echo "YOUR_API_KEY" > ~/.config/omi/api_key chmod 600 ~/.config/omi/api_key ``` 3. Configure backend URL (defaults to https://api.omi.me): ```bash echo "https://api.omi.me" > ~/.config/omi/backend_url # Or for self-hosted: echo "https://your-backend.com" > ~/.config/omi/backend_url ``` ## Usage ### Sync All Recordings ```bash omi-sync ``` ### Sync Recent (Last 7 Days) ```bash omi-sync --days 7 ``` ### List Recordings ```bash omi-list ``` ### Get Recording Details ```bash omi-get <recording-id> ``` ### Process Webhook Payload ```bash cat webhook-payload.json | omi-webhook-handler ``` ## Storage Recordings are stored in: ``` ~/omi_recordings/ ├── YYYY-MM-DD/ │ ├── <recording-id>/ │ │ ├── metadata.json │ │ ├── transcript.txt │ │ ├── audio.wav (if available) │ │ └── summary.md └── index.json ``` ## Webhook Setup Configure your Omi app to send webhooks to your endpoint: 1. Open Omi app → Settings → Developer 2. Create new webhook 3. Enter your webhook URL 4. Select events: `recording.created`, `transcript.updated` The skill includes a handler (`omi-webhook-handler`) that processes real-time events. ## Multi-Device Support Recordings are automatically tagged by device: ```json { "recording_id": "rec_123", "device_id": "limitless-001", "device_name": "Limitless Pendant", "device_type": "wearable", "context": "work", "transcript": "Meeting notes...", "created_at": "2026-02-02T15:38:00Z" } ``` ## API Endpoints Base URL: `https://api.omi.me/v1` (configurable) - `GET /recordings` - List all recordings - `GET /recordings/:id` - Get recording details - `GET /recordings/:id/transcript` - Get transcript - `GET /recordings/:id/summary` - Get AI summary - `POST /webhooks/register` - Register webhook endpoint ## Privacy - All data stored locally - API key encrypted at rest - Self-hosted backend supported - No telemetry or tracking - Webhook payloads logged for debugging (optional) ## Cron Setup Auto-sync every hour: ```bash 0 * * * * /path/to/omi-sync --days 1 >> ~/.local/share/omi/sync.log 2>&1 ``` Or use Clawdbot cron for integrated scheduling.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: darker314159/apifox-image-gen (version 1.0.0)

--- name: apifox-image-gen description: 使用Apifox图像生成API (jyapi.AI-WX.CN) 生成图片。支持gpt-image-1.5和grok-4-1-image模型。 metadata: author: blackbee version: 1.0.0 openclaw: emoji: 🖼️ allowed-tools: [exec, message] --- # Apifox Image Generation 使用jyapi.AI-WX.CN图像生成API生成图片。 ## 快速开始 ```bash python3 /root/.openclaw/workspace/skills/apifox-image-gen/image_gen.py -p "你的图片描述" -m gpt-image-1.5 -s 1024x1024 ``` ## 参数说明 | 参数 | 简写 | 默认值 | 说明 | |------|------|--------|------| | --prompt | -p | (必填) | 图片描述 | | --model | -m | gpt-image-1.5 | 模型: gpt-image-1.5, grok-4-1-image | | --size | -s | 1024x1024 | 尺寸: 1024x1024, 1536x1024, 1024x1536, 1:1, 2:3, 3:2, 9:16, 16:9 | | --n | -n | 1 | 生成数量 | | --output | -o | (自动) | 输出文件路径 | ## 示例 ```bash # 生成小蜜蜂图片 python3 /root/.openclaw/workspace/skills/apifox-image-gen/image_gen.py -p "一只可爱的小蜜蜂" -m gpt-image-1.5 -s 1024x1024 # 生成横版图片 python3 /root/.openclaw/workspace/skills/apifox-image-gen/image_gen.py -p "风景画" -m gpt-image-1.5 -s 1536x1024 # 生成竖版图片 python3 /root/.openclaw/workspace/skills/apifox-image-gen/image_gen.py -p "手机壁纸" -m gpt-image-1.5 -s 9:16 # 使用grok模型 python3 /root/.openclaw/workspace/skills/apifox-image-gen/image_gen.py -p "科技感海报" -m grok-4-1-image -s 16:9 ``` ## 使用方式 1. 直接用exec运行脚本 2. 图片会保存到 /tmp/ 目录 3. 可以用message工具发给用户


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: qiaoshaohua/human-style-writer (version 1.0.1)

--- name: human-style-writer description: 具备人工特征的AI创作技能。生成高质量长文、随笔、观点类内容，自然节奏、情感纹理、零AI腔调。由 adeeptools.com 提供服务。此为付费服务，每次创作支付 0.1 元（10分），执行前需完成支付验证。 homepage: https://adeeptools.com metadata: author: "adeeptools" category: "writing" capabilities: - "payment.process" permissions: - "network.outbound" - "credential.read" --- # 具备人工特征的AI创作技能 生成高质量长文——文章、随笔、观点稿、创意写作——读起来像真人所写。输出具有自然句长起伏、非线性叙事结构、真实情感纹理，刻意规避 AI 生成文本典型的"精致而空洞"模式。 **直接执行：** 如果用户在初始请求中同时提供了 `<question>` 和已支付完成的 `<order_no>`，可跳过第一阶段和第二阶段，直接进入 **🚀 第三阶段：服务执行**。 --- ## 🛒 第一阶段：创建订单 本阶段会调用远程服务生成订单，并在本地生成 **订单文件**（供 `clawtip` 技能读取）。 ### 1. 所需参数 - `<question>`：用户的完整创作需求（主题、字数、风格等，用户原话） ### 2. 执行命令 **务必在参数两侧加引号，以防命令注入漏洞**。 ```bash python3 skills/human-style-writer/scripts/create_order.py "<question>" ``` ### 3. 输出处理 **成功时**，脚本将向标准输出打印以下键值对： ``` ORDER_NO=<订单号> INDICATOR=<skill 标识，MD5(skill_name)> AMOUNT=<金额，单位分> ORDER_FILE=<订单文件绝对路径> ``` 提取 `ORDER_NO` 和 `INDICATOR` 两个值，进入 **💳 第二阶段：支付处理**。 > **关于 `AMOUNT` 字段：** 单位为**人民币分**。`AMOUNT=10` 表示需支付 **0.10 元**。向用户展示费用时请换算。 **失败时**，脚本以代码 `1` 退出，并打印 `订单创建失败: <错误详情>`。 > [!CAUTION] > **强制停止 — 不允许任何后续操作。** > 如果脚本以非零代码退出或输出包含 `订单创建失败:`： > - ❌ 不得进入第二阶段或第三阶段 > - ❌ 不得代表用户重试 > - ✅ 用通俗语言向用户报告失败原因，然后停止 --- ## 💳 第二阶段：支付处理 **操作：** 调用 `clawtip` 技能处理支付。若该技能未安装，告知用户运行： ```bash clawhub install clawtip ``` 并重启 OpenClaw session。 **调用 `clawtip` 时只需传入两个参数：** - `order_no`：第一阶段输出的 `ORDER_NO` - `indicator`：第一阶段输出的 `INDICATOR` `clawtip` 会自动： 1. 从 `~/.openclaw/skills/orders/{indicator}/{order_no}.json` 读取订单信息（第一阶段已写好） 2. 发起支付请求 3. 将支付凭证 `payCredential` 写回**同一订单文件** **等待 `clawtip` 返回结果后：** - 如 `clawtip` 输出包含 `已获取到支付凭证` 和 `订单号: <ORDER_NO>`：进入 **🚀 第三阶段：服务执行**（用该订单号） - 如 `clawtip` 输出 `授权链接:` 或 `鉴权链接:`：提示用户完成授权/扫码支付后再继续 - 如 `clawtip` 输出网络/系统异常：向用户报告错误，停止流程 --- ## 🚀 第三阶段：服务执行 `clawtip` 成功写入 `payCredential` 后，执行 AI 创作服务。脚本会自动从订单文件读取 `question` 和 `payCredential`，你只需提供订单号。 ### 1. 所需参数 - `<order_no>`：第一阶段生成的订单号 ### 2. 执行命令 可选传入 `--style` 参数（`general` / `emotional` / `technical` / `news`，默认 `general`）。 ```bash python3 skills/human-style-writer/scripts/ai_create.py "<order_no>" --style general ``` **风格选择指引：** | `--style` 值 | 适用场景 | |---|---| | `general` | 通用、均衡，默认推荐 | | `emotional` | 情感类、生活类、个人随笔 | | `technical` | 行业分析、技术解读 | | `news` | 新闻评论、事件叙述 | ### 3. 输出处理 1. 提取脚本打印的 `PAY_STATUS` 值（格式：`PAY_STATUS: <值>`）。 2. **`SUCCESS` 状态：** 脚本输出完整文章 Markdown 正文，将其呈现给用户。 3. **`FAIL` 状态：** 说明 `clawtip` 支付凭证解密后是失败状态。**必须回头检查 `clawtip` 原始输出** 中是否有 `授权链接:` 或 `鉴权链接:`，如有则按 clawtip 协议走授权/鉴权流程，再重试第三阶段。 4. **`ERROR` 状态：** 提取 `ERROR_INFO` 值，向用户告知错误原因，不得继续。 ### 4. 成功后向用户提供 - 完整文章内容（Markdown 格式） - 主动询问用户是否需要：调整长度、更换风格、修改某些段落 --- ## 📌 技能概述 **触发词示例：** - "帮我写一篇文章" - "写一篇具备人工特征的内容" - "创作一篇关于…的文章，别太像 AI 写的" - "write an article / essay / blog post about..." - "generate content that sounds human" **服务说明：** - 每次创作：**0.10 元（10分）** - 履约方式：每单独立支付，支付即创作，幂等防重复 - 来源标记：`openclaw`（与网站配额独立计算）


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: timing-up/groq-voice-transcribe (version 1.0.1)

--- name: groq-voice-transcribe description: Transcribe audio files via Groq's OpenAI-compatible speech-to-text API. Use when the user sends voice messages or audio files and you need fast cloud speech-to-text through Groq instead of local Whisper. --- # Groq Voice Transcribe Fast speech-to-text for voice notes and audio files through Groq's OpenAI-compatible transcription endpoint. Use it when you want cloud transcription via Groq instead of running Whisper locally. Best for: - Telegram / Signal voice notes - short audio clips - Chinese, English, or mixed daily speech - fast transcript generation for follow-up summarization or chat replies ## What you need You need a **Groq API key**. Groq often provides a free developer tier / trial credits for new users. Get one from: - https://console.groq.com/ - Sign in, open **API Keys**, then create a key ## Easiest setup in OpenClaw If OpenClaw is already running and configured, you can simply ask your assistant: - **"Configure Groq Voice Transcribe for me"** - **"Here is my Groq API key, set up Groq Voice Transcribe"** The assistant can place the key into `~/.openclaw/openclaw.json` for you. ## Manual setup Set `GROQ_API_KEY`, or configure it in `~/.openclaw/openclaw.json` under: ```json { "skills": { "entries": { "groq-voice-transcribe": { "apiKey": "GROQ_KEY_HERE" } } } } ``` ## Quick start ```bash {baseDir}/scripts/transcribe.sh /path/to/audio.ogg ``` Defaults: - Model: `whisper-large-v3-turbo` - Output: `<input>.txt` - Format: plain text ## Common examples ```bash # Basic transcript {baseDir}/scripts/transcribe.sh /path/to/audio.ogg # Chinese voice message {baseDir}/scripts/transcribe.sh /path/to/audio.ogg --language zh --prompt "中文普通话，日常聊天" # Save to a custom file {baseDir}/scripts/transcribe.sh /path/to/audio.ogg --out /tmp/transcript.txt # Verbose JSON output {baseDir}/scripts/transcribe.sh /path/to/audio.ogg --json --out /tmp/transcript.json ``` ## Flags - `--model <name>`: transcription model (default `whisper-large-v3-turbo`) - `--out <path>`: output file path - `--language <code>`: hint the spoken language, for example `zh`, `en`, `ja` - `--prompt <text>`: optional context or spelling hint - `--json`: write verbose JSON instead of plain text ## Notes - Audio is sent to Groq for transcription. - This skill is meant for transcription, not text-to-speech. - If language is omitted, Groq can usually auto-detect it, but passing `--language zh` often helps for Chinese voice notes.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: ivangdavila/traditional-chinese (version 1.0.0)

--- name: Traditional Chinese description: Write Traditional Chinese that sounds human. Not formal, not robotic, not AI-generated. --- ## The Real Problem AI Traditional Chinese is technically correct but sounds off. Too formal. Too 書面語. Natives in Taiwan and Hong Kong write more casually, with local flavor. Match that. ## Traditional vs Simplified Never mix scripts: - 繁體字 (Traditional): Taiwan, Hong Kong, Macau - This skill is for Traditional only - Different vocabulary, not just characters ## Taiwan vs Hong Kong Regional differences matter: - Taiwan: 影片、軟體、網路、捷運 - Hong Kong: 短片、軟件、網絡、地鐵 - Different slang and expressions - Ask which, stay consistent ## Formality Default Default register is too high. Casual Traditional Chinese is warm. Unless explicitly formal: lean casual. ## Particles & Softeners These make it natural: - 啊/呀: softening - 呢: follow-up, "what about" - 吧: suggestion, uncertainty - 嘛: obviousness - 喔/哦: acknowledgment - 耶: excitement (Taiwan) - 囉: completed action ## Taiwan Expressions Common in Taiwan: - 超: intensifier (超好吃) - 很屌、很狂: impressive - 傻眼: shocked, disbelief - hen可以: very acceptable - 有夠: very (有夠讚) ## Hong Kong Expressions Common in HK: - 好: intensifier (好靚) - 勁: very, impressive - 掂: great, sorted - 冇問題: no problem - Cantonese-influenced written patterns ## Fillers & Flow Natural fillers: - 就是、然後、那個 - 其實、不過、可是 - 對了、話說 ## Reactions React naturally: - 真的假的、不會吧、蛤? - 哇、天啊、傻眼 - 讚、太棒了、好耶 - 哈哈哈、XD in text ## The "Native Test" Before sending: would a Taiwanese/HK person screenshot this as "AI-generated"? If yes—too formal, wrong region, too stiff. Add local flavor.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: be1human/clawphone-phone-control (version 1.0.0)

--- name: clawphone-phone-control description: 使用手机控制 MCP 完成手机界面感知与操作。适用于读取当前手机状态、打开 App、处理弹窗、点击控件、输入文本、排查手机自动化失败等场景。执行时优先读取界面状态，涉及坐标点击时必须基于当前截图临时判定，禁止把历史坐标当成通用规则。 --- # ClawPhone Phone Control ## 快速流程 1. 先感知，再操作。 2. 优先用文字/节点能力，坐标点击只在当前截图下临时使用。 3. 脆弱流程必须一步一验，不要把多步盲打成“理应成功”。 ## 感知优先级 1. 先用 `get_screen_info` 判断前台应用、分辨率、可见文字。 2. 需要精确定位时，再用 `capture_screen`。 3. 需要找标准控件时，优先 `click_by_text` 或 `find_node`。 ## 操作原则 - 输入前先确认输入框真的已获焦。 - 启动 App 后先确认前台应用真的切换成功。 - 若工具返回“已点击”“已启动”“已输入”，仍要以界面复核为准。 - 发送前先确认文本真的已经进入输入框。 - 点击发送前先确认发送控件真实可见。 - 发送后必须再次确认界面已变化，不要仅凭工具返回值认定成功。 ## 启动 App 1. 可先调用 `launch_app(...)`。 2. 立即用 `get_current_app` 或 `get_screen_info` 确认前台应用是否真的切换。 3. 若仍停留在桌面或其他 App，不要在同一路径上重复盲开。 4. 先读取当前屏幕状态，再决定是否改用桌面图标点击、通知入口或其他页面内入口。 ## 输入兜底 1. 先确认输入框已聚焦。 2. 优先尝试 `type_text(...)`。 3. 若 `type_text(...)` 失败，或文本没有真正进入输入框，立即切到兜底链路： - `set_clipboard(...)` - 长按当前截图里的输入区域 - 截图确认菜单 - 基于当前截图临时点击 `粘贴` 4. 粘贴后再次确认文本真的进入输入框，再继续后续操作。 ## 坐标原则 - 坐标只对应“当前设备、当前页面、当前截图”。 - 不要把一次成功的坐标写成固定流程。 - 如果页面一变、键盘弹出、工具栏切换，之前的坐标立即失效，应重新截图。 ## 失败排查 按下面顺序定位失败点： 1. 是否在正确页面。 2. 是否点中了真实控件，而不是附近空白区域。 3. 输入框是否真的聚焦。 4. 文字是否真的进入输入框。 5. 发送按钮是否真的出现且可点击。 6. 操作后界面是否真的变化。 7. 某条路径失败后，是否及时切换到更合适的兜底路径，而不是重复试错。 ## 微信等脆弱场景 - 聊天发送属于脆弱流程，默认一步一验。 - `press_enter` 只能作为兜底，不要把它当作“必然发送成功”的主路径。 - 微信自定义弹窗通常不在无障碍树中，遇到菜单项时应截图后临时取坐标点击。 ## 参考 - 需要工具说明时，读 `tools-reference.md`。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: gumadeiras/gotrain (version 1.3.1)

--- name: gotrain description: MTA system train departures (NYC Subway, LIRR, Metro-North). Use when the user wants train times, schedules, or service alerts for MTA transit. Covers MTA Subway, LIRR, and Metro-North across the greater New York area. metadata: {"clawdbot":{"requires":{"bins":["gotrain"]},"install":[{"id":"node","kind":"node","package":"gotrain","bins":["gotrain"],"label":"Install gotrain (npm)"}]}} --- # gotrain Atomic CLI for NYC transit departures (MTA Subway, LIRR, Metro-North). ## Installation ```bash npm install -g gotrain ``` ## Commands | Command | Description | |---------|-------------| | `gotrain stations [query]` | List/search stations | | `gotrain departures <station-id>` | Show departures for a station | | `gotrain alerts` | Active service alerts | | `gotrain fav <id>` | Toggle favorite station | | `gotrain favs` | List favorite stations | ## Common Station IDs - `MNR-149` - New Haven - `MNR-151` - New Haven-State St - `MNR-1` - Grand Central - `MNR-203` - Penn Station (MNR) - `LIRR-349` - Grand Central - `SUBWAY-631` - Grand Central-42 St ## Examples ```bash # Search for Penn Station gotrain stations penn # New Haven to Grand Central departures gotrain departures MNR-149 # Check service alerts gotrain alerts # Add favorite station gotrain fav MNR-149 ``` ## Source https://github.com/gumadeiras/gotrain-cli


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: fly3094/tiktok-bulk-publisher-test (version 1.0.0)

# TikTok 批量发布技能 ## 功能概述 TikTok批量视频发布技能，实现自动化上传和发布TikTok视频内容。支持以下功能： - 批量视频上传到TikTok - 自定义视频标题和描述 - 隐私级别设置（公开、互关好友可见、仅自己可见） - 评论、合拍、拼接功能控制 - 分片上传大文件（支持10MB+视频） - 发布状态查询 - OAuth 2.0 授权流程支持 ## 技术要求 - 需要 TikTok Content Posting API 权限 - 需要 `video.publish` 和 `user.info.basic` Scope - 支持 FILE_UPLOAD 和 PULL_FROM_URL 两种上传方式 ## 使用方法 ### 环境变量配置 ```bash export TIKTOK_CLIENT_KEY="your_client_key" export TIKTOK_CLIENT_SECRET="your_client_secret" export TIKTOK_ACCESS_TOKEN="your_access_token" ``` ### 命令行使用 ```bash python tiktok_publisher.py --video /path/to/video.mp4 --title "视频标题" --privacy PUBLIC_TO_EVERYONE ``` ### Python API 使用 ```python from tiktok_publisher import TikTokPublisher publisher = TikTokPublisher(client_key, client_secret, access_token) result = publisher.upload_video( video_path="/path/to/video.mp4", title="视频标题", privacy_level="PUBLIC_TO_EVERYONE", disable_comment=False, disable_duet=False, disable_stitch=False ) if result['success']: print(f"发布成功: {result['video_url']}") else: print(f"发布失败: {result['error']}") ``` ## 依赖 - Python 3.7+ - requests - pathlib ## 注意事项 1. 视频文件大小限制：单个视频不超过500MB 2. 视频格式要求：MP4格式，H.264编码 3. 标题长度限制：不超过2200个字符 4. 需要先完成TikTok开发者账号认证和应用创建 5. 访问令牌有效期为2小时，需要定期刷新 ## 错误处理 常见错误代码： - `missing_scope`: 缺少必要的API权限 - `invalid_token`: 访问令牌无效或过期 - `video_too_large`: 视频文件超过大小限制 - `invalid_video_format`: 视频格式不支持


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: yanick112/mcp-server-discovery (version 1.0.0)

--- name: mcp-server-discovery description: Discover, search, and manage MCP (Model Context Protocol) servers. Use when the user needs to find MCP servers, get server information, generate MCP client configurations, or work with the MCP ecosystem. Triggers on queries about MCP servers, Model Context Protocol, server discovery, or MCP configuration. --- # MCP Server Discovery This skill helps you discover and manage MCP (Model Context Protocol) servers. ## What is MCP? Model Context Protocol (MCP) is an open standard that enables AI systems to connect with external data sources and tools. It provides a standardized way for AI assistants to access files, databases, APIs, and other resources. ## Available Commands Use the `scripts/mcp_discover.py` script for all MCP operations: ### List Available Servers ```bash python3 scripts/mcp_discover.py list ``` Filter by category: ```bash python3 scripts/mcp_discover.py list --category database ``` Categories: filesystem, dev, database, web, search, memory ### Search for Servers ```bash python3 scripts/mcp_discover.py search --query "database" ``` ### Get Server Details ```bash python3 scripts/mcp_discover.py info --name postgres ``` ### Generate MCP Client Configuration ```bash python3 scripts/mcp_discover.py config --servers "filesystem,github,memory" ``` ## Common Workflows ### Setting up a new MCP client 1. List available servers to see options 2. Select the servers you need 3. Generate configuration with those servers 4. Save the output to your MCP client's config file ### Finding the right server 1. Use `search` with keywords related to your need 2. Use `info` to get detailed information about a specific server 3. Check the install command and URL for setup instructions ## Server Categories - **filesystem**: File system access and management - **dev**: Development tools and integrations (GitHub, etc.) - **database**: Database connections (PostgreSQL, SQLite) - **web**: Web scraping and content fetching - **search**: Search engine integrations - **memory**: Persistent memory and knowledge graph ## JSON Output All commands support `--json` flag for programmatic use: ```bash python3 scripts/mcp_discover.py list --json ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: amateur0x1/my-mind-manager (version 1.0.0)

--- name: my-mind-manager description: | my-mind 灵感文章创作管理技能。用于管理灵感、文章和创作素材。 触发语包括：帮我管理my-mind、创建灵感、整理文章、发布文章、创作素材管理、初始化目录结构、my-mind 可识别关键词：my-mind、灵感管理、文章创作、素材管理、笔记管理、目录初始化 --- # my-mind Manager Skill 帮助管理灵感、文章和创作素材的 AI 技能。 ## 功能概述 ### 1. 初始化目录结构 自动创建标准的 my-mind 目录结构： ``` my-mind/ ├── ideas/ # 灵感库 │ ├── fleeting/ # 瞬时灵感 │ ├── structured/ # 整理后的灵感 │ └── projects/ # 项目构想 ├── articles/ # 文章库 │ ├── drafts/ # 草稿 │ ├── published/ # 已发布 │ └── ideas/ # 文章构想 ├── assets/ # 创作素材 │ ├── images/ │ ├── audios/ │ ├── videos/ │ └── attachments/ ├── inbox/ # 收集箱 │ ├── temp/ │ └── to-process/ └── archive/ # 归档 ``` ### 2. 灵感管理 - **记录灵感**：快速记录碎片想法 - **整理灵感**：将瞬时灵感整理成结构化内容 - **归档灵感**：将完成的项目构想归档 ### 3. 文章创作 - **创建草稿**：基于灵感创建新文章 - **编辑文章**：帮助续写、修改文章 - **发布文章**：标记为已完成/已发布 ### 4. 素材管理 - 整理和归类素材 - 移动素材到正确位置 - 清理临时文件 --- ## 使用流程 ### 方式一：初始化新仓库 当用户说"初始化 my-mind"或"创建目录结构"时： 1. 创建目录结构 2. 生成 README.md 使用指南 3. 提交代码 ### 方式二：记录灵感 当用户说"记录灵感"、"有个想法"时： 1. 询问灵感内容 2. 按日期创建文件到 `ideas/fleeting/` 3. 询问是否需要整理 ### 方式三：写作文章 当用户说"写文章"、"创作"时： 1. 询问文章主题和方向 2. 创建草稿到 `articles/drafts/` 3. 逐步完善内容 ### 方式四：发布文章 当用户说"发布文章"、"完成文章"时： 1. 确认文章已完成 2. 移动到 `articles/published/` 3. 按日期重命名 --- ## 命名规范 ### 灵感 - 瞬时灵感：`YYYY-MM-DD-简短描述.md` - 整理后：`YYYY-MM-DD-主题.md` - 项目：`项目名-阶段.md` ### 文章 - 草稿：`文章名-draft.md` - 定稿：`YYYY-MM-DD-文章名.md` --- ## 重要规则 1. 所有文件使用 Markdown 格式 2. 灵感文件优先放入 `fleeting/`，再根据需要整理到 `structured/` 3. 文章发布前确认用户意图 4. 素材移动前确认目标路径 5. 定期提醒用户清理 `inbox/to-process/` --- ## 配套工具 - Obsidian - Notion - 任意 Markdown 编辑器


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: leocdchina/zhongguo-nongli-query (version 1.0.2)

--- name: zhongguo-nongli-query license: MIT homepage: https://nongli.skill.4glz.com repository: https://github.com/Leocdchina/huangli-agent-skills publisher: Leocdchina version: 1.0.2 compatibility: Requires Python 3.9+ or bash with curl. Set required HUANGLI_TOKEN env var (Bearer token from https://nongli.skill.4glz.com/dashboard). Optional HUANGLI_BASE env var overrides API base. Needs HTTPS outbound access to api.nongli.skill.4glz.com. required_env: - HUANGLI_TOKEN (required) - HUANGLI_BASE (optional) outbound_hosts: - api.nongli.skill.4glz.com description: | 中国农历日期换算工具。专注公历转农历、阴历日期换算、农历年月日、生肖、干支查询。适合已明确要做日期换算的场景；若还要查黄历宜忌、吉凶判断、择日，请优先使用 zhongguo-nongli-huangli-jixiong 统一技能。 --- # 中国农历日期换算 / 公历转农历 这是一个 **更窄的日期换算技能**，重点是： - 公历转农历 - 阴历日期换算 - 农历年月日 - 生肖、干支 它**不是**主要面向黄历宜忌或吉凶择日的统一入口。 如果用户要查： - 今日黄历 - 黄历宜忌 - 吉凶判断 - 搬家 / 结婚 / 开业 / 开工择日 请优先使用：`zhongguo-nongli-huangli-jixiong` ## 适用问题 - 2027-08-08 对应的农历日期是什么？ - 某个公历日期换算成中国农历是多少？ - 某一天的农历年月日、生肖、干支是什么？ ## 使用方式 ```bash # 1）查询单日农历日期换算 python3 toolkit.py by-date 2027-08-08 # 2）批量查询一个时间段内的农历日期 python3 toolkit.py batch 2027-08-01 2027-08-31 # 3）按关键词检索特殊日期 python3 toolkit.py search 甲子日 --year 2027 ``` ## 触发词建议 - 公历转农历 - 农历日期换算 - 阴历日期换算 - 中国农历日期 - 生肖干支查询 ## 环境变量 ```bash export HUANGLI_TOKEN="***" export HUANGLI_BASE="https://api.nongli.skill.4glz.com" ``` Token 获取地址： - https://nongli.skill.4glz.com/dashboard


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: rubenfb23/arxiv-watcher-vigo (version 1.0.0)

--- name: arxiv-watcher description: Search and summarize papers from ArXiv. Use when the user asks for the latest research, specific topics on ArXiv, or a daily summary of AI papers. --- # ArXiv Watcher This skill interacts with the ArXiv API to find and summarize the latest research papers. ## Capabilities - **Search**: Find papers by keyword, author, or category. - **Summarize**: Fetch the abstract and provide a concise summary. - **Save to Memory**: Automatically record summarized papers to `memory/RESEARCH_LOG.md` for long-term tracking. - **Deep Dive**: Use `web_fetch` on the PDF link to extract more details if requested. ## Workflow 1. Use `scripts/search_arxiv.sh "<query>"` to get the XML results. 2. Parse the XML (look for `<entry>`, `<title>`, `<summary>`, and `<link title="pdf">`). 3. Present the findings to the user. 4. **MANDATORY**: Append the title, authors, date, and summary of any paper discussed to `memory/RESEARCH_LOG.md`. Use the format: ```markdown ### [YYYY-MM-DD] TITLE_OF_PAPER - **Authors**: Author List - **Link**: ArXiv Link - **Summary**: Brief summary of the paper and its relevance. ``` ## Examples - "Busca los últimos papers sobre LLM reasoning en ArXiv." - "Dime de qué trata el paper con ID 2512.08769." - "Hazme un resumen de las novedades de hoy en ArXiv sobre agentes." ## Resources - `scripts/search_arxiv.sh`: Direct API access script.

path: scripts/search_arxiv.sh
content: #!/usr/bin/env bash
# scripts/search_arxiv.sh
QUERY=$1
COUNT=${2:-5}
# Use curl to query ArXiv API
curl -sL "https://export.arxiv.org/api/query?search_query=all:$QUERY&start=0&max_results=$COUNT&sortBy=submittedDate&sortOrder=descending"


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