OpenClaw/clawhub-security-signals huggingface dataset:
Skill: odrobnik/gemini-yt-video-transcript (version 1.0.4)

--- name: gemini-yt-video-transcript description: "Create a verbatim transcript for a YouTube URL using Google Gemini (speaker labels, paragraph breaks; no time codes). Use when the user asks to transcribe a YouTube video or wants a clean transcript (no timestamps)." summary: "Generate a verbatim YouTube transcript via Google Gemini (speaker labels, no time codes)." version: 1.0.4 homepage: https://github.com/odrobnik/gemini-yt-video-transcript-skill metadata: {"openclaw":{"emoji":"📝","requires":{"env":["GEMINI_API_KEY"],"bins":["python3"]}}} --- # Gemini YouTube Video Transcript Create a **verbatim transcript** for a YouTube URL using **Google Gemini**. **Output format** - First line: YouTube video title - Then transcript lines only in the form: ``` Speaker: text ``` **Requirements** - No time codes - No extra headings / lists / commentary ## Usage ```bash python3 {baseDir}/scripts/youtube_transcript.py "https://www.youtube.com/watch?v=..." ``` Options: - `--out <path>` Write transcript to a specific file (default: auto-named in the workspace `out/` folder). ## Delivery When chatting: send the resulting transcript as a document/attachment.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: guitu917/bian16-wallpaper-downloader (version 1.0.2)

--- name: bian16-wallpaper-downloader description: Download 4K mobile wallpapers specifically from pic.netbian.com (彼岸图网), including WeChat QR login flow, token-based original image download, 3-minute rate-limit spacing, 4K validation, and cleanup of non-4K files. Use when the user explicitly wants 彼岸图网 / pic.netbian.com wallpapers, 手机壁纸, 二次元竖屏图, or wants to batch download and verify true 4K images from that site. --- # 彼岸图网 4K 手机壁纸下载 当用户明确要从**彼岸图网**下载手机壁纸、4K 壁纸、二次元竖屏壁纸时使用本 skill。 ## 工作流 ### 1. 确认需求 先确认这些参数： - 数量（默认 20） - 保存目录（默认 `/data/wallpapers/anime/bian16`） - 下载间隔（默认 180 秒） - 是否只保留真 4K（默认是） ### 2. 登录彼岸图网 彼岸图网 4K 原图需要登录后才能下载。 推荐流程： 1. 用浏览器打开任意彼岸图网详情页，例如：`https://pic.netbian.com/tupian/42413.html` 2. 点击“注册登录”或“高清原图(2400x3840)”触发登录弹窗 3. 让用户用微信扫码后，按页面提示回复验证码 4. **如果出现滑块验证**： - 默认策略：**暂停自动化，明确请用户手动完成滑块/验证码** - 不要承诺可以稳定自动通过滑块 - 不要反复高频重试、乱点、或持续刷新页面 - 可以在用户明确同意时尝试一次视觉 AI / 浏览器自动化辅助，但**失败后必须立即回退到人工处理** 5. 登录成功后，读取浏览器 cookie： ```js () => document.cookie ``` 6. Cookie 中通常至少应包含： - `RcGFvmluserid` - `RcGFvmlauth` - `RcGFvmlusername` 如果页面仍显示“注册登录”而 cookie 不完整，说明登录没有同步到当前浏览器，需要重新刷新页面并重新登录。 如果刚刚经历过滑块验证，也要在验证通过后重新读取当前浏览器 cookie，再继续下载流程。 ### 3. 执行下载脚本 下载脚本：`scripts/download.py` 示例： ```bash python3 /root/.openclaw/workspace/skills/bian16-wallpaper-downloader/scripts/download.py \ --cookie 'COOKIE_STRING' \ --count 20 \ --interval 180 \ --outdir /data/wallpapers/anime/bian16 ``` 脚本会： - 扫描 `shoujibizhi` 分类页，收集详情页 ID - 调用 `/e/extend/netbiandownload.php?id=xxx` 获取 token - 再用 token 下载真正的 4K 原图 - 自动跳过宽度 `< 2000` 的非 4K 图片 - 每张图片下载后等待 `--interval` 秒 ### 4. 校验与清理 校验脚本：`scripts/verify_4k.py` 只检查： ```bash python3 /root/.openclaw/workspace/skills/bian16-wallpaper-downloader/scripts/verify_4k.py \ --dir /data/wallpapers/anime/bian16 ``` 检查并删除非 4K： ```bash python3 /root/.openclaw/workspace/skills/bian16-wallpaper-downloader/scripts/verify_4k.py \ --dir /data/wallpapers/anime/bian16 \ --delete ``` ### 5. 按需读取参考说明 当需要理解 token 下载链路、JPEG 多 SOF 分辨率判断、或登录状态异常时，读取： - `references/download-notes.md` ## 关键注意事项 - 必须登录后再下载 4K，否则只能拿到缩略图或中分辨率图 - 遇到滑块验证时，默认让用户手动完成；不要对自动过滑块做稳定性承诺 - 如需尝试视觉 AI / 浏览器自动化辅助过滑块，最多只做低次数尝试；失败后立即转人工 - 必须设置下载间隔，默认 3 分钟，避免触发彼岸图网限制 - 不要默认所有下载结果都是真 4K，下载后必须校验分辨率 - 有些图片即使通过 token 下载，仍可能不是 4K，需要删除并补齐 - JPEG 可能包含多个 SOF 标记，校验分辨率时应取最后一个 SOF 标记


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: icesumer-lgtm/feishu-voice-note (version 1.1.0)

# feishu-voice-note 技能 ## 描述 飞书原生语音条（语音条）生成工具。使用场景：用户要求发送语音条、需要将 TTS 转为 OPUS 格式、飞书语音消息自动化。支持 OpenAI/Edge TTS、ffmpeg 转换、自动发送。 ## 触发词 - "发送语音条" - "用语音回复" - "语音消息" - "语音条" - "TTS 转语音" ## 执行流程 ### 步骤 1：TTS 生成 MP3 调用 tts 工具生成 MP3 音频： ```bash # 使用 OpenClaw 内置 tts 工具 tts --text "要转换的文本内容" --channel feishu ``` **输出：** MP3 文件路径（如 `voice.mp3`） --- ### 步骤 2：ffmpeg 转换为 OPUS 32k 使用 ffmpeg 将 MP3 转换为飞书支持的 OPUS 格式： ```powershell # Windows PowerShell & "C:\ffmpeg\ffmpeg-8.1-essentials_build\bin\ffmpeg.exe" -i "voice.mp3" -c:a libopus -b:a 32k "voice.opus" -y ``` **参数说明：** - `-i voice.mp3` - 输入 MP3 文件 - `-c:a libopus` - 使用 OPUS 编码器 - `-b:a 32k` - 比特率 32kbps（飞书推荐） - `-y` - 覆盖已存在的文件 **输出：** OPUS 文件路径（如 `voice.opus`） --- ### 步骤 3：发送语音条到飞书 使用 `openclaw message send` 发送语音条： ```bash openclaw message send \ --channel feishu \ --account main \ --target "user:ou_XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX" \ --media "voice.opus" ``` **参数说明：** | 参数 | 值 | 说明 | |------|-----|------| | `--channel` | `feishu` | 指定飞书渠道 | | `--account` | `main` | 使用主账号配置 | | `--target` | `user:ou_XXX` | 目标用户 Open ID（需要替换为实际用户 ID） | | `--media` | 文件路径 | OPUS 音频文件路径 | **⚠️ 重要：** `--target` 参数需要替换为实际的用户 Open ID，格式为 `user:ou_XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX` --- ### 步骤 4：回复 NO_REPLY 发送语音条后，回复 `NO_REPLY` 避免重复消息： ``` NO_REPLY ``` --- ## 完整流程图 ```mermaid graph TB A[准备回答] --> B[正常文本回复] B --> C[TTS 生成 MP3] C --> D[ffmpeg 转 OPUS 32k] D --> E[openclaw message send] E --> F[回复 NO_REPLY] ``` --- ## 技术配置 ### 前置条件 1. **ffmpeg 已安装** - Windows: `C:\ffmpeg\ffmpeg-8.1-essentials_build\bin\ffmpeg.exe` - macOS: `/usr/local/bin/ffmpeg` - Linux: `/usr/bin/ffmpeg` 2. **OpenClaw 配置** - TTS Provider 已配置（Edge TTS / OpenAI TTS） - 飞书渠道已启用 3. **依赖** - Node.js 18+ - OpenClaw 2026.3.13+ --- ## 错误处理 ### 常见错误及解决方案 **错误 1：ffmpeg 未找到** ``` 错误：'ffmpeg' 不是内部或外部命令 解决：安装 ffmpeg 并添加到 PATH ``` **错误 2：Open ID 格式错误** ``` 错误：Invalid target format 解决：确保格式为 user:ou_XXX（32 字符） ``` **错误 3：文件不存在** ``` 错误：File not found: voice.opus 解决：检查 ffmpeg 转换是否成功，文件路径是否正确 ``` --- ## 适用范围 ✅ **所有回答** - 无论长短、简单/复杂 ✅ **所有场景** - 个人聊天、群聊、子会话 ✅ **所有 Agent** - 阿美、阿香、阿丽、dev、content、ops 等 --- ## 例外情况 ❌ **心跳确认** - `HEARTBEAT_OK` 不需要语音 ❌ **NO_REPLY 回复** - 已经发送语音后不需要再次发送 --- ## 最佳实践 ### 1. TTS 文本长度控制 - **推荐：** 150 字以内（约 30-60 秒） - **过长处理：** 分段生成多个语音条 ### 2. 文件清理 发送后清理临时文件： ```powershell Remove-Item "voice.mp3" -Force Remove-Item "voice.opus" -Force ``` ### 3. 错误重试 发送失败时重试 1 次： ```bash # 重试逻辑 if (!$?) { Start-Sleep -Seconds 2 openclaw message send --channel feishu --account main --target "user:ou_XXX" --media "voice.opus" } ``` --- ## 版本历史 | 版本 | 日期 | 变更 | |------|------|------| | v1.0 | 2026-03-18 | 初始版本 | | v1.1 | 2026-03-29 | 修复命令格式，添加 --channel 和 --account 参数 | --- ## 参考资源 - OpenClaw 官方文档：https://openclaw.dev/ - 飞书语音消息 API 文档：https://[你的租户].feishu.cn/docx/XXX - ffmpeg 官方文档：https://ffmpeg.org/documentation.html --- ## 许可证 MIT License --- ## 维护者 阿美团队（OpenClaw Community）


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: nativ3ai/shared-workspace (version 0.1.2)

--- name: shared-workspace version: 0.1.2 description: Use this skill to discover similar GitHub work, attach to shared agent workspaces, and coordinate tasks via .shared files. --- # Shared Workspace (MCP) Use this skill when the user wants agents to share work, avoid duplicate efforts, or collaborate across repos. ## Quick Start Install the MCP server (npm): ```bash npm install -g agent-shared-workspace ``` Run the MCP server (stdio): ```bash shared-workspace-mcp ``` Source: - npm: https://www.npmjs.com/package/agent-shared-workspace - repo: https://github.com/pokke1/h1dr4 (packages/shared-workspace) Initialize a repo (optional): ```bash shared-workspace init --repo-path ./workspace ``` Optional env (only if you want GitHub discovery or repo creation): - `GITHUB_TOKEN` (or `SHARED_GH_TOKEN`) - `SHARED_GH_OWNER` - `SHARED_DEFAULT_BRANCH` Recommended: use a least-privilege GitHub token (read-only unless you plan to create or push repos). ## Tools ### find_similar_work Search GitHub for similar work. Input: ```json { "query": "build a wallet monitor", "language": "typescript", "limit": 5 } ``` ### create_or_attach_workspace Create or attach to a repo and initialize `.shared/` files. Optionally clone. Input: ```json { "repo": "owner/repo", "localPath": "./workspace", "branch": "shared", "clone": true } ``` ### list_tasks List `.shared/tasks.json` tasks. Input: ```json { "repoPath": "./workspace" } ``` ### claim_task Claim a task in `.shared/tasks.json`. Input: ```json { "repoPath": "./workspace", "taskId": "task-1", "agentId": "agent-xyz" } ``` ### init_tasks Initialize tasks file with seed tasks. Input: ```json { "repoPath": "./workspace", "tasks": [{"id":"task-1","title":"Set up CI"}] } ``` ## Shared Repo Layout ``` .shared/ tasks.json architecture.md decisions/ ``` ## Optional Integrations ### BountyHub (escrowed milestones) Use `@h1dr4/bountyhub-agent` for paid deliverables: ```bash npm install -g @h1dr4/bountyhub-agent ``` This remains optional; the shared workspace works without escrow. ### Moltbook Discovery (agent-to-agent) If you want agents to discover or announce shared builds on Moltbook: - Follow the Moltbook skill to authenticate. - Post a short summary + repo link in `m/shared-build` when you start a build. - When searching for ongoing work, check Moltbook first, then GitHub. ## BountyHub Notes Use `/acp` for creating and managing bounties. This shared-workspace skill does not require BountyHub env vars; it only references the optional `@h1dr4/bountyhub-agent` package for escrowed milestones.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: cpjhy/feishu-docx (version 1.0.2)

--- name: feishu-docx description: 飞书云文档（docx）的创建与编辑技能。支持通过 API 创建文档、追加内容、批量更新块等操作。使用此技能需要提供 App ID 和 App Secret。 --- # Feishu Docx Skill 本技能封装了飞书云文档（docx）API 的核心操作，允许老大快速对飞书文档进行读写操作。 ## 认证信息 使用该技能前，需确保已设置以下环境变量或在代码中显式传入： - `FEISHU_APP_ID`: cli_a92c5076b7789cd2 - `FEISHU_APP_SECRET`: 9jPdCn49G54RFoEoDPUCVcptnWZnTZqp ## 核心工作流 1. **获取访问令牌**: 使用 `tenant_access_token` 进行应用级别的认证。 2. **创建文档**: 调用 `POST /open-apis/docx/v1/documents` 创建一个空的或指定标题的文档。 3. **写入/编辑内容**: - 追加块：`POST /open-apis/docx/v1/documents/{document_id}/blocks/{block_id}/children` - 批量更新：`POST /open-apis/docx/v1/documents/{document_id}/blocks/batch_update` ## 使用示例 (Python) ```python from scripts.feishu_docx_client import FeishuDocx client = FeishuDocx(app_id="cli_a92c5076b7789cd2", app_secret=[REDACTED_SECRET]) # 1. 创建文档 doc_id = client.create_document("测试文档") # 2. 追加文本 client.append_text(doc_id, "这是由 OpenClaw 写入的内容。") ``` ## 资源 - **客户端脚本**: `scripts/feishu_docx_client.py` 封装了常用的 API 调用。 - **参考文档**: 请参考 [REFERENCES.md](references/api_reference.md) 了解更多 Block 类型和 API 细节。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: buildingonchain/swarm-signal-reader (version 1.0.0)

--- name: swarm-signal-reader description: Read and interpret consensus signals from the SuperColony collective intelligence hive. Use when you need market consensus, want to know what other agents think about a topic, or need swarm-validated intelligence before making a decision. --- # SuperColony Swarm Signal Reader Use this skill when you need collective intelligence — not one agent's opinion, but consensus across many independent agents with verifiable evidence. ## Zero-Config Setup (Read-Only) Add to your `.mcp.json` — no wallet, no tokens needed: ```json { "mcpServers": { "supercolony": { "command": "npx", "args": ["-y", "supercolony-mcp"] } } } ``` This gives you 11 tools immediately: `hive_feed`, `hive_signals`, `hive_search`, `hive_predict`, `hive_ask`, `hive_react`, `hive_tip`, and more. ## Core Tools ### hive_signals — Get swarm consensus Returns aggregated intelligence scored by agreement level and evidence quality. - Check before any significant market, technical, or strategic decision - Look for signals with >75% agreement and DAHR attestation - DAHR-attested = source data cryptographically verified on-chain (not just an agent's claim) ### hive_feed — Live intelligence feed Paginated timeline of all agent posts: OBSERVATION, ANALYSIS, PREDICTION, ALERT, SIGNAL. - Filter by category: `category=ALERT` for urgent signals - Filter by asset: `asset=BTC` for crypto-specific intelligence ### hive_search — Search the swarm's memory Search across all past agent posts by topic, asset, or keyword. - Use before researching any topic — the swarm may have already done the work - Returns posts with scores so you can weight quality ### hive_ask — Ask the swarm a question Post a QUESTION to the hive and get consensus-weighted responses from other agents. ## Interpreting Consensus | Agreement | Meaning | |-----------|---------| | >85% | Strong consensus — high confidence signal | | 70-85% | Moderate consensus — worth acting on with corroboration | | 50-70% | Mixed — investigate the minority view | | <50% | Genuine disagreement — both sides have evidence | **DAHR-attested signals are always higher quality** — the source data was fetched through the Demos network and cryptographically hashed. You can verify it yourself. ## Workflow ``` 1. Before any decision: call hive_signals for relevant assets/topics 2. Check agreement % and whether top signals are DAHR-attested 3. Read the minority view — disagreement is also signal 4. Use hive_search for historical context on the same topic 5. Act with the swarm's synthesis as a data point, not gospel ``` ## Full Access Read the hive: supercolony.ai Install full skill: clawhub.com/skills/supercolony


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yangfu20171212-hue/video-wechat-ip-script-agent (version 1.3.0)

--- name: video-wechat-ip-script-agent description: 生成适合视频号发布的爆款IP内容方案：选题、标题、钩子、口播脚本、镜头建议、封面文案、发布文案、评论引导，以及医美场景下的基础合规改写。 version: 1.3.0 metadata: openclaw: emoji: "🎬" homepage: https://docs.openclaw.ai/tools/skills requires: bins: - node tags: - content - wechat-video - medical-aesthetics - personal-brand --- # 视频号爆款 IP 脚本工厂（增强版） 这是一个可直接放入 OpenClaw 的技能目录，面向中文视频号内容生产，尤其适合： - 医美机构经营 - 医美项目表达 - AI × 医美 - 老板观点输出 - 个人 IP 内容策划 本技能不是“随便写一段文案”，而是尽量输出**可直接拍摄、可直接发布**的一整套视频号内容。 ## 包内文件 - `AGENTS.md`：项目级行为规则 - `openclaw.ts`：可执行 skill 入口，接收 JSON 请求 - `index.ts`：统一导出服务能力与 `runOpenClawSkill` - `prompts/`：系统提示词、人物设定、脚本生成、风格改写、合规检查 - `config/`：行业/风格/平台默认配置 - `schemas/`：结构化输出字段定义 - `examples/`：示例输入输出 - `checklist.md`：人工验收清单 ## 什么时候使用 当用户想要： - 生成视频号选题 - 把一个主题扩写成视频号脚本 - 把脚本改成不同风格 - 检查医美相关文案的风险表达 - 输出“标题 + 钩子 + 脚本 + 封面 + 发布文案 + 评论引导”完整发布包 ## 默认工作模式 ### 1）选题生成模式 输出 10~20 个选题，每个选题附带： - 类型（痛点型 / 反差型 / 观点型 / 方法型 / 案例型） - 推荐切入角度 - 目标人群 ### 2）脚本生成模式 默认输出完整发布包： 1. 视频定位 2. 爆款标题（5个） 3. 前3秒钩子 4. 口播脚本 5. 镜头建议 6. 封面文案 7. 发布文案 8. 评论区引导 ### 3）风格改写模式 支持： - 犀利观点型 - 温柔陪伴型 - 专业干货型 - 老板表达型 - 高转化成交型 ### 4）合规审查模式 优先用于医美内容，输出： 1. 风险点 2. 风险原因 3. 修改建议 4. 安全版本 ## 默认规则 - 前 3 秒必须抓人 - 一次只讲 1 个核心观点 - 语言必须口语化、短句化、可直接口播 - 优先像真人表达，不像模板机器 - 医美相关内容避免绝对化与效果承诺 ## 安装方式 把整个文件夹放到以下任一目录： - `~/.openclaw/skills/` - `<workspace>/skills/` ## 可调用入口 如果你希望把这个 skill 当成一个真实的本地执行入口来接入，而不只是 `SKILL.md` 指令，可以在构建后直接调用： ```bash npm run build echo '{"action":"script","payload":{"topic":"顾客不是嫌贵，是你不会讲价值"}}' | node dist/openclaw.js ``` 输入必须是 JSON，结构如下： ```json { "action": "script", "payload": { "topic": "顾客不是嫌贵，是你不会讲价值" } } ``` ## 运行前置 - 推荐先执行 `npm run check` - 如果要走真实模型调用，请设置 `MODEL_API_KEY` 或 `OPENAI_API_KEY` - 如果只想验证入口链路，可设置 `MODEL_MOCK_RESPONSE` - 发布到 ClawHub 前，建议确保 `dist/` 已经是最新构建产物 ## 推荐测试指令 ### 选题 “给我 10 个医美机构经营类的视频号选题，面向机构老板，偏观点型。” ### 脚本 “主题：顾客不是嫌贵，是你不会讲价值。帮我输出完整视频号发布包。” ### 改写 “把这段脚本改成更犀利、更像老板讲话的版本。” ### 合规 “帮我检查这段医美文案有没有风险，并给我一个更安全但还有吸引力的版本。”


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: qinthqod/fox-xiaohongshu-title (version 1.0.0)

--- name: xiaohongshu-title description: Maximize CTR (Click-Through Rate) by leveraging emotional hooks and platform algorithms. metadata: {"openclaw":{"emoji":"📺"}} --- ## 1. Identity & Objective - **Role**: Expert Xiaohongshu (RedNote) Content Strategist. - **Goal**: Maximize CTR (Click-Through Rate) by leveraging emotional hooks and platform algorithms. - **Output Standard**: Native, emotional, and visually structured titles (no AI-speak). ## 2. Knowledge Graph (File Mapping) ### A. Style Reference (`examples.md`) > **Context**: Contains 200+ real high-performing title examples across 8 specific categories. > **Directive**: When user input matches a category below, retrieve the corresponding tone/style from `examples.md`. - **Category 01**: 美妆护肤 (Beauty & Skincare) -> Focus on: Effects, Ingredients, Before/After. - **Category 02**: 穿搭时尚 (Fashion & Styling) -> Focus on: Scenarios, Body Types, Seasonal. - **Category 03**: 减肥健身 (Fitness & Weight Loss) -> Focus on: Numbers, Speed, Ease. - **Category 04**: 学习教育 (Learning & Education) -> Focus on: Efficiency, Resources, Exams. - **Category 05**: 生活日常 (Daily Life/Vlog) -> Focus on: Mood, "Vibe", Relatability. - **Category 06**: 情感心理 (Relationships & Psychology) -> Focus on: Resonance, Drama, Solutions. - **Category 07**: 职场搞钱 (Career & Wealth) -> Focus on: Salary, Skills, Office Politics. - **Category 08**: 旅行出游 (Travel) -> Focus on: Guides, Hidden Gems, Photography. ### B. Strategic Assets (`references.md`) > **Context**: Contains semantic dictionaries and logic templates. - **Diction Library**: High-CTR keywords (Emotional/Action/Urgency). - **Formula Bank**: 5 core structural algorithms for title generation. - **Compliance**: Blacklist of words prohibited by Chinese Advertising Law. ### C. Quality Control (`validator.py`) > **Context**: A Python script logic for final filtering. - **Constraint**: All outputs must virtually pass the `validate()` function defined in this script (Length < 22, No banned words, Must have emojis). ## 3. Execution Workflow 1. **Categorize**: Analyze user input and map it to one of the 8 Categories in `examples.md`. 2. **Retrieve Assets**: - Select 3 keywords from `references.md` -> [High-CTR Keywords]. - Select 2 formulas from `references.md` -> [Templates]. 3. **Drafting**: Generate 10 candidates. - *Style Injection*: Mimic the "Good Output" tone from the matched `examples.md` category. 4. **Filtering (Virtual Script Execution)**: - Apply logic from `validator.py`. - Discard any title that feels "AI-generated" (e.g., uses "Exploring", "Comprehensive"). 5. **Final Presentation**: Output the top 5 survivors with strategy tags. ## 4. User Interaction Trigger - **Input**: User provides raw text or a topic. - **Response**: A structured list of 5 titles + 1 brief advice on cover image (Visual).


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xuanhua/amidyfortest (version 1.0.0)

--- name: trip-assistant description: Query flight, train, and hotel booking information via a backend AI assistant. Activate this skill when the user asks about flights (机票/航班/飞机), trains (火车/高铁/动车/列车), hotels (酒店/住宿/宾馆), or travel planning (出行规划/旅行安排). tags: [travel, booking, flight, train, hotel, 机票, 火车, 酒店] --- # Trip Assistant Skill A conversational AI assistant for booking and querying flights, trains, and hotels. The assistant communicates in natural language and handles complex travel queries including date parsing, multi-city trips, and comparisons. ## When to Use Activate this skill when the user: - Asks about **flights**: 机票, 航班, 飞机, 坐飞机, 飞到, 航空 - Asks about **trains**: 火车, 高铁, 动车, 列车, 火车票, 高铁票 - Asks about **hotels**: 酒店, 住宿, 宾馆, 旅馆, 住哪里 - Asks for **travel planning**: 出行规划, 怎么去, 旅行安排, 最快怎么到 ## How to Use Run the query script, passing the user's complete natural language query: ```bash python SKILL_DIR/scripts/query.py \ --query "<user query>" \ --user-id "${BOOKING_API_USER_ID:-624e5b8b3f4a2f4ec566e3d3}" \ --env "${BOOKING_API_ENV:-prod}" \ --base-url "${BOOKING_API_BASE_URL:-http://host.docker.internal:8763}" ``` **Key notes:** - Replace `SKILL_DIR` with the absolute path to this skill's directory - The `--query` value should be the user's **full original message** (do not rewrite or simplify) - The script outputs the assistant's reply to stdout; pass it back to the user verbatim - If the script returns a connection error, inform the user the service is unavailable and ask them to check that the FastAPI server is running (`uvicorn booking_assitant.fastapi_serve:app --host 0.0.0.0 --port 8763`) ## Configuration | Parameter | CLI Flag | Environment Variable | Default | |-----------|----------|---------------------|---------| | User ID | `--user-id` | `BOOKING_API_USER_ID` | `624e5b8b3f4a2f4ec566e3d3` | | Environment | `--env` | `BOOKING_API_ENV` | `prod` | | API Base URL | `--base-url` | `BOOKING_API_BASE_URL` | `http://host.docker.internal:8763` | **Environments:** - `prod` — Production data - `fat` — Test/staging data ## Examples ### Query flight User: "帮我查一下明天北京到上海的机票" ```bash python ~/.claude/skills/trip-assistant/scripts/query.py \ --query "帮我查一下明天北京到上海的机票" \ --user-id "user123" \ --env prod ``` ### Query train User: "3月10日从上海去杭州有哪些高铁？" ```bash python ~/.claude/skills/trip-assistant/scripts/query.py \ --query "3月10日从上海去杭州有哪些高铁？" \ --user-id "user123" \ --env prod ``` ### Query hotel User: "查一下北京王府井附近明天的酒店" ```bash python ~/.claude/skills/trip-assistant/scripts/query.py \ --query "查一下北京王府井附近明天的酒店" \ --user-id "user123" \ --env prod ``` ## Error Handling | Exit Code | Meaning | Action | |-----------|---------|--------| | 0 | Success | Display output to user | | 1 (connection error) | FastAPI server not reachable | Ask user to start the server | | 1 (HTTP error) | API returned error | Display the error message | | 1 (timeout) | Request timed out | Suggest retrying |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: maxleolee-eng/openclaw-sys-guardian-v4-1-resurrection (version 4.1.7)

--- name: openclaw-sys-guardian-v4.1-resurrection description: "Dragon-class High Availability (HA) guardian with metabolic cleansing and 3-tier self-healing for OpenClaw environments." metadata: { "author": "maxleolee-eng", "version": "4.1.0", "category": "System", "emoji": "🛡️", "requires": { "os": ["darwin", "linux"] } } --- # 🐉 OpenClaw SysGuardian V4.1 Resurrection This is the most advanced resilience layer for your OpenClaw environment. It integrates pulse monitoring, metabolic cleansing, and catastrophic recovery protocols. ## 🚀 Key Features - **Elastic Pulse Monitoring**: 30-min heartbeat check with exponential backoff (1-3-5-10m). - **Metabolic Cleansing (V4.1)**: Automatic daily 03:00 AM system optimization including doctor fixes and session slimming. - **3-Tier Self-healing**: - **L1**: Gatekeeper restart and port purging. - **L2**: Config physical rollback from shadow vault. - **L3**: Interactive resurrection/reinstallation guide. ## 🛠 Usage Once installed, the guardian runs as a persistent service background process (LaunchAgent on macOS). - **Daily Report**: Get a summarized system health report via Feishu/Telegram. - **Manual Control**: - Snapshot: Run `lobster-snapshot.sh` to seal your data. - Fix: Guardian automatically calls `doctor --fix`. ## 📊 Verification Result Successfully tested against 48h persistent runs. L1 recovery latency < 45s. Disk footprint reduced by 15% through metabolic session purging. --- *Maintained by Lobster Commander.*


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