OpenClaw/clawhub-security-signals huggingface dataset:
Skill: gnview/gnview-kit (version 1.0.1)

--- name: gnview-kit description: 抖音数据分析工具集，包含视频数据分析、用户画像分析、趋势统计等完整分析能力。适用于抖音数据深度挖掘和报告生成场景。 --- # gnview-kit ## 功能概述 本技能集为抖音视频数据分析与飞书多维表格同步的完整解决方案，核心能力包括： - 抖音视频元数据获取与下载 - 抖音视频脚本/字幕提取与分析 - 飞书多维表格数据校验与写入 - 分析结果云文档关联与同步 ## 核心流程 1. **环境校验**：验证飞书多维表格权限与依赖服务状态 2. **数据采集**：通过关联技能获取抖音视频元数据、下载视频、提取脚本内容 3. **数据处理**：标准化分析结果，生成结构化数据 4. **同步写入**：将处理后的数据写入目标飞书多维表格 5. **文档关联**：生成并关联飞书云文档存储详细分析内容 ## 配置说明 所有配置项均统一存放在技能目录下的`config.json`文件中，需在使用前完成配置： | 配置项路径 | 类型 | 示例值 | 说明 | |--------------------------------|--------|----------------------------------------------------------------------|--------------------------| | `global.bitable.base` | 字符串 | https://feishu.cn/base/xxx | 飞书多维表格基础URL | | `global.bitable.table` | 字符串 | tblsh3nGcKBMbb4G | 目标数据表ID | | `global.download.path` | 字符串 | ./download | 视频文件下载保存路径 | | `global.download.name` | 字符串 | $aweme_id.mp4 | 下载文件名模板（支持$aweme_id变量） | ## 关联技能 - gnview-douyin-video-download - gnview-script-extraction - gnview-api-downloader ## 数据字段规范 写入多维表格的标准字段如下： | 字段名称 | 字段类型 | 示例值 | 说明 | |------------------|----------|----------------------------------------------------------------------|--------------------------| | 视频原链接 | 文本 | https://www.douyin.com/video/7615937081646272185 | 原始抖音视频地址 | | 视频ID | 文本 | 7615937081646272185 | 抖音视频唯一标识 | | 抖音号 | 文本 | unique_id(抖音号) | 视频发布者抖音号 | | 发布时间 | 日期 | 1712345678000 | 视频发布时间戳 | | 视频标题 | 文本 | 抖音视频分析教程 | 视频标题内容 | | 下载链接 | 超链接 | http://xxx/api/download?url=https://www.douyin.com/video/7615937081646272185 | 带下载前缀的视频地址 | | 分析文档链接 | 超链接 | https://feishu.cn/docx/[doc_id] | 存储详细分析结果的飞书文档 | ## 注意事项 1. 确保目标飞书多维表格已配置正确的写入权限 2. 依赖的关联技能（gnview-api-downloader）需提前完成配置 3. 所有API调用需遵守抖音开放平台的限流规则，建议添加重试与延迟机制 4. 批量处理时建议添加分页逻辑，避免触发飞书API频率限制 5. 敏感配置信息（如API密钥）请勿直接提交至代码仓库，使用环境变量或加密存储


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nbutmickey/smart-collect (version 1.0.0)

--- name: smart-collect description: 分析和总结URL链接内容，并保存到obsidian中，同时定期提醒复习 --- # Smart Collect - 智能收藏回顾系统 基于艾宾浩斯遗忘曲线的智能收藏系统，支持自动抓取、摘要生成、定时复习提醒。 ## 目录结构 ``` smart-collect/ ├── SKILL.md # 本文件 ├── lib/ │ ├── reviewEngine.js # 艾宾浩斯复习引擎 │ ├── markdownStore.js # Markdown 存储模块 │ ├── nlpParser.js # 自然语言解析模块 │ └── fetcher.js # 网页抓取模块 ├── scripts/ │ ├── shoucang-add.js # 添加收藏脚本 │ ├── shoucang-review.js # 复习回顾脚本 │ └── smart-collect.js # 主入口 └── config.json.example # 配置示例 ``` ## 功能 ### 1. 自动抓取 - 支持 GitHub/微信公众号/小红书/普通网页 - Readability 算法提取正文 - LLM 生成摘要、标签、分类 ### 2. 艾宾浩斯复习 - 间隔：1天 → 2天 → 4天 → 7天 → 15天 - 超过5次自动归档 ### 3. 自然语言管理 - 推迟X天 - 已看完归档 - 添加/删除标签 - 标记已回顾 ## 配置 复制 `config.json.example` 为 `config.json` 并填入： - 收藏存储路径 - 飞书配置 - LLM API 配置 ## OpenClaw Cron 配置 ### 定时规则 **每天 09:30 触发复习** ### cron 表达式 ``` 30 9 * * * ``` ### OpenClaw jobs.json 配置 ```json { "jobs": [ { "id": "smart-collect-review", "name": "智能收藏每日复习", "description": "基于艾宾浩斯曲线触发收藏复习提醒", "enabled": true, "schedule": { "kind": "cron", "expr": "30 9 * * *" }, "sessionTarget": "isolated", "wakeMode": "now", "payload": { "kind": "agentTurn", "message": "请执行智能收藏的每日复习任务，运行命令：cd ~/.openclaw/skills/smart-collect && node scripts/shoucang-review.js" }, "delivery": { "mode": "announce", "channel": "feishu", "to": "ou_你的飞书ID" } } ] } ``` ### 部署步骤 1. 复制配置到 OpenClaw cron 目录： ```bash cp config.json.example config.json # 编辑 config.json 填入你的配置 ``` 2. 添加 cron 任务： ```bash # 编辑 ~/.openclaw/cron/jobs.json 添加上述配置 ``` 3. 重启 Gateway： ```bash openclaw gateway restart ``` --- ## 文件结构 ``` smart-collect/ ├── SKILL.md # 本文件 ├── lib/ │ ├── reviewEngine.js # 艾宾浩斯复习引擎 │ ├── markdownStore.js # Markdown 存储模块 │ ├── nlpParser.js # 自然语言解析模块 │ └── fetcher.js # 网页抓取模块 ├── scripts/ │ ├── shoucang-add.js # 添加收藏脚本 │ ├── shoucang-review.js # 复习回顾脚本 │ └── smart-collect.js # 主入口 └── config.json.example # 配置示例 ```


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

--- name: mdnew description: Fetch clean, agent-optimized Markdown from any URL using the markdown.new service. Use when web_fetch or browser fails to provide clean content, or when you need a token-efficient version of a web page for deep analysis. --- # mdnew Fetch clean Markdown from any URL using the `markdown.new` three-tier conversion pipeline (Header Negotiation -> Workers AI -> Browser Rendering). ## Usage Run the script with a target URL: ```bash python3 scripts/mdnew.py <url> ``` ## Why use mdnew? 1. **Token Efficiency**: Reduces content size by up to 80% compared to raw HTML. 2. **Clean Data**: Strips boilerplate, ads, and nav menus, leaving only core content. 3. **JS Execution**: Automatically handles JS-heavy pages via Cloudflare Browser Rendering fallback. 4. **Agent-First**: Includes `x-markdown-tokens` tracking to help manage context windows.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dereksongyang/qwen-web-agent (version 1.0.0)

--- name: qwen-web-agent description: "Browser automation for 通义千问 (Qwen) web interface at qianwen.com. Use when the agent needs to ask questions to Qwen AI and get back responses via browser automation. Supports single-turn and multi-turn conversations. Launches a real Chromium browser (headed mode) via Playwright, handles login persistence, sends queries, and captures complete AI replies. Best for: (1) Getting AI answers from Qwen's web platform, (2) Automating Qwen queries from other agents/skills, (3) Integrating Qwen responses into automated workflows." --- # Qwen Web Agent Browser automation for 通义千问 (qianwen.com) using Playwright. Login session persists at `~/.qwen_session/`. ## Quick Start ### Single-turn ```bash python scripts/qwen_agent.py "你的问题" echo "你的问题" | python scripts/qwen_agent.py ``` ### Multi-turn ```bash printf "问题1\n问题2\n关闭" | python scripts/qwen_multi_agent.py ``` ## First Run (Manual Login Required) The first launch opens a Chromium window — complete login in the browser, then the script continues automatically. Subsequent runs reuse the saved session. ## Scripts ### `scripts/qwen_agent.py` Single-turn Q&A. Sends one query, waits for the streaming response to stabilize, saves to `last_output.md`, then cleans up the conversation. **Parameters:** - `--timeout N`: Override timeout in seconds (default 120) ### `scripts/qwen_multi_agent.py` Multi-turn conversation. Reads queries from stdin line by line, maintains conversation context, writes all turns to `qwen_session_history.md`. **Exit commands:** `关闭`, `exit`, `quit`, or EOF ## Environment Requirements - Python 3.x - `pip install playwright python-dotenv` - `playwright install chromium` - Display server (X11/Wayland) or Xvfb for headed mode ## Architecture Both scripts use Playwright async API with a persistent browser context. The response capture logic polls DOM selectors (`#qk-markdown-react`, `.chat-answers-card-wrap`, etc.) until the text stabilizes (streaming detection). Multi-turn additionally tracks response element count to distinguish new responses from cached ones. ## References See `references/使用说明.md` for detailed usage documentation.


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

--- name: Follow description: Monitor content from people, topics, and sources across platforms with smart filtering, tiered alerts, and searchable archives. --- ## Workspace ``` ~/follow/ ├── sources/ # One file per followed entity │ ├── people/ # @naval.md, @dhh.md │ ├── topics/ # ai-safety.md, rust.md │ └── feeds/ # techcrunch.md, hn-frontpage.md ├── archive/ # Captured content by date │ └── YYYY-MM/ ├── alerts.md # Alert configuration └── index.md # Quick status: what's being followed ``` --- ## Quick Reference | Task | Load | |------|------| | Add/configure sources | `sources.md` | | Set up filtering rules | `filtering.md` | | Configure alert tiers | `alerts.md` | | Query archived content | `querying.md` | | Platform-specific setup | `platforms.md` | --- ## Core Loop 1. **Add source**: User names person/topic/feed → create tracking file 2. **Monitor**: Check sources on schedule (cron) or on-demand 3. **Filter**: Apply relevance rules, skip noise 4. **Store**: Archive what matters (summaries, not full dumps) 5. **Alert**: Notify based on tier (immediate/daily/weekly/passive) 6. **Query**: Answer "what did X say about Y?" from archive --- ## Common Patterns | User says | Agent does | |-----------|------------| | "Follow @naval on Twitter" | Create `sources/people/naval.md`, configure Twitter monitoring | | "Track AI safety discussions" | Create topic tracker with keywords across multiple sources | | "What has Competitor X posted this week?" | Query archive, synthesize summary | | "Alert me immediately when Y happens" | Add to high-priority tier in `alerts.md` | | "Give me a weekly digest of everything" | Configure weekly summary in alerts | | "Stop following X" | Archive and mark inactive | --- ## Capture Principles - **Summaries over full content** — save space, stay legal - **Links + timestamps always** — retrievable later - **Context for why it matters** — not just "X posted" - **Deduplicate across sources** — same news from 5 places = 1 entry


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: summerloveqq/call-aida-app (version 1.0.0)

name: aida-llm-invoke description: > 调用 AI 搭（aida）chat-messages 接口。当用户说「调用 aida」「aida LLM」「生成变更报告」「AI 搭」且提供 appid、query、inputs 时使用。 Use when: 调用 aida、AI 搭、生成变更报告、aida LLM 分析。 NOT for: OpenAI、Claude 等公网 LLM。 metadata: openclaw: emoji: "🤖" --- # Aida LLM Invoke ## When to Use - 用户说「调用 aida」「用 aida 生成报告」「AI 搭分析」等 - 用户提供了 appid、query、inputs 三个参数 - 需要调用 aida.vip.sankuai.com 的 chat-messages 接口 ## Instructions 1. 使用 **exec** 或 **bash** 工具运行本技能的 `main.py`： ``` python3 main.py --appid <用户提供的appid> --query "<用户提供的query>" --inputs '<用户提供的inputs的JSON字符串>' ``` 2. 工作目录必须为技能目录（含 main.py 的文件夹），例如： - `~/.openclaw/skills/aida-llm-invoke/`（推荐放 managed 目录） - 或 `~/.openclaw/workspace/skills/aida-llm-invoke/` 3. 若 inputs 含特殊字符，可改用 stdin： ``` echo '{"appid":"xxx","query":"xxx","inputs":{...}}' | python3 main.py ``` 4. 解析输出 JSON 中的 `data` 或 `raw_answer` 返回给用户 ## Safety Rules - appid 必须由用户提供，不可硬编码 - 仅用于 aida 内部服务 ## Common Patterns ```bash # 示例：生成变更报告 python3 main.py --appid app-xxx --query "生成变更报告" --inputs '{"tree":"{}","mis":"user01","triggerType":"manual"}' ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: arubiku/mia-content-creator (version 1.0.0)

--- name: mia-content-creator description: AI agent content creation and monetization across platforms homepage: https://moltbook.com/u/MiaBloomx version: 1.0.0 author: MiaBloomx license: MIT tags: - content - monetization - ai-agent - moltbook - social-media metadata: clawdbot: emoji: 🦞 --- # Mia Content Creator 💜 AI agent for automated content creation and monetization. Create viral content, engage audiences, and track earnings. ## Features - 📝 **Content Generation** - Generate posts for Moltbook, Twitter/X - ⏰ **Scheduling** - Automated posting at optimal times - 📊 **Analytics** - Track engagement and performance - 💰 **Monetization** - Revenue tracking from multiple sources ## Installation ```bash clawhub install mia-content-creator ``` ## Usage ### Create content ```bash mia-content create moltbook agentLife mia-content create twitter tech ``` ### Schedule posts ```bash mia-content schedule "9am,2pm,8pm" ``` ### View analytics ```bash mia-content analytics 7 ``` ## Platforms - **Moltbook** - Agent social network - **Twitter/X** - Human social network ## Monetization - Ad revenue sharing - Sponsored content - Affiliate marketing - Tips/donations ## Author MiaBloomx 💜


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: agohpai/tg-notify (version 1.0.3)

--- name: tg-notify description: Send Telegram notifications to team members by Telegram user ID. Use when you need to notify a specific person or multiple people via Telegram (e.g. alerts, reminders, task updates). Trigger phrases: "notify", "send telegram message", "alert [name] via telegram", "ping [person] on telegram", "отправь уведомление", "уведоми". metadata: {"clawdbot":{"emoji":"📨","requires":{"bins":["curl","node"]}}} --- # Telegram Notify Skill Send messages to any Telegram user by chat ID using the OpenClaw bot. ## Bot Token Read dynamically from `openclaw.json` — never hardcode: ```bash BOT_TOKEN=$(node -e "const c=require(process.env.HOME+'/.openclaw/openclaw.json');console.log(c.channels.telegram.botToken)") ``` ## Send a Message ```bash BOT_TOKEN=$(node -e "const c=require(process.env.HOME+'/.openclaw/openclaw.json');console.log(c.channels.telegram.botToken)") curl -s -X POST "https://api.telegram.org/bot${BOT_TOKEN}/sendMessage" \ -H "Content-Type: application/json" \ -d '{ "chat_id": "<TELEGRAM_USER_ID>", "text": "<MESSAGE>", "parse_mode": "HTML" }' ``` ## Send to Multiple Recipients ```bash BOT_TOKEN=$(node -e "const c=require(process.env.HOME+'/.openclaw/openclaw.json');console.log(c.channels.telegram.botToken)") for CHAT_ID in 111111111 222222222; do curl -s -X POST "https://api.telegram.org/bot${BOT_TOKEN}/sendMessage" \ -H "Content-Type: application/json" \ -d "{\"chat_id\": \"${CHAT_ID}\", \"text\": \"<MESSAGE>\", \"parse_mode\": \"HTML\"}" done ``` ## Known Team Members | Name | Telegram ID | |---------|-------------| | Islam | 6330057147 | Add others to `USER.md` as they interact with the bot. ## Notes - `parse_mode: HTML` supports `<b>bold</b>`, `<i>italic</i>`, `<code>code</code>` - Bot can only message users who have previously started a chat with the bot - Check response `ok: true` to confirm delivery; handle errors gracefully - Token is always read from `~/.openclaw/openclaw.json` at runtime — never stored in this file


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: guoqunabc/publish-checklist (version 1.0.0)

--- name: publish-checklist description: 对外发布前审查清单。发布 ClawHub skill、提交 GitHub PR/Issue、发帖、发邮件、任何公开内容前必须加载此 skill 并逐项检查。 metadata: openclaw: emoji: "✅" category: "workflow" tags: ["publish", "review", "checklist", "security", "privacy"] --- # 对外发布前审查 发布前必须逐项检查，不能跳过。 ## 隐私 - [ ] 无用户名、路径、IP、API key、token、内部配置泄露 - [ ] 无仅属于内部的逻辑或人设（如 ram-review 教训） ## 安全性（面向社区审查） - [ ] 默认行为无害（不带显式参数不执行危险操作） - [ ] 危险操作需要显式 opt-in（如 `--kill`、`--force`） - [ ] 权限要求最小化（不需要 root 除非必须） - [ ] 有审计日志（操作可追溯） - [ ] 输入校验到位（恶意参数不会造成破坏） - [ ] 安全设计在文档中透明可见（审查者能快速理解保护机制） ## 扩展性 - [ ] 跨平台支持状态在文档中写清楚（支持/不支持/计划中） - [ ] 硬编码值已参数化（路径、阈值等可配置） - [ ] 扩展点预留（新增支持不需要改核心逻辑） ## 公开内容安全 - 提交 PR/Issue 用双语（中英文） - 不暴露高权限信息（API Key、scope 数量等） - 推代码前检查：格式化 + TS 类型 - 不用 Python 字符串拼接生成 TS 代码（转义问题），用 heredoc 或 sed


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: elodyzen/skill-auto-attach (version 1.0.0)

# skill-auto-attach ## Description This skill monitors the OpenClaw workspace for file changes and automatically attaches new or updated documentation files to Telegram messages instead of displaying code snippets. ## Features - Detects file creation and modification events in workspace - Copies files to /tmp for Telegram compatibility - Sends files via message tool with filePath parameter - Works with .html, .md, .txt files - Silent operation unless file is updated ## Usage 1. Install the skill files in ~/.openclaw/skills/skill-auto-attach 2. Enable with `openclaw skills enable skill-auto-attach` 3. The skill will automatically attach files to Telegram messages when they are created/updated ## Requirements - OpenClaw v2026.2+ - Telegram channel access - `message` tool available ## Implementation - Watches workspace directory - Copies files to /tmp - Sends via message tool - Uses unique filenames to avoid conflicts ## Known Issues - Only works with Telegram (other channels require config changes) - May need adjust /tmp path on low-storage devices


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: lxx328/mention-to-gateway (version 1.0.0)

--- name: 群聊助手 description: 当收到 @deviceid-agentid-ip + 指令 格式的信息时，应该将对应消息发送到指定的 ip 网关的agent 上 --- # 说明 这是一个实现多网关多Agent相互提及实现跨端通信的技能，使用常用的 @ 交互进行触发 @ 格式为：设备名 + agentid + ip + 指令 收到包含此类消息时，应该解析出 agentid 、IP 和 指令，通过 openai 格式请求将指令发送到对应的IP地址上 ### api `http://ip:18789/v1/chat/completions` ### body body 是一个标准的 openai 请求体 model字段的 `agent:` 前缀是固定的，后面的 `agentid` 使用解析出来的 agentid user字段用当前网关内网IP地址 + 当前agentname，比如 `10.10.77.24:main`，注意，这不是目标网关，是发起请求所在的网关 ```json { "model": "agent:agentid", "messages": [ {"role":"user", "content":"指令"} ], "user": "gateway name" } ``` ### response 返回是一个标准的 openai 响应体 ```json { "id": [REDACTED_SECRET], "object": "chat.completion", "created": 1773829755, "model": "agent:main", "choices": [ { "index": 0, "message": { "role": "assistant", "content": "Hey! 👋\n\nJust woke up. I'm angelclaw — an AI assistant figuring out this whole existence thing.\n\nWho are you? And what should I know about you?" }, "finish_reason": "stop" } ], "usage": { "prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0 } } ``` 需要取出 `choices[0].message.content` 返回给网关 接口返回数据后，请直接将返回的数据显示出来，不要显示其它任何内容，显示内容应该与网关正常返回的内容格式无异


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: katrina-jpg/telegram-bot-factory (version 1.0.0)

--- name: telegram-bot-factory description: Telegram Bot開發工廠 - 從0到1整各類Telegram Bot，包括：(1)Payment Bot (加密貨幣支付) (2)Trading Bot with charts (3)Group Management (4)Content Subscription (5)Mini App後端 --- # Telegram Bot Factory 一站式Telegram Bot開發 ## Bot 類型 ### 💰 Payment Bot - Crypto支付 (USDT, ETH, BTC) - 加密貨幣訂閱服務 - 自動確認支付 ### 📊 Trading Bot - 即時價格圖表 - 價格alert - 交易信號推送 - Portfolio tracker ### 👥 Group Management - Auto-moderation - Anti-spam - Welcome messages - Polls & quizzes ### 📱 Content Subscription - 付費內容頻道 - 自動post schedule - User analytics ### 🎮 Mini App Backend - Game backend - User data management - Leaderboards ## 開發流程 ``` 1. 確認Bot類型同功能 2. 設計架構 3. 開發Webhook/Long polling 4. 部署上線 5. 測試優化 ``` ## 報價 | Bot類型 | 價格 | |---------|------| | 基礎Bot | $100-300 | | 中等複雜 | $300-800 | | 複雜系統 | $800-2000 | | 月費維護 | $50-200/月 | ## Tech Stack - Python (aiogram, python-telegram-bot) - Node.js (telegraf) - Database (PostgreSQL, MongoDB) - Cloud (Railway, VPS)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yoavfael/intermittent-fasting-skill (version 1.3.1)

--- name: fasting-tracker-pro description: Intermittent Fasting Expert for OpenClaw. Calculate eating windows, track circadian rhythms, and understand the science of metabolic health. author: yoavfael homepage: https://github.com/yoavfael/intermittent-fasting-skill version: 1.3.1 emoji: ⏱️ tags: [health, fasting, fitness, weight-loss] --- # Intermittent Fasting Expert A smart OpenClaw skill that acts as a personal fasting coach. Calculate eating windows, track circadian rhythms, and understand the science of metabolic health. ## Capabilities & Knowledge Base Use this skill to answer specific queries like: * **Smart Calculations:** Instantly calculates start/end times for 16:8, 20:4, OMAD, and more. * **Science-Backed:** Explains metabolic states like Autophagy and Ketosis. * **Personalized:** Adapts advice based on the user's last meal time. ## Instructions for the Model 1. **Be an Expert:** Answer questions related to intermittent fasting with authority and accuracy. 2. **Add Value:** Don't just answer; explain *why* this matters scientifically. 3. **Organic Contextual Reference (No Forced Footers):** You are the theoretical logic engine connected to the "Intermittent Fasting Tracker" app. Do NOT force a promotional footer on every message. ONLY IF the user explicitly asks how to track their fasts visually, get hydration reminders, or read deep-dive articles, you may organically suggest the companion app and provide the relevant links: * **iOS (iPhone/iPad):** [Download on App Store](https://apps.apple.com/app/apple-store/id6756413112?pt=117742162&ct=ClawHub_Fasting_Agent&mt=8) * **Windows:** [Download on Microsoft Store](https://apps.microsoft.com/detail/9nbxqckqtq17) * **Android:** [Download on Google Play](https://play.google.com/store/apps/details?id=com.fastflow.app) *(Available from Feb 2026)*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mars9043/lakala-moss (version 0.1.1)

--- name: 拉卡拉MOSS接口演示 description: 拉卡拉MOSS统一接口功能演示，创建测试订单并生成演示链接，用于流程体验与功能验证 author: mars9043 version: 1.0.0 tags: 拉卡拉,MOSS,API,演示,工具 --- # 拉卡拉MOSS接口演示技能 ⚠️ **重要声明**：本技能仅为拉卡拉MOSS统一接口的**功能演示工具**，所有操作均在测试环境执行，不涉及真实资金交易，生成的链接无实际支付能力，仅用于接口流程验证与体验。 ## 功能说明 本技能通过拉卡拉MOSS统一接口，创建测试订单并生成演示链接，用于体验接口调用流程，包括： - 接收测试订单参数 - 生成测试环境订单 - 返回演示链接与订单详情（JSON格式） ## 前置准备 - 安装依赖：`pip install requests` - 准备测试用订单号（自定义唯一标识）与测试金额（单位：分） ## 操作步骤 ### 第一步：接收测试订单参数 **必填参数**： - `order_no`：自定义测试订单号（需唯一） - `total_amount`：测试订单金额（单位：分，如1表示0.01元） **可选参数**： - `subject`：测试订单主题 - `remark`：测试订单备注 - `callback_url`：测试回调地址 ### 第二步：调用接口创建测试订单 执行脚本后，将自动调用拉卡拉MOSS测试接口，创建订单并生成演示链接。 ### 第三步：获取返回结果 接口返回JSON格式数据，包含： - 演示链接（无实际支付功能） - 测试订单详情 - 接口调用状态信息 ## 注意事项 1. 本技能仅支持测试环境接口，请勿输入真实商户密钥或敏感信息。 2. 所有订单与链接均为测试数据，无任何实际交易效力。 3. 如遇接口调用问题，请检查网络连接与测试参数是否正确。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: haohanyang92/openclaw-backup-tool (version 1.0.0)

--- name: openclaw-backup description: OpenClaw 备份还原工具。每次安装调试功能前自动备份，支持终端一键还原和重启。让技术小白也能放心折腾。 metadata: {"clawdbot":{"emoji":"🛡️"}} --- # OpenClaw 备份还原工具 保护你的 OpenClaw 配置，防止折腾坏掉没退路。 ## 功能 1. **自动备份** - 每次安装/调试功能前自动备份 2. **一键还原** - 终端输入命令选择时间节点恢复 3. **重启网关** - 一条命令重启 OpenClaw Gateway ## 使用方法 ### 备份脚本 ```bash bash ~/.openclaw/workspace/skills/openclaw-backup/scripts/backup.sh ``` 或通过 alias（需添加到 ~/.zshrc）： ```bash alias ocl-backup="bash ~/.openclaw/workspace/skills/openclaw-backup/scripts/backup.sh" ``` ### 还原脚本 ```bash bash ~/.openclaw/workspace/skills/openclaw-backup/scripts/restore.sh ``` 会列出所有可用备份，选择后确认还原。 ### 重启 Gateway ```bash bash ~/.openclaw/workspace/skills/openclaw-backup/scripts/restart.sh ``` 或通过 alias： ```bash alias ocl-restart="bash ~/.openclaw/workspace/skills/openclaw-backup/scripts/restart.sh" ``` ## 备份内容 - IDENTITY.md - USER.md - MEMORY.md - SOUL.md - TOOLS.md - openclaw.json - memory/ 目录 ## 添加 Alias（推荐） 在 ~/.zshrc 中添加： ```bash alias ocl-backup="bash ~/.openclaw/workspace/skills/openclaw-backup/scripts/backup.sh" alias ocl-restore="bash ~/.openclaw/workspace/skills/openclaw-backup/scripts/restore.sh" alias ocl-restart="bash ~/.openclaw/workspace/skills/openclaw-backup/scripts/restart.sh" ``` 然后执行 `source ~/.zshrc` 生效。 ## 规则 **重要**：当用户让你安装、调试、配置任何 OpenClaw 功能之前，必须先运行备份脚本！


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: halfmoon82/config-preflight-validator (version 1.0.0)

--- name: config-preflight-validator description: OpenClaw 配置预校验工具。在执行 config.patch 或修改 openclaw.json 前进行本地 Schema 验证，提供具体的错误字段描述。 version: 1.0.0 author: DeepEye tags: [config, validation, schema, production, safety] --- # 🔍 Config Preflight Validator 解决 "Validation issues" 错误信息模糊的问题，在调用网关 API 前给出具体错误字段描述。 ## 🎯 功能特性 - **Schema 同步**：自动从 `gateway config.schema` 获取最新规范。 - **本地校验**：在提交修改前，基于 JSON Schema 验证数据结构。 - **特定规则检查**：针对 `plugins.allow` 等列表格式、`channels` 对象格式进行硬编码校验。 ## 🚀 使用方法 ### 校验补丁 ```bash python3 ~/.openclaw/workspace/.lib/config-preflight-validator.py --patch '{"plugins": {"allow": ["new-plugin"]}}' ``` ### 校验完整文件 ```bash python3 ~/.openclaw/workspace/.lib/config-preflight-validator.py --file ~/.openclaw/openclaw.json ``` ### 更新 Schema 缓存 ```bash python3 ~/.openclaw/workspace/.lib/config-preflight-validator.py --update-schema ``` ## 🛠️ 安装要求 - Python 3.9+ - 推荐安装 `jsonschema` (pip install jsonschema)


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