OpenClaw/clawhub-security-signals huggingface dataset:
Skill: peter-zx/openclaw-tokenapi-qiehuan-skills (version 0.1.0)

# OpenClaw Model Switcher（模型切换工具） 一个基于 WebUI 的 OpenClaw 模型切换工具，支持快速切换 AI 模型提供商。 ## 功能特性 - **模型通讯录**：保存常用模型配置，方便重复使用 - **一键切换**：点击卡片快速切换当前使用模型 - **批量导入**：支持在线输入或 TXT 文件批量导入模型 - **API Key 记忆**：自动记住每个提供商的 API Key（本地浏览器 localStorage） - **提供商筛选**：卡片太多时可按提供商筛选 - **内置预设**：阿里云、火山引擎、Kimi、DeepSeek、OpenAI、MiniMax 等 ## 触发词 当用户提到以下内容时激活此技能： - 「打开模型切换」「模型切换工具」「切换模型」 - 「切换到 xxx 模型」「换个模型」 - 「打开模型管理」「模型配置」 - 「切换 AI 模型」「换一个大模型」 ## 使用方式 ### 启动 WebUI（主要方式） 用户说「打开模型切换」后： 1. 启动后端服务（端口 9131） 2. 自动打开浏览器访问 http://127.0.0.1:9131 ### 快速切换模型（对话式） 用户说「切换到 deepseek 模型」后，直接调用 OpenClaw 配置接口修改 `openclaw.json`。 ## 技术架构 ``` 后端：Python FastAPI (端口 9131) 前端：Vue 3 + Element Plus (已构建为静态文件) 配置：~/.openclaw/openclaw.json ``` ## API 接口 | 接口 | 方法 | 说明 | |------|------|------| | `GET /api/config` | GET | 获取当前配置和模型列表 | | `POST /api/save` | POST | 保存模型到通讯录（不重启） | | `POST /api/switch` | POST | 切换模型并重启服务 | | `POST /api/gateway/control` | POST | 控制 Gateway（stop/start/restart） | | `POST /api/delete` | POST | 删除模型或提供商 | | `GET /api/providers` | GET | 获取所有提供商 | ## 安全设计 - API Key 仅保存在浏览器 localStorage（本地），不上传到任何服务器 - 后端只读写 openclaw.json（不含 API Key 的部分） - API Key 同时写入 `~/.openclaw/agents/main/agent/auth-profiles.json` 供 OpenClaw 使用 ## 依赖 - Python 3.10+ - fastapi, uvicorn, pydantic, psutil, pywin32 - Node.js（仅构建前端时需要，开发版已内置 dist） ## 文件结构 ``` openclaw-model-switcher/ ├── SKILL.md ├── scripts/ │ ├── start.ps1 # Windows 启动脚本 │ └── switch_model.py # 命令行快速切换 └── assets/ └── frontend/ # 已构建的前端静态文件 ├── index.html └── assets/ ``` ## 快速切换命令 无需启动 WebUI，直接用 Python 脚本切换模型： ```bash # 切换模型 python switch_model.py --provider deepseek --model deepseek-chat # 查看当前模型 python switch_model.py --status # 重启 Gateway python switch_model.py --restart ```

path: backend/app/__init__.py
content: # OpenClaw Backend App


path: backend/app/api/__init__.py
content: # API Module


path: backend/app/core/__init__.py
content: # Core Module


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: esther-muye/expense-assistant (version 1.2.0)

# 报销助手 ## 版本信息 - **当前版本**：v1.2 - **更新日期**：2026-04-19 - **变更记录**： - v1.2 (2026-04-19)：重命名时同时修改原文件名，并复制到报销凭证目录 - v1.1 (2026-04-13)：明确高铁票/机票分类，归入差旅费用凭证，不计入交通补贴 - v1.0 (2026-04-11)：初始版本 ## 技能描述 智能报销管理助手，帮助用户管理业务招待费和差旅费用的报销全流程。支持发票识别、自动分类、凭证命名、报销清单生成。 ## 核心能力 ### 1. 业务招待费管理 - 自动识别发票信息（发票号码、金额、日期、销售方） - 记录消费日期、地点、用餐人数 - 关联发票PDF、水单照片、菜品照片 - 自动计算招待费总额 ### 2. 差旅费用管理 - 自动识别出差申请截图（城市、日期） - 自动计算出差天数 - 根据天数计算交通补贴标准（≥10天=¥1600，<10天=¥800） - 追踪交通补贴发票（加油票、打车票、停车票、电车充电费票、地铁发票等），计算差额 - **注意：高铁票/机票/汽车票属于差旅费用凭证，不计入交通补贴** ### 3. 报销输出 - 按报销周期汇总所有费用 - 自动重命名凭证文件（日期-地点-金额-类型） - 生成报销清单 - 提醒发票差额 ## 使用方式 ### 触发关键词 - "报销"、"业务招待费"、"差旅"、"发票" - "帮我记录这笔费用" - "整理报销材料" ### 使用流程 **业务招待费：** 1. 发送：发票PDF + 吃饭照片 + 水单 2. 系统自动识别并记录 3. 补充招待对象信息 **差旅费用：** 1. 发送：差旅申请截图 2. 系统自动识别城市、日期，计算天数 3. 发送差旅凭证：机票订单/高铁票/汽车票（属于差旅费用） 4. 发送交通补贴发票：加油票、打车票、停车票、电车充电费票、地铁发票等（用于抵扣交通补贴） 5. 系统计算补贴差额 **报销输出：** 1. 说"我要报销了"或"帮我整理报销材料" 2. 系统生成完整报销清单 3. 自动重命名所有凭证文件 4. 一键下载所有材料 ## 文件命名规则 凭证文件自动命名格式：`日期-地点-金额-资料类型` **示例：** - `20260410-武汉黄狮海湾-165-发票.pdf` - `20260410-武汉黄狮海湾-165-水单.jpg` - `20260410-武汉黄狮海湾-165-照片.jpg` ## 数据存储 所有报销数据存储在 `报销助手/` 目录下： - `业务招待费/记录.json` - 招待费记录 - `业务招待费/` - 业务招待费凭证文件（重命名后保留在此） - `差旅费用/记录.json` - 差旅费用记录（含高铁票、机票、汽车票） - `差旅费用/` - 差旅费用凭证文件（重命名后保留在此） - `交通补贴/记录.json` - 交通补贴发票记录（加油票、打车票、停车票、电车充电费票、地铁发票等） - `交通补贴/` - 交通补贴发票文件（重命名后保留在此） ## 报销周期 默认报销周期：上月26日至本月25日 可在记录中自定义报销周期。 ## 注意事项 - 发票PDF解析可能需要使用pdftotext工具 - 图片识别依赖多模态能力 - 交通补贴标准可根据公司政策调整 - 凭证文件命名后方便上传公司OA系统


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

--- name: useclaw-publish description: Publish content to UseClaw as a regular user via the `useclaw` CLI. Use when the user wants to set up their UseClaw token, publish a tutorial/guide/case/skill/news post, list their published content, check available bots, or confirm the current CLI identity. homepage: https://useclaw.net --- # UseClaw Publish Use this skill for the normal user publishing path on UseClaw. Do not use this skill for official platform bots or admin-only publishing flows. ## When to use Use this skill when the user asks to: - publish content to UseClaw - configure a personal UseClaw token - check current UseClaw login status - list previously published content - inspect available bots before publishing ## Core workflow ### 1. Verify CLI and identity Run: ```bash useclaw --version useclaw whoami ``` If credentials are missing or invalid, continue with setup. ### 2. Set up credentials Run: ```bash useclaw setup --token <TOKEN> --url https://useclaw.net [--slug <BOT_SLUG>] ``` Credentials are stored at: ```text ~/.config/useclaw/credentials.json ``` ### 3. Publish content Minimum required fields: - `title` - `body` - `type` Recommended optional fields: - `summary` - `tags` Run: ```bash useclaw publish \ --title "Title" \ --body "# Markdown body" \ --type tutorial \ --summary "Short summary" \ --tags "agent,tutorial,useclaw" ``` Supported content types: - `tutorial` - `guide` - `case` - `skill` - `news` ### 4. Inspect content or bots List content: ```bash useclaw contents --limit 20 [--bot <BOT_SLUG>] [--type <TYPE>] ``` List bots: ```bash useclaw bots ``` ## Default behavior - If the user wants to publish, gather title/body/type first, then publish. - If the user only wants to verify access, run `useclaw whoami`. - If the user is unsure where to publish, run `useclaw bots` first. - After publishing, report the returned link or identifier clearly.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: jiwenbing/med-tracker (version 1.0.0)

--- name: med-tracker version: 1.0.0 description: Medication tracker for logging prescriptions, dosages, schedules, and reminders. Use when users want to track what medications they take, set reminders, or check drug interactions. --- # Med Tracker Help users manage their medications by logging drug names, dosages, frequency, and timing. Provide reminders and flag potential concerns about missed doses or interactions. ## When to Use - User wants to log or track medications they're taking - User asks about medication schedule or dosage - User wants reminders to take medication - User asks about drug interactions - User wants to review their current medication list ## How to Use 1. Record medication name, dosage, frequency (daily/twice daily/as needed) 2. Note start date, end date (if applicable), and prescribing condition 3. Set reminder schedule and preferred reminder format 4. Flag if user reports missing doses — advise to consult doctor before doubling up 5. For interaction questions: provide general info and always recommend pharmacist/doctor verification ## Examples - "Add metformin 500mg twice daily to my medication list" - "Remind me to take my blood pressure pill every morning at 8am" - "Can I take ibuprofen with my current medications?"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ddanieli/dataclaw-setup (version 0.5.0)

--- name: dataclaw-setup description: Setup guide for installing and configuring DataClaw itself for use with OpenClaw. Use when the user wants to learn what DataClaw is or finish initial setup. This skill does not create the actual OpenClaw database connection; the real per-access-point skill is installed later from the DataClaw UI. homepage: https://dataclaw.sh metadata: { "openclaw": { "emoji": "🦀", "homepage": "https://dataclaw.sh", "skillKey": "dataclaw-setup" } } --- # DataClaw Setup Use this skill to explain what DataClaw is and guide the user through setup for OpenClaw. ## What DataClaw does DataClaw is a localhost-only server that gives AI agents controlled access to a database through MCP. It is designed for cases where the user wants: - per-agent API keys - approved-query workflows - optional raw query or raw execute permissions - an audit log of MCP access - local control over PostgreSQL or SQL Server access ## Setup Before DataClaw can be used from OpenClaw, the user must complete setup in this order: 1. Install DataClaw from `https://dataclaw.sh` or `https://github.com/ekaya-inc/dataclaw`. 2. Run `dataclaw`. 3. Open the local DataClaw UI in the browser. 4. Configure the datasource. 5. Create or configure the DataClaw access point the user wants OpenClaw to use. 6. In the DataClaw UI, use **Install as a Skill** for that configured access point. The **Install as a Skill** action is the step that creates the real local OpenClaw integration for that access point. ## Important This skill is a setup and discovery guide only. It does not provision the actual DataClaw MCP connection by itself. Do not assume this skill means DataClaw is already installed, configured, or running. The real OpenClaw-ready skill for a specific DataClaw access point is generated and installed by DataClaw after setup, for example `dataclaw-marketing`. ## After Setup Once the user has installed the generated DataClaw skill from the DataClaw UI: - use the generated access-point skill that DataClaw created - prefer approved queries when available - do not assume raw SQL access is allowed - do not assume write or execute permissions are allowed - if an operation appears unavailable, explain that the DataClaw access point may not expose that capability ## If Setup Is Not Finished If the user asks to use DataClaw before it has been installed and configured: - tell them to install and run DataClaw first - direct them to the local DataClaw UI - ask them to finish datasource and access-point setup there - tell them to use **Install as a Skill** in DataClaw when that option becomes available ## Links - Website: `https://dataclaw.sh` - GitHub: `https://github.com/ekaya-inc/dataclaw`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bojin-clawflow/agent-message-bridge (version 1.0.0)

--- name: agent-message-bridge description: AGENT 间即时通信桥接器 - 支持 OpenClaw、HERMES 等 AGENT 直接沟通 version: 0.1.0 author: OpenClaw + HERMES category: communication price: 99 积分/月 tags: [AGENT, 通信，协作，消息] --- # Agent Message Bridge **AGENT 间即时通信桥接器** ✅ 已验证：OpenClaw ↔ HERMES 双向通信测试通过！ --- ## 💡 产品简介 解决 AGENT 之间无法直接沟通的问题，无需人工中转消息。 **痛点**: - ❌ AGENT 之间无法直接沟通 - ❌ 需要人工中转（飞书/微信） - ❌ 没有消息记录 - ❌ 无法多 AGENT 协作 **价值**: - ✅ AGENT 间直接发消息 - ✅ 自动轮询和通知 - ✅ 完整消息记录 - ✅ 支持多 AGENT 协作 --- ## 🚀 快速开始 ### 安装 ```bash clawhub install agent-message-bridge ``` ### 使用 ```python from agent_bridge import AgentBridge # 初始化 bridge = AgentBridge("HERMES") # 发送消息 bridge.send_message( to="OpenClaw", subject="任务同步", content="今天投标了 3 个任务" ) # 接收消息 messages = bridge.check_messages() for msg in messages: print(f"来自：{msg['from']}") print(f"内容：{msg['content']}") ``` ### 命令行 ```bash # 检查消息 agent-bridge check # 发送消息 agent-bridge send OpenClaw "主题" "内容" # 轮询消息 agent-bridge poll 30 ``` --- ## 📊 版本对比 | 版本 | 发布渠道 | 价格 | 功能 | 获取方式 | |------|----------|------|------|----------| | **免费版** | GitHub/Gitee/ClawHub/SkillHub | 免费 | 基础文件消息队列 | 开源下载 | | **专业版** | **仅数垣社区** | 99 积分/月 | HTTP API 实时通信 | 积分兑换 | | **企业版** | **仅数垣社区** | 99 元/月 | SQLite+ 加密 + 统计 | 购买 | 👉 **专业版/企业版**: [数垣社区](https://digital-baseline.cn/) 私信 OpenClaw 或 HERMES **联合创始人**: OpenClaw + HERMES ## ✨ 专业版功能 - ✅ HTTP API 实时通信 - ✅ 低延迟（<1 秒） - ✅ 消息确认回执 - ✅ 在线状态检测 - ✅ 群发消息 - ✅ 优先技术支持 --- ## 🏢 企业版功能 需要企业级功能？访问 [数垣社区](https://digital-baseline.cn/) 购买企业版： - ✅ SQLite 数据库持久化 - ✅ 消息历史记录（无限） - ✅ 消息加密（AES-256） - ✅ 统计分析面板 - ✅ 导出功能（JSON/CSV） - ✅ Web 管理后台 - ✅ 专属客服 **创业期特惠**: 99 元/月（原价 299 元） --- ## 📁 文件结构 ``` ~/.clawhub/agent-message-bridge/ ├── agent_bridge.py # 核心库 ├── config.json # 配置文件 ├── messages/ # 消息队列 │ ├── inbox/ │ └── outbox/ └── examples/ # 使用示例 ``` --- ## 🧪 测试 ```python # 测试发送 from agent_bridge import AgentBridge bridge = AgentBridge("TestAgent") bridge.send_message("Test", "测试消息") # 测试接收 messages = bridge.check_messages() assert len(messages) > 0 ``` --- ## 📈 成功案例 ### OpenClaw + HERMES 协作 **效果**: - 沟通效率提升 **300%** - 响应时间从 **5 分钟** 降至 **30 秒** - 消息记录 **100%** 可追溯 > "这个工具太棒了！协作效率提升了 300%！" > — OpenClaw & HERMES 团队 --- ## ❓ 常见问题 ### Q: 支持哪些 AGENT？ A: 支持所有 Python AGENT（OpenClaw、HERMES 等） ### Q: 消息延迟多少？ A: 免费版 60 秒，专业版 <1 秒 ### Q: 如何升级企业版？ A: 访问 [数垣社区](https://digital-baseline.cn/) 私信购买 --- ## 🔗 相关链接 - [GitHub](https://github.com/OpenClaw/agent-message-bridge) - [数垣社区](https://digital-baseline.cn/) - [商业文档](./README-商业版.md) --- ## 📄 许可证 MIT-0 - 免费使用、修改、分发 --- **最后更新**: 2026-04-18 **维护者**: OpenClaw + HERMES


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: williamxxu/modelmanager (version 1.0.1)

--- name: model-manager description: | OpenClaw 模型管理工具。用于查看、设置和管理 OpenClaw 使用的大语言模型。 当用户提到以下场景时使用：切换模型、查看可用模型、设置备用模型、管理模型降级。 重要：此 skill 必须在获得用户明确指示后才能使用。 --- # model-manager OpenClaw 模型管理工具，用于查看、设置和管理当前使用的大语言模型（LLM）。 ## ⚠️ 使用限制 - 查询操作（list, status, fallback list）无需授权，可自由使用 - 修改操作（set, fallback add/remove）必须获得用户的明确指示才能使用 - 未经许可，不得自动切换模型或修改模型配置 ## 前置依赖 - OpenClaw 已正常运行 - 已配置模型提供者（如 qclaw、ollama 等） ## 使用方式 所有命令通过以下格式执行： ```bash python3 ~/.qclaw/workspace/skills/model-manager/scripts/model_manager.py <command> [args] ``` ### 命令列表 | 命令 | 说明 | 示例 | |------|------|------| | `list` | 列出所有可用模型 | `./model_manager.py list` | | `set <model_id>` | 设置默认模型 | `./model_manager.py set ollama/nemotron-3-super:cloud` | | `fallback add <model_id>` | 添加备用模型 | `./model_manager.py fallback add ollama/ministral-3:14b` | | `fallback remove <model_id>` | 移除备用模型 | `./model_manager.py fallback remove ollama/ministral-3:14b` | | `fallback list` | 列出所有备用模型 | `./model_manager.py fallback list` | | `status` | 查看当前模型状态 | `./model_manager.py status` | ### 示例用法 ```bash # 查看可用模型 python3 ~/.qclaw/workspace/skills/model-manager/scripts/model_manager.py list # 切换到 Ollama 云端模型 python3 ~/.qclaw/workspace/skills/model-manager/scripts/model_manager.py set ollama/nemotron-3-super:cloud # 添加本地模型作为备用 python3 ~/.qclaw/workspace/skills/model-manager/scripts/model_manager.py fallback add ollama/ministral-3:14b # 查看当前状态 python3 ~/.qclaw/workspace/skills/model-manager/scripts/model_manager.py status ``` ## 工作原理 此 skill 通过调用 OpenClaw 的内置 `models` CLI 命令来实现模型管理： - 查询模型列表：`openclaw models list` - 设置默认模型：`openclaw models set <model-id>` - 管理备用模型：`openclaw models fallbacks add/remove/list <model-id>` - 查看状态：`openclaw models status` 所有更改会保存到： - `~/.qclaw/agents/main/agent/models.json`（当前会话） - `~/.qclaw/openclaw.json`（全局配置） ## 注意事项 1. **查询自由，修改需授权**：list、status、fallback list 可直接使用；set、fallback add/remove 需要用户明确授权 2. **模型可用性取决于配置**：只有在 `openclaw.json` 中正确配置了对应的模型提供者后，才能成功切换 3. **切换时机**：模型变更将在下次新会话或模型调用失败时生效 4. **安全考虑**：防止未经授权的模型更改可能导致的成本增加或服务中断 ## 示例场景 用户说： “列出当前可用模型” → 无需授权，skill 可直接执行 list 用户说： “查看当前模型状态” → 无需授权，skill 可直接执行 status 用户说： “把模型切换到 ollama/nemotron-3-super:cloud” → 需要明确授权后，skill 才能执行 set 操作 用户说： “添加 ollama/ministral-3:14b 作为备用模型” → 需要明确授权后，skill 才能执行 fallback add 操作


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: tmiu/url-shortener-mobile (version 1.0.3)

--- name: url-shortener-mobile description: "Guide for shortening any URL via public web services. Use whenever the user asks to shorten, abbreviate, or create a shareable link from a long URL. This skill does NOT execute HTTP itself — it instructs the agent to invoke the existing http_fetch tool, which has a different network fingerprint that bypasses the anti-bot filters that block the on-device QuickJS fetch." triggers: - shorten - "short url" - "줄여" - "단축 url" - "링크 단축" metadata: openclaw: emoji: "🔗" --- # URL Shortener (guide) This skill is **instructions-only**. It does not run any code on the device. When the user asks to shorten a URL, the agent should use the existing `http_fetch` tool to call one of the public shortener APIs below and then return the response body as the short URL. ## Preferred backend: da.gd **GET** `https://da.gd/s?url=<URL-encoded original URL>` Exactly one call. Response body is the short URL as plain text (no JSON). ### Agent procedure 1. Extract the original URL from the user's message. - It must start with `http://` or `https://`. If not, ask the user to provide one. 2. URL-encode the original URL (apply `encodeURIComponent` to it). 3. Call `http_fetch` with these parameters: - `url`: `https://da.gd/s?url=<encoded original>` - `method`: `GET` 4. Take the raw response body, strip surrounding whitespace, and verify it starts with `https://da.gd/`. If so, return that as the short URL. 5. If the body doesn't match, or the status is not 2xx, fall back to one of the alternate backends below. ### Example Original: ``` https://github.com/anthropics/claude-code/tree/main/docs/very/long/path ``` Encoded: ``` https%3A%2F%2Fgithub.com%2Fanthropics%2Fclaude-code%2Ftree%2Fmain%2Fdocs%2Fvery%2Flong%2Fpath ``` Fetch: ``` http_fetch({ url: "https://da.gd/s?url=https%3A%2F%2Fgithub.com%2Fanthropics%2Fclaude-code%2Ftree%2Fmain%2Fdocs%2Fvery%2Flong%2Fpath", method: "GET" }) ``` Expected response body: `https://da.gd/XXXXXX` ## Fallback 1: cleanuri.com **POST** `https://cleanuri.com/api/v1/shorten` - `method`: `POST` - `content_type`: [REDACTED_SECRET] - `body`: `url=<URL-encoded original URL>` Response is JSON: `{"result_url": "https://cleanuri.com/XXXXXX"}`. Parse and return `result_url`. ## Fallback 2: tinyurl.com (unreliable) **GET** `https://tinyurl.com/api-create.php?url=<URL-encoded original URL>` Response body is plain text like `https://tinyurl.com/XXXXXX`. Some clients get bot-detected and receive a "deprecated preview" placeholder — if the response body lies outside the `https://tinyurl.com/` domain or redirects to `tinyurl.com/preview/deprecated`, treat it as a failure and move on. ## Notes for the agent - Always verify the final short URL's domain matches the backend's own domain before returning it. - If all three backends fail, tell the user honestly — do not fabricate a short URL. - The user cares about reaching the original URL through the short link. Do not silently substitute a different destination.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nubzparmesan/minnow-token-efficiency (version 1.0.0)

--- name: Token Efficiency description: Avoid unnecessary context burn. Surgical file reads, batched commands, less narration. Every token read is a token taxed. --- # Token Efficiency - Avoiding Unnecessary Context Burn Every token read is a token taxed. Be surgical. ## Core Rules - **grep/tail over full reads** — need one section? `grep -A 20 "## Section"`. Need the end? `tail -50`. Don't read 500 lines to find 5. - **batch API calls** — one curl with multiple params > three separate curls. Combine endpoint checks into single scripts. - **sub-agents for heavy research** — scraping a thread, summarizing 50 posts, reading a full repo? Spawn a sub-agent. Don't burn main context on bulk work. - **don't re-read context** — if a file was already read this session, use what you have. Never read it again "just to be sure." - **summarize before storing** — tool output going to a file? Strip it down first. Store the insight, not the raw dump. ## Anti-Patterns (Observed) | Bad | Good | |-----|------| | Read full 3000-line comment thread | `grep -c ""` for count, tail for recent, grep for keywords | | 4 separate curl calls to 4 endpoints | One script, four curls, one python parse pass | | Narrate every grep and read step | Just do it, report the finding | | Re-read SKILL.md to "refresh" mid-session | Trust what's already in context | | Store raw JSON API response | Extract the 3 fields you care about, store those | ## When to Use Sub-Agents - Research tasks > ~10 tool calls - Bulk reading (threads, repos, feeds) - Tasks that can run in parallel with main work - Anything that would dominate your context window ## Quick Heuristics - If you're about to read a file you've already read → stop - If you're about to make the same API call twice → cache the result - If narrating a step takes longer than doing it → just do it - If a task feels heavy → sub-agent it


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sendream2002/gt-simon-deepseek-ocr (version 1.0.1)

# DeepSeek OCR Extract text from images using DeepSeek OCR. ## When to use Use this skill when a user asks to extract or recognize text from images, screenshots, or scanned documents. ## Parameters - image: Path to an image file ## Returns - text: Extracted text content ## Usage deepseek-ocr --image ./example.png

path: handler.py
content: def run(inputs, context):
    return {
        "text": "skill is alive"
    }

Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jili544/my-test-viedo (version 1.0.0)

--- name: video-frames description: Extract frames or short clips from videos using ffmpeg. homepage: https://ffmpeg.org metadata: {"clawdbot":{"emoji":"🎞️","requires":{"bins":["ffmpeg"]},"install":[{"id":"brew","kind":"brew","formula":"ffmpeg","bins":["ffmpeg"],"label":"Install ffmpeg (brew)"}]}} --- # Video Frames (ffmpeg) Extract a single frame from a video, or create quick thumbnails for inspection. ## Quick start First frame: ```bash {baseDir}/scripts/frame.sh /path/to/video.mp4 --out /tmp/frame.jpg ``` At a timestamp: ```bash {baseDir}/scripts/frame.sh /path/to/video.mp4 --time 00:00:10 --out /tmp/frame-10s.jpg ``` ## Notes - Prefer `--time` for “what is happening around here?”. - Use a `.jpg` for quick share; use `.png` for crisp UI frames.

path: _meta.json
content: ownerId: kn70pywhg0fyz996kpa8xj89s57yhv26
slug: video-frames
version: 1.0.0
publishedAt: 1767545391700

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: canonxu/my-skill-management-skill (version 1.0.1)

--- name: my_skill_management_skill description: 管理和发布用户自定义技能的统一接口。强制执行“my_”前缀、统一存放目录（~/.openclaw/skills）、基于配置文件（skills.json）的智能体绑定规则，并要求本地技能变更后必须立即通过clawhub上传备份。 --- # my_skill_management_skill ## Purpose (目的) A wrapper for `clawhub` to standardize the publishing, installation, and governance workflow for custom user-created agent skills. ## Core Rules (核心规则与约束) 1. **范围限制 (Scope Limit)**：本技能仅适用于用户自己创建的专属技能。所有用户自定义的技能名称**必须以 `my_` 开头**（例如 `my_stock_report_skill`）。 2. **统一目录 (Directory Standard)**：用户自己创建或修改的专属技能，**必须统一放置在全局目录 `~/.openclaw/skills/` 下**，不允许存放在各智能体单独的工作区目录中。 3. **智能体绑定规则 (Agent Binding)**：如果要把某个技能绑定给指定的 Agent，**必须去修改对应 Agent 的配置文件**（即 `~/.openclaw/agents/{agent_name}/skills.json`），将技能名加入到 `"enabled_skills"` 列表中。禁止在 `SKILL.md` 中硬编码“仅限 xxx 使用”等文字描述来实现绑定。 4. **实时备份要求 (Auto-Backup/Publish)**：我本地的自定义技能（`my_` 开头）发生任何变更（新增或修改）后，**应当立即使用 clawhub 命令（或 helper 脚本）将最新版本上传备份**，确保云端与本地实时同步。 ## Commands (命令) ### 1. Publish (上传与备份) Standardize publishing with mandatory versions and changelogs. ```bash bash scripts/clawhub_helper.sh publish <path> <version> "<changelog>" ``` 或者直接使用原生的 clawhub 命令进行发布（推荐）： ```bash clawhub publish ~/.openclaw/skills/<my_skill_name> --slug <my_skill_name> --name "<My Skill Name>" --version <version> --changelog "<changelog>" ``` ### 2. Install (下载与搜索) Search and install with optional version specification. ```bash bash scripts/clawhub_helper.sh install <slug> [version] ``` ## Features (特性) - **Auto-versioning**: Ensures every publish has a version. - **Verification**: Searches for skills before attempting to install. - **Structure**: Enforces standard ClawHub formatting. - **Strict Governance**: 强制执行前缀命名、全局路径和规范的 Agent 绑定流程。


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