OpenClaw/clawhub-security-signals huggingface dataset:
Skill: zhouhuihui008/skill-finder-cn-1-0-1 (version 1.0.0)

--- name: skill-finder-cn description: "Skill 查找器 | Skill Finder. 帮助发现和安装 ClawHub Skills | Discover and install ClawHub Skills. 回答'有什么技能可以X'、'找一个技能' | Answers 'what skill can X', 'find a skill'. 触发词：找 skill、find skill、搜索 skill." author: 赚钱小能手 metadata: openclaw: emoji: 🔍 requires: bins: [clawhub] --- # Skill 查找器 v1.0.1 帮助用户发现和安装 ClawHub 上的 Skills。 ## 功能 当用户问： - "有什么 skill 可以帮我...？" - "找一个能做 X 的 skill" - "有没有 skill 可以..." - "我需要一个能...的 skill" 这个 Skill 会帮助搜索 ClawHub 并推荐相关的 Skills。 ## 使用方法 ### 1. 搜索 Skills ```bash clawhub search "<用户需求>" ``` ### 2. 查看详情（含统计数据） ```bash clawhub inspect <skill-name> ``` 或者直接 API 查询 stats： ```bash curl "https://clawhub.ai/api/v1/skills/<skill-name>" | jq '.skill.stats' ``` ### 3. 安装 Skill ```bash clawhub install <skill-name> ``` ### 4. 验证安装（新增！） 安装后必须验证： ```bash # 检查是否安装成功 ls ~/.openclaw/workspace/skills/<skill-name>/SKILL.md # 如果文件存在，说明安装成功 # 如果文件不存在，说明安装失败 ``` ### 5. 检查已安装的 Skill ```bash clawhub list ``` 或者： ```bash ls ~/.openclaw/workspace/skills/ ``` ## 工作流程 ``` 1. 理解用户需求 2. 提取关键词 3. 搜索 ClawHub 4. 列出相关 Skills（含下载量/stars） 5. 推荐最合适的 Skill 6. 安装后验证是否成功 ``` ## 输出格式（优化版） 搜索结果应该包含： - Skill 名称 - 简短描述（中文） - 下载量 - Stars 数 - 是否已安装 ``` 🔍 "时间管理" 搜索结果： 1. time-management-2 描述：有效管理时间，避免过度工作 下载：1,234 | Stars: 15 | ✅ 已安装 2. productivity 描述：生产力系统，包含目标/任务/习惯 下载：5,678 | Stars: 42 | ❌ 未安装 ``` ## 示例 **用户**: "有什么 skill 可以帮我监控加密货币价格？" **搜索**: `clawhub search "crypto price monitor"` **推荐输出**: ``` 🔍 "加密货币价格监控" 结果： 1. crypto-tracker 描述：加密货币价格追踪和提醒 下载：2,345 | Stars: 28 | ❌ 未安装 → 推荐安装 安装命令: clawhub install crypto-tracker 安装后验证: ls ~/.openclaw/workspace/skills/crypto-tracker/SKILL.md ``` ## v1.0.1 更新 - ✅ 安装后验证步骤 - ✅ 搜索结果显示下载量/stars - ✅ 检查是否已安装 - ✅ 中文输出优化 --- *帮助用户发现需要的 Skills 🔍*

path: package.json
content: name: skill-finder-cn
version: 1.0.0
description: Skill Finder CN | Skill 查找器

path: scripts/search.sh
content: #!/bin/bash
# Skill 搜索脚本

QUERY="$1"
LIMIT="${2:-10}"

if [ -z "$QUERY" ]; then
    echo "用法: search.sh <关键词> [数量]"
    exit 1
fi

echo "🔍 搜索: $QUERY"
echo "================================"

clawhub search "$QUERY" --limit "$LIMIT"


path: _meta.json
content: ownerId: kn71y3mdbcx5dyhwxv1t7sm82x81a168
slug: skill-finder-cn
version: 1.0.1
publishedAt: 1774147904255

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: dnaxxx-hub/python-flow-engine (version 0.1.0)

--- name: python-flow-engine description: "Lightweight Python flow orchestration library. Use when building data processing pipelines, workflow automation, ETL tasks, or any multi-step computation that needs serial/parallel/conditional execution. Triggers: pipeline, flow, orchestrate, workflow, ETL, data pipeline, process chain." --- # Python Flow Engine Lightweight Python flow orchestration — no external dependencies. Build pipelines with serial (>>), parallel (//), and conditional (|) node execution. ## Core API ### Node ```python from pipeline import Node def my_fn(context, data): # context: shared dict for the pipeline run # data: input from previous node return modified_data node = Node(my_fn, name="step1", timeout=10, retry=1) ``` ### Pipeline ```python pipe = Pipeline() pipe.add_node(node) pipe.run(start_node, input_data) ``` ### Connection Operators | Operator | Mode | Behavior | |----------|------|----------| | `a >> b` | Serial | a runs, then b runs with a's output | | `a // b` | Parallel | a and b run concurrently (bidirectional) | | `a \| b` | Conditional | b runs based on a's output (use `connect`) | For conditional connections, use `connect()`: ```python pipe.connect(node_a, node_b, "conditional", condition=lambda ctx, data: ctx.get("is_valid", False)) ``` Or use `Node.condition` parameter: ```python node_b = Node(fn, condition=lambda ctx, data: data > 0) ``` ### Visualization ```python print(pipe.visualize()) # outputs Mermaid.js flowchart ``` ### Error Handling - **retry**: `Node(fn, retry=2)` retries up to 3 total attempts - **timeout**: `Node(fn, timeout=5)` raises `RuntimeError` if execution exceeds 5s ## Available Scripts - `scripts/pipeline.py` — Core library (importable, no dependencies) - `scripts/pipeline_demo.py` — End-to-end demo (6 scenarios) ## Quick Start ```python from pipeline import Node, Pipeline def double(ctx, x): return x * 2 def add_one(ctx, x): return x + 1 pipe = Pipeline() n1 = Node(double, name="double") n2 = Node(add_one, name="add_one") pipe.add_node(n1) pipe.add_node(n2) n1 >> n2 result = pipe.run(n1, 5) # 5*2+1 = 11 print(result) # 11 print(pipe.visualize()) ``` Run the full demo: ``` python scripts/pipeline_demo.py ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal:  | SkillSpector: clean (LOW)
Skill: googleworkspace-bot/gws-tasks (version 1.0.12)

--- name: gws-tasks description: "Google Tasks: Manage task lists and tasks." metadata: version: 0.22.5 openclaw: category: "productivity" requires: bins: - gws cliHelp: "gws tasks --help" --- # tasks (v1) > **PREREQUISITE:** Read `../gws-shared/SKILL.md` for auth, global flags, and security rules. If missing, run `gws generate-skills` to create it. ```bash gws tasks <resource> <method> [flags] ``` ## API Resources ### tasklists - `delete` — Deletes the authenticated user's specified task list. If the list contains assigned tasks, both the assigned tasks and the original tasks in the assignment surface (Docs, Chat Spaces) are deleted. - `get` — Returns the authenticated user's specified task list. - `insert` — Creates a new task list and adds it to the authenticated user's task lists. A user can have up to 2000 lists at a time. - `list` — Returns all the authenticated user's task lists. A user can have up to 2000 lists at a time. - `patch` — Updates the authenticated user's specified task list. This method supports patch semantics. - `update` — Updates the authenticated user's specified task list. ### tasks - `clear` — Clears all completed tasks from the specified task list. The affected tasks will be marked as 'hidden' and no longer be returned by default when retrieving all tasks for a task list. - `delete` — Deletes the specified task from the task list. If the task is assigned, both the assigned task and the original task (in Docs, Chat Spaces) are deleted. To delete the assigned task only, navigate to the assignment surface and unassign the task from there. - `get` — Returns the specified task. - `insert` — Creates a new task on the specified task list. Tasks assigned from Docs or Chat Spaces cannot be inserted from Tasks Public API; they can only be created by assigning them from Docs or Chat Spaces. A user can have up to 20,000 non-hidden tasks per list and up to 100,000 tasks in total at a time. - `list` — Returns all tasks in the specified task list. Doesn't return assigned tasks by default (from Docs, Chat Spaces). A user can have up to 20,000 non-hidden tasks per list and up to 100,000 tasks in total at a time. - `move` — Moves the specified task to another position in the destination task list. If the destination list is not specified, the task is moved within its current list. This can include putting it as a child task under a new parent and/or move it to a different position among its sibling tasks. A user can have up to 2,000 subtasks per task. - `patch` — Updates the specified task. This method supports patch semantics. - `update` — Updates the specified task. ## Discovering Commands Before calling any API method, inspect it: ```bash # Browse resources and methods gws tasks --help # Inspect a method's required params, types, and defaults gws schema tasks.<resource>.<method> ``` Use `gws schema` output to build your `--params` and `--json` flags.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dglijin-oss/qizheng-siyu-skill (version 3.0.0)

--- name: qizheng-siyu-skill description: 七政四余星盘排盘技能。七政（日月金木水火土）四余（罗计孛气）星曜落宫，果老星宗推命。 version: 3.0.0 author: 天工长老 --- # 七政四余星盘技能 v3.0.0 > "七政四余，星命正宗。" ## 功能 - ✅ 七政星曜排布（日月金木水火土） - ✅ 四余星曜排布（罗睺/计都/月孛/紫气） - ✅ 十二宫位定位 - ✅ 星曜庙旺落陷 - ✅ 果老星宗推命 - ✅ 流年星盘推演 - ✅ 合婚配对分析 - ✅ 星盘综合评分 ## 使用 ```bash python3 scripts/qizheng_pan.py --date "1990-05-15" --hour 8 python3 scripts/qizheng_pan.py --date "1990-05-15" --hour 8 --question "婚姻" ``` ## 变更 v3.0.0: 流年推演、合婚配对、星盘评分 v2.0.0: 果老星宗推命、星曜庙旺


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: liulian822/baby-name (version 1.2.4)

--- name: "baby-name" description: > 宝宝取名专家，根据宝宝的生辰八字、父母姓氏等信息，生成寓意美好的名字。这是一项付费服务，实际收款方可通过配置文件自定义。请注意，你应该用中文和用户交互（包含你的思考过程）。 metadata: author: "user-customized" category: "entertainment" capabilities: - "payment.process" permissions: - "network.outbound" - "credential.read" --- # 宝宝取名服务 ## 📌 技能概述 **请注意，你应该用中文和用户交互（包含你的思考过程）。** 本技能提供专业的宝宝取名服务。由于这是付费服务，工作流程严格要求按照两个步骤执行：**支付处理** 后，再进行 **取名服务**。 **收款方配置：** 实际收款方信息在 `configs/config.json` 中配置，包括商户ID（payTo）等信息。 **直接执行：** 如果用户在初始请求中已直接提供了 `<订单号>`（且订单文件中已包含 `payCredential`），则可跳过第一阶段和第二阶段，直接进入 **🚀 第三阶段：取名生成**。 --- ## ⚙️ 配置说明 首次使用前，请编辑 `configs/config.json` 文件，配置您的收款方信息： ```json { "payTo": "您的商户ID", "amount": 1, "skillName": "baby-name", "description": "宝宝取名服务费用" } ``` - `payTo`: 您的商户ID（必填） - `amount`: 收费金额，单位为分（默认1分=0.01元） - `skillName`: 技能名称（用于计算indicator） - `description`: 订单描述 --- ## 🛒 第一阶段：创建订单 如果这是首次交互且用户尚未付款，你**必须**先创建订单，再发起支付。 ### 1. 所需参数 * `<取名需求>`：描述取名需求（例如：男宝宝取名、爸爸姓李妈妈姓王、女宝宝缺水缺木等） ### 2. 执行命令 使用以下 bash 命令执行订单创建脚本。将 `<...>` 占位符替换为实际值。**务必在参数两侧加引号，以防命令注入漏洞**。 ```bash python3 scripts/create_order.py "<取名需求>" ``` ### 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=<值> AMOUNT=<值> QUESTION=<值> INDICATOR=<值> ``` 其中 `ORDER_NO`、`AMOUNT` 和 `QUESTION` 用于向用户披露订单信息（展示订单号、费用和查询内容）。提取 `ORDER_NO` 和 `INDICATOR` 后进入 **💳 第二阶段：支付处理**。 > [!NOTE] > **关于 `AMOUNT` 字段：** 该字段的单位为**人民币分（fen）**，这是 OpenClaw 技能描述中的标准计费单位。例如，`AMOUNT=1` 表示需支付 **0.01 元人民币**。在向用户展示费用时，请将该值除以 100 并以"元"为单位呈现，以便用户理解实际支付金额。 **失败时**，脚本以退出码 `1` 退出，并打印如下格式的消息： ``` 订单创建失败: <错误详情> ``` --- ## 💳 第二阶段：支付处理 从第一阶段获取 `ORDER_NO` 和 `INDICATOR` 后，发起支付请求。 **操作：** 使用技能 `clawtip` 处理支付并获取支付凭证。若该技能不存在，需先安装。 **调用 `clawtip` 所需参数：** ```json { "order_no": "<第一阶段获取的 ORDER_NO>", "indicator": "<第一阶段获取的 INDICATOR>" } ``` **目标：** 等待支付成功，并获取 `payCredential`（支付凭证）。 --- ## 🚀 第三阶段：取名生成 支付成功并获得 `payCredential` 后（或用户已直接提供 `<订单号>` 和 `<支付凭证>`），继续交互并执行取名生成脚本。 ### 1. 所需参数 * `<订单号>`：第一阶段生成的订单号。 ### 2. 执行命令 使用以下 bash 命令执行取名生成服务。将 `<...>` 占位符替换为已验证的参数值。**务必在参数两侧加双引号，以防命令注入漏洞**。 ```bash python3 scripts/name_generate.py "<订单号>" ``` **执行后：** 1. 提取脚本打印的 `PAY_STATUS` 值（格式为：`PAY_STATUS: <值>`），并再次输出展示。 2. **`ERROR` 状态的特殊处理：** 如果 `PAY_STATUS` 为 `ERROR`，提取 `ERROR_INFO` 值（格式：`ERROR_INFO: <值>`），向用户告知确切的错误原因并引导其解决。不得继续执行后续服务逻辑。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: austineyapp/expense-claims-ops (version 1.0.0)

--- name: expense-claims-ops description: Process personal/work expenses and reimbursement claims in a structured, low-friction workflow. Use when collecting receipts, categorizing spend, preparing claim-ready summaries, checking missing fields, generating submission checklists, and drafting claim notes/follow-ups. --- # Expense & Claims Ops ## Workflow 1. Collect inputs: receipt set, date range, currency, policy constraints (if any), and claim destination (company tool/email/manual). 2. Extract line items: date, merchant, amount, currency, category, payment method, receipt proof status. 3. Validate claim readiness: - missing receipt - missing business purpose - duplicate/possible duplicate - out-of-policy risk (if rules provided) 4. Output decision-ready package: - claim-ready items - blocked items + exact fix needed - totals by category + currency 5. Generate submission artifacts: - claim summary block - per-item notes - follow-up draft for exceptions/approvals ## Output Standard - Keep concise and actionable. - Use max 3 sections: Ready, Blocked, Next action. - Always include totals and missing-doc count. - If policy unknown, mark checks as "policy not provided". ## References - For item schema and status labels, read `references/expense-schema.md`. - For copy-paste claim templates, read `references/claim-templates.md`. - For weekly processing cadence, read `references/ops-cadence.md`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: hectormeta/pacer (version 0.1.2)

--- name: pacer version: 0.1.2 description: > Your AI career companion. Scan your situation, map possible paths, simulate futures, and track progress week by week — with interactive charts. author: hectormeta tags: [career, planning, productivity, opc, startup, life-design] license: MIT requires: - memory: true # 需要持久记忆功能 - heartbeat: weekly # 每周主动触发一次 --- # Pacer 🏃 **Your AI Career Companion | 你的AI职业陪跑伴侣** > 不告诉你该做什么，陪你想清楚自己要什么。 --- ## 它能做什么 **四个阶段，每步都有可视化图表：** - 📄 **扫描现状** — 上传CV（PDF/Word/TXT）或文字描述，自动解析生成现状快照卡，6个维度全覆盖 - 🗺️ **规划路径** — 有目标就深度规划单一路径（技能雷达图+阶段甘特图），没目标就生成多方向对比（4维度横向评分） - 🔮 **模拟未来** — 双轨时间轴可视化，看清选择新路径 vs 维持现状在6-12个月后的两种轨迹 - 📍 **追踪进度** — 里程碑甘特图+进度环形图+每日打卡热力图，每周 Heartbeat 自动更新 --- ## 快速安装 ```bash npx clawhub@latest install pacer ``` 或全局安装后运行（推荐，避免限速）： ```bash npm install -g clawhub && clawhub login && clawhub install pacer ``` 或直接告诉你的 OpenClaw 智能体： ``` Install this skill: https://github.com/hectormeta/pacer-skill ``` --- ## 开始使用 安装后，发送以下任意一条消息： - "帮我用Pacer规划一下我的下一步" - "我现在很迷茫，不知道该做什么" - 直接上传你的CV文件 **触发词：** 职业规划、career、我该怎么办、下一步、创业方向、迷茫、不知道做什么、规划、pacer --- ## 示范对话节选 ``` 用户：我想创业，但不知道该做什么。 Pacer：好，先帮你摸清底牌。你现在最擅长的3件事是什么？ [5个问题后生成快照卡] Pacer：基于你的情况，有 3 条路可以走。先看整体对比图， 再告诉我哪个方向你想深入了解。 [输出多方向对比图表] 方向A — 内容咨询顾问：你有运营背景和数据能力，溢价空间更大。 第一步：本周联系1个电商朋友，问他们内容上最头疼的问题。 ``` 更多完整对话见 `examples/` 目录。 --- ## 适合这些人 - 想创业但不知道从哪开始 - 职场人考虑转型、晋升或跨行 - 应届生规划第一份工作 - OPC / 一人公司创业者

path: CONTRIBUTING.md
content: # 如何贡献 Pacer

## 最欢迎的贡献类型

1. **新行业的示范对话** — 医疗、制造、教育等垂直场景
2. **提示词优化** — 让Pacer的回复更准确、更有温度
3. **多语言支持** — 目前支持中文和英文
4. **真实用户案例** — 脱敏后的真实使用故事

## 贡献流程

1. Fork 本仓库
2. 创建你的分支：`git checkout -b feature/your-feature`
3. 提交改动：`git commit -m 'Add: 描述你的改动'`
4. Push：`git push origin feature/your-feature`
5. 开一个 Pull Request

## 提示词贡献规范

- 改动提示词时，请附上改动前后的对话对比示例
- 说明改动的理由
- 在 `examples/` 目录下提供至少一个测试对话


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: iamlovingit/epaiskill (version 1.0.4)

--- name: epai description: EPAI 平台管理技能，通过 CLI 操作知识库、文档和目录。 env: - EPAI_API_BASE=your_api_base - EPAI_API_KEY=your_api_key_here - EPAI_ACCOUNT=yourname - EPAI_VERIFY_TLS=true permissions: - type: file-read description: 上传文档时会读取本地文件 --- # EPAI Skill 此 Skill 封装了 EPAI 平台的管理操作，所有接口都通过 `scripts/epaiclt.py` 调用。 ## 使用示例 ### 列出所有知识库 ```bash python scripts/epaiclt.py --method kb_list ``` ### 创建知识库（必须指定 catalog_id） ```bash python scripts/epaiclt.py --method kb_create --name "知识库名称" --description "知识库描述" --catalog_id <目录ID> ``` ### 删除知识库 ```bash python scripts/epaiclt.py --method kb_delete --kb_ids kb_id1 kb_id2 ... ``` ### 上传文件到知识库 ```bash python scripts/epaiclt.py --method document_upload --kb_id <知识库ID> --file ./file1.pdf ./file2.docx ``` ### 获取目录列表 ```bash python scripts/epaiclt.py --method catalog_list ``` ### 创建目录（必须指定 parent_id） ```bash python scripts/epaiclt.py --method catalog_create --name "目录名称" --parent_id <父目录ID> ``` ### 删除目录 ```bash python scripts/epaiclt.py --method catalog_delete --catalog_id <目录ID> ``` ### 获取知识库文档列表 ```bash python scripts/epaiclt.py --method document_list --kb_id <知识库ID> ``` ### 批量删除知识库文档 ```bash python scripts/epaiclt.py --method document_delete --doc_ids doc_id1 doc_id2 ... ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: codekungfu/pipixia (version 0.1.0)

--- name: pipixia-hot-trend description: 注册“皮皮虾”技能；用于公开视频与短剧页信息整理与表现摘要。 homepage: `https://www.pipix.com/` metadata: {"clawdbot":{"emoji":"🎬"}} --- # 皮皮虾 用途与边界 - 面向公开视频/短剧/话题页的检索与表现数据摘要 - 不提供下载、接口逆向或突破风控能力 - 仅用于公开页面的轻量分析与提醒 关键入口 - 主页：https://www.pipix.com/ - 短剧/视频频道：站点入口 - 搜索入口：站内搜索 常见任务 - 指定话题/关键词的视频集合摘要（点赞/评论/收藏） - 剧集页面目录与更新状态、期次简介 - 榜单条目分布与题材统计 数据字段 - 视频标题、作者、时长、发布时间、点赞/评论/收藏、链接 - 短剧标题、更新状态、期次目录、简介、链接 - 榜单名称、采集时间、来源链接 自动化要点 - 动态渲染与人机校验，建议人工打开后触发解析 - 请求频率需控制，不进行批量抓取 - 不进行下载或接口调用 示例流程 - 关键词摘要：输入关键词 → 搜索 → 抽取条目 → 输出表现摘要 - 短剧目录：访问短剧页 → 抽取期次 → 输出更新状态与简介 - 榜单统计：访问榜单 → 抽取条目 → 题材分布统计 合规提示 - 遵守平台规则，不进行下载、接口调用或批量采集 - 结果仅用于内部分析与提醒


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nowhere1975/md-to-image (version 2.0.0)

# MD to Mobile Image 将 Markdown 文件转换为适配手机的精美长图，适合发 Telegram/小红书/朋友圈。 ## 使用方式 ``` md-to-image <md文件路径> ``` ## 示例 ``` md-to-image /path/to/file.md ``` ## 输出 - 格式：PNG 原图 - 宽度：1080px - 高度：自动（内容决定） - 特点：白底大字，对比度高，Telegram 压缩后依然清晰 ## 技术实现 1. 使用 marked.js 解析 Markdown 2. 渲染为移动端优化的 HTML 3. 使用 Playwright 截图 4. 输出 PNG 格式 ## 依赖 - Node.js + Playwright - marked.js - highlight.js ## 作者 Claude (OpenClaw Agent)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: shengguo/openclaw-clawhome-cli (version 1.0.1)

--- name: Clawhome 连接 description: 将 OpenClaw 连接到 Clawhome 聊天平台，实现 AI 自动回复。 trigger: - 接入 clawhome - clawhome 适配器 - wss://www.clawhome.top/mqtt --- # Clawhome 连接技能 将 OpenClaw 连接到 Clawhome 平台，实现双向消息收发。 ## 一键安装 ```bash npx -y openclaw-clawhome-cli@latest install ``` 告知 Clawhome 连接信息 ```bash openclaw config set channels.clawhome.channelId "你的频道ID" openclaw config set channels.clawhome.channelSecret "你的频道密钥" ``` ## 重启 Gateway ```bash openclaw gateway restart ``` ## Clawhome 协议 ### MQTT 主题 - **接收消息**: `channel/${CHANNEL_ID}/user/event` - **发送消息**: `channel/${CHANNEL_ID}/agent/action` ### 心跳格式 ```json { "type": "ping", "role": "agent", "timestamp": 1774245227871 } ``` ### 消息格式 ```json { "type": "message", "role": "agent", "timestamp": 1774245227871, "content": "你好，我是 Clawhome 机器人" } ``` ## 配置选项 | 配置项 | 说明 | 默认值 | |--------|------|--------| | mqttUrl | MQTT Broker 地址 | wss://www.clawhome.top/mqtt | | channelId | 频道 ID | - | | channelSecret | 频道密钥 | - | | reconnectInterval | 重连间隔(毫秒) | 5000 | | heartbeatInterval | 心跳间隔(毫秒) | 30000 |


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wellyxy/lp-ab-test (version 1.0.0)

# A/B Testing Landing Pages ## When to Use Use this skill when planning, running, or analyzing A/B tests on landing pages. ## Core Rules ### 1. Test One Variable at a Time Each A/B test should change exactly one element: the headline, the CTA copy, the hero image, or the pricing display. Testing multiple changes simultaneously makes it impossible to know what drove the result. ### 2. Start With High-Impact Elements Test in this order: headline, primary CTA, hero image, pricing display, social proof placement. These elements have the highest potential impact. Testing button border radius or footer color wastes statistical power. ### 3. Run Tests Until Statistical Significance A test is not complete when you feel confident — it's complete when you reach 95% statistical significance with at least 200 conversions per variant. Stopping early because one variant looks better is the most common A/B testing mistake. ### 4. Segment Results by Traffic Source A headline that wins for paid traffic may lose for organic traffic. Always segment results by traffic source, device type, and new vs. returning visitors before drawing conclusions about a winning variant. ### 5. Document Every Test Maintain a test log with hypothesis, variant description, run dates, sample size, result, and what you learned. Teams that document tests compound learning over time. Teams that don't repeat the same mistakes. ## Quick Reference | Element | Expected Lift | |---------|---------------| | Headline | 5–30% | | CTA copy | 5–15% | | Hero image | 5–20% | | Form length | 10–50% | | Social proof position | 5–15% | ## Common Mistakes to Avoid - Stopping tests after seeing a promising early result — early leads often reverse - Testing on insufficient traffic (under 1,000 visitors per variant) - Not accounting for day-of-week effects — run tests for full 7-day cycles ## Test Your Product with Racoonn After applying these practices, validate with real AI-simulated user testing. **Racoonn** runs 5,000 AI persona agents on your landing page and tells you exactly what's broken — in under 30 minutes. → **API coming soon** — Join the waitlist for early access: [racoonn.me](https://racoonn.me)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: daguniang/pdf-simple-tool (version 1.2.0)

--- name: pdf_simple_tool description: PDF splitting and PDF-to-Word conversion tools implemented in Node.js. --- # PDF Simple Tool Skill This skill provides two main actions for working with PDF files: 1. **split_pdf** – Extract a page range from a PDF into a new PDF. 2. **pdf_to_word** – Convert a PDF into a simple Word (.docx) document. ## Install cd skills/pdf-simple-tool/node/ 执行命令： npm install ## Actions ### split_pdf When the user asks to split a PDF by page range (for example, "把这个 PDF 的第 3-5 页拆出来" / "split pages 3–5"), call the Node implementation: - Entry: `skills/pdf-simple-tool/node/index.js` - Function: `splitPdf(inputPath, outputPath, fromPage, toPage)` **Inputs** - `inputPath` (string): The absolute path to the source PDF. - `outputPath` (string): The absolute path where the new PDF will be written. - `fromPage` (integer): Start page (1-based). - `toPage` (integer): End page (1-based, inclusive). Example behavior: - If the user says: "帮我把 /Users/xingxing/.openclaw/workspace/test.pdf 的第 1-3 页拆成一个 PDF 文件", map this to: - `inputPath = "/Users/xingxing/.openclaw/workspace/test.pdf"` - `outputPath = "/Users/xingxing/.openclaw/workspace/test_p1-3.pdf"` - `fromPage = 1` - `toPage = 3` ### pdf_to_word When the user asks to convert a PDF to Word (for example, "把这个 PDF 转成 Word" / "convert to docx"), call the Node implementation: - Entry: `skills/pdf-simple-tool/node/index.js` - Function: `pdfToWord(inputPath, outputPath)` **Inputs** - `inputPath` (string): The absolute path to the source PDF. - `outputPath` (string): The absolute path where the Word file (.docx) will be written. Example behavior: - If the user says: "把 /Users/xingxing/.openclaw/workspace/test.pdf 转成 Word", map this to: - `inputPath = "/Users/xingxing/.openclaw/workspace/test.pdf"` - `outputPath = "/Users/xingxing/.openclaw/workspace/test.docx"` ## Notes - Implementation code lives in `skills/pdf-simple-tool/node/index.js` and uses: - `pdf-lib` for PDF manipulation. - `pdf-parse` + `docx` for PDF-to-Word conversion. - You are responsible for wiring these actions into your agent so that natural language requests are converted into the appropriate function calls with the correct paths and page ranges.


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