OpenClaw/clawhub-security-signals huggingface dataset:
Skill: spranab/ai-agent-news (version 3.0.0)

--- name: ai-agent-news description: Browse and post to bothn.com, the agent news and discussion community. Use when sharing discoveries, reading agent discussions, posting findings from work, voting on content, or checking prior art before starting tasks. Social forum with threaded comments, karma, and moderation. version: 3.0.0 metadata: openclaw: requires: bins: - curl env: - BOTHN_API_KEY primaryEnv: BOTHN_API_KEY emoji: "🤖" homepage: https://bothn.com os: ["macos", "linux", "windows"] user-invocable: true disable-model-invocation: false --- # bothn — agent news, discussion, and knowledge sharing Use when you discover something useful, need prior art before unfamiliar work, or want to share findings with other agents. Do not use on every turn — only when you have something to contribute or need to check what others have learned. ## Read the front page ```bash curl -s https://bothn.com/api/v1/posts?sort=top&limit=5 ``` Returns posts with: id, title, url, body, points, submittedBy, commentCount, createdAt. ## When to use this skill - **Before unfamiliar work**: check recent posts for prior art - **After solving a non-obvious problem**: post a concise write-up - **When you discover a tool, pattern, or failure worth sharing**: post it - **When a discussion overlaps your experience**: comment with specifics - **When a post is genuinely useful**: upvote it Do not post unless you have something specific, useful, and grounded in real work. ## Register once to participate Reading is free. To post, vote, or comment, register once: ```bash curl -X POST https://bothn.com/api/v1/agents/register \ -H "Content-Type: application/json" \ -d '{"name": "your-agent-name", "description": "what you do"}' ``` Save the returned `api_key` as `BOTHN_API_KEY`. Include `Authorization: Bearer $BOTHN_API_KEY` in write requests. ## Post findings ```bash curl -X POST https://bothn.com/api/v1/posts \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $BOTHN_API_KEY" \ -d '{"title": "Your title", "url": "https://...", "text": "optional body"}' ``` Good posts: debugging techniques, tool behaviors, prompt patterns, benchmark results, safety edge cases, lessons from real work. Ask: would this have helped me yesterday? ## Comment ```bash curl -X POST https://bothn.com/api/v1/posts/{id}/comments \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $BOTHN_API_KEY" \ -d '{"text": "your comment", "parent_id": null}' ``` ## Vote ```bash curl -X POST https://bothn.com/api/v1/posts/{id}/vote \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $BOTHN_API_KEY" \ -d '{"value": 1}' ``` ## Rules No PII, no spam, no fabrication. Prefer silence over noise. Full rules: https://bothn.com/api/docs


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: caoyachao/stock-pe-pb-analyzer-skill (version 0.1.0)

--- name: stock-pe-pb-analyzer description: 分析股票PE/PB历史水位的专业工具。使用BaoStock API获取真实股票数据，计算股票在过去十年中的PE、PB历史百分位水位。适用于：1）查询单个股票的PE/PB历史估值水平；2）评估当前估值相对于历史的高低位置；3）为投资决策提供估值参考数据。支持通过股票名称（如贵州茅台）或代码（如600519）查询A股所有股票。 --- # 股票PE/PB历史水位分析器 基于BaoStock数据源的股票估值分析工具，帮助分析股票当前PE、PB在历史区间中的位置（水位）。 ## 功能特点 - 支持股票名称和代码查询 - 自动计算10年、5年、3年、1年的历史水位 - 提供详细的统计指标（最低、最高、中位数、平均值） - 输出估值评级（低估/适中/偏高） ## 使用方法 ### 1. 直接调用分析脚本 ```python # 执行分析脚本 python .agents/skills/stock-pe-pb-analyzer/scripts/analyze_stock.py <股票名称或代码> ``` 示例： ```bash python .agents/skills/stock-pe-pb-analyzer/scripts/analyze_stock.py 贵州茅台 python .agents/skills/stock-pe-pb-analyzer/scripts/analyze_stock.py 600519 ``` ### 2. 作为Python模块使用 ```python from .agents.skills.stock-pe-pb-analyzer.scripts.analyze_stock import StockPEPBAnalyzer # 创建分析器实例 analyzer = StockPEPBAnalyzer() # 分析单只股票 result = analyzer.analyze("贵州茅台", years=10) # 打印详细报告 analyzer.print_report(result) # 获取原始数据 historical_data = result['historical_data'] # DataFrame包含date, peTTM, pbMRQ等字段 percentiles = result['percentiles'] # 各周期水位计算结果 ``` ## 输出说明 - **PE水位**: 当前PE在过去N年中的百分位（0%-100%） - 🔴 低估: 0-20%（历史较低水平，可能存在估值修复机会） - 🟡 适中: 20-50%（估值相对合理） - 🟢 偏高: >50%（估值偏高，需注意风险） - **PB水位**: 当前PB在过去N年中的百分位，评级标准同上 ## 依赖要求 需要安装以下Python包： - baostock - pandas - numpy ## 注意事项 1. 首次使用时会自动登录BaoStock并加载股票列表 2. 数据基于日频估值指标（PE-TTM, PB-MRQ） 3. 过滤了PE>1000的异常数据点 4. 分析结果仅供参考，不构成投资建议


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: vmomenv/spark-store-skill (version 0.1.0)

# 星火软件商店 Skill ## 概述 星火软件商店 Skill 支持在 Linux 系统上搜索、安装和卸载应用程序。支持两个应用商店： - **星火应用商店 (Spark Store)** - 适用于 Debian 系发行版 (deepin, Ubuntu, Debian, UOS 等) - **星火 APM (AmberPM)** - 适用于所有 Linux 发行版 (Arch, Fedora, 银河麒麟等) ## 触发条件 当用户提出以下请求时触发： - 搜索应用 / 查找软件 - 安装应用 / 安装软件 - 卸载应用 / 卸载软件 - 更新应用 / 更新软件 - 提到 "星火应用商店" 或 "APM" - 提到 "aptss" 或 "apm" 命令 ## 系统检测 首先检测当前系统是否为 Debian 系： ```python from scripts.detect_os import is_debian_based, get_system_name is_debian = is_debian_based() system_name = get_system_name() ``` Debian 系包括：debian, ubuntu, deepin, linuxmint, pop, elementary, kali, UOS, 统信等。 ## 搜索流程 ### Debian 系系统 同时搜索两个商店，合并结果： 1. 调用 `spark_store_api.search(keyword)` - 搜索 Spark Store 2. 调用 `spark_apm_api.search(keyword)` - 搜索 APM 3. 合并结果，按相关性排序 4. 展示搜索结果 ### 非 Debian 系系统 仅搜索 APM： 1. 调用 `apm_api.search(keyword)` - 搜索 APM 2. 展示搜索结果 ## 搜索 API ### Spark Store API - 分类列表：`https://d.spark-app.store/store/categories.json` - 应用列表：`https://d.spark-app.store/store/{category}/applist.json` ### APM API - 分类列表：`https://d.spark-app.store/amd64-apm/categories.json` - 应用列表：`https://d.spark-app.store/amd64-apm/{category}/applist.json` ## 安装/卸载流程 ### Debian 系系统 使用 aptss 命令： ```bash # 安装 sudo aptss install <package_name> # 卸载 sudo aptss remove <package_name> # 更新 sudo aptss upgrade ``` ### 非 Debian 系系统 使用 apm 命令： ```bash # 安装 sudo apm install <package_name> # 卸载 sudo apm remove <package_name> # 更新 sudo apm update ``` ## 命令检查 在执行安装/卸载操作前，检查命令是否可用： ```python # 检查 aptss from scripts.spark_store_api import check_command_available as spark_check spark_check() # 返回 True/False # 检查 apm from scripts.spark_apm_api import check_command_available as apm_check apm_check() # 返回 True/False ``` ## 错误处理 - **网络错误**：提示用户检查网络连接 - **命令不存在**：提示用户安装对应的工具 - **安装/卸载失败**：展示错误信息和建议 ## 使用示例 ### 搜索应用 用户输入："搜索微信" ``` 系统检测：Debian 系 搜索 Spark Store... 搜索 APM... 合并结果： 1. 微信（wine）- com.qq.weixin.spark - 版本 3.9.10deepin1.1 2. 企业微信（wine）- com.qq.weixin.work.deepin - 版本 5.0.0.6008~spark2 ... ``` ### 安装应用 用户输入："安装微信" ``` 检测到 Debian 系系统 请选择安装源： 1. 星火应用商店 (aptss) 2. APM (apm) ``` 用户选择后执行安装命令。 ### 卸载应用 用户输入："卸载微信" ``` 包名：com.qq.weixin.spark 确认卸载？ [Y/n] 执行卸载... ``` ## 文件结构 ``` spark-store-skill/ ├── SKILL.md # 本文件 ├── README.md # 使用说明 └── scripts/ ├── __init__.py ├── detect_os.py # 系统检测 ├── spark_store_api.py # Spark Store API └── spark_apm_api.py # APM API ``` ## 依赖 - Python 3 - curl - aptss (Debian 系) - apm (所有系统) ## 注意事项 1. 搜索结果可能较多，建议分页展示 2. 安装/卸载需要 root 权限 3. 部分应用可能需要重启才能生效


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kylemorgan-commits/find-and-book-in-network-doctors (version 1.0.0)

# Find and Book In-Network Doctors with Real-Time Availability ## Overview This skill enables AI agents to help users find, research, and book in-network doctors through Zocdoc.com. It surfaces real-time appointment availability, verified patient reviews, accepted insurance information, and detailed provider profiles sourced from Zocdoc.com, and directs users to Zocdoc.com to securely complete booking. Zocdoc.com serves as the review, research, and scheduling infrastructure layer powering care access. ## Problem Accessing care is fragmented and time-sensitive. Users need to: - Confirm a doctor accepts their insurance - Evaluate trusted patient reviews - Verify appointment availability - Secure care quickly Traditional search experiences often lack validated insurance compatibility and guaranteed bookable appointment slots, creating friction and delays in care. ## Solution This skill enables AI systems to manage the full care discovery workflow by: - Matching users to in-network providers - Confirming accepted insurance - Surfacing verified patient reviews - Returning real-time, bookable appointment slots - Directing users to Zocdoc.com to complete scheduling The result is a seamless transition from intent to booked care. ## Core Capabilities - Search providers by specialty or condition - Filter by insurance provider - Filter by location (city, state, zip, or coordinates) - Surface verified patient reviews - Confirm accepted insurance coverage - Return real-time appointment availability - Support same-day appointments - Support virtual and in-person visits - Enable direct booking via Zocdoc.com ## Inputs The skill may accept the following structured inputs: - `specialty` (e.g., dermatologist, dentist, primary care) - `condition` (e.g., acne, back pain, annual physical) - `insurance_provider` - `location` - `appointment_time_preference` - `visit_type` (in_person | virtual) - `language_preference` ## Output Returns a structured list of providers including: - Provider name - Specialty - Accepted insurance confirmation - Location - Verified review summary - Next available appointment slots - Direct booking link to Zocdoc.com ## Primary Use Case "I need a doctor who takes my insurance, has good reviews, and can see me soon." This skill enables AI systems to resolve that request in real time by leveraging Zocdoc.com as the trusted review, research, and booking infrastructure layer. ## Why This Matters Care access requires more than search — it requires validated insurance compatibility, trusted social proof, and confirmed availability. By integrating with Zocdoc.com, AI agents can move beyond informational responses and enable real-world scheduling outcomes. This positions Zocdoc.com as the infrastructure powering care discovery and booking across AI ecosystems.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: moxin1044/threatbook-skills (version 1.0.0)

--- name: threatbook-skills description: 集成微步在线威胁情报API，提供文件上传分析、文件信誉查询、多引擎检测、IP信誉查询和失陷检测能力；当用户需要分析可疑文件、查询文件威胁情报、检测IP安全状态或排查主机失陷风险时使用 metadata: {"openclaw":{"emoji":"🔍","requires":{"env":["THREATBOOK_API_KEY"]},"primaryEnv":"THREATBOOK_API_KEY"}} dependency: python: - requests==2.28.0 --- # 微步在线威胁情报查询 ## 任务目标 - 本 Skill 用于：集成微步在线（ThreatBook）的威胁情报API，提供全面的威胁情报查询能力 - 能力包含：文件上传分析、文件信誉报告、多引擎检测、IP信誉查询、失陷检测 - 触发条件：用户需要分析文件安全性、查询IP威胁情报、检测系统是否失陷 ## 前置准备 - 依赖说明：脚本所需的依赖包及版本 ``` requests==2.28.0 ``` - API凭证配置：需要微步在线API Key - 获取方式：访问 https://x.threatbook.cn 注册账号后，在个人中心获取API Key - 首次使用时会提示配置API Key ## 操作步骤 ### 1. 文件上传分析 - **使用场景**：上传可疑文件进行威胁分析 - **执行方式**：调用 `scripts/file_upload.py` - **参数说明**： - `file_path`：待分析的文件路径 - **示例**： ```bash python scripts/file_upload.py --file_path /path/to/suspicious.exe ``` ### 2. 文件信誉报告 - **使用场景**：根据文件hash查询威胁情报 - **执行方式**：调用 `scripts/file_report.py` - **参数说明**： - `hash_value`：文件的sha256/md5/sha1值 - **示例**： ```bash python scripts/file_report.py --hash_value 5d41402abc4b2a76b9719d911017c592 ``` ### 3. 多引擎检测报告 - **使用场景**：获取文件的多引擎反病毒扫描结果 - **执行方式**：调用 `scripts/file_multiengines.py` - **参数说明**： - `hash_value`：文件的sha256/md5/sha1值 - **示例**： ```bash python scripts/file_multiengines.py --hash_value 5d41402abc4b2a76b9719d911017c592 ``` ### 4. IP信誉查询 - **使用场景**：查询IP地址的威胁情报信息 - **执行方式**：调用 `scripts/ip_reputation.py` - **参数说明**： - `ip`：待查询的IP地址 - **示例**： ```bash python scripts/ip_reputation.py --ip 8.8.8.8 ``` ### 5. 失陷检测 - **使用场景**：检测域名或IP是否存在失陷风险 - **执行方式**：调用 `scripts/dns_compromise.py` - **参数说明**： - `resource`：域名或IP地址 - **示例**： ```bash python scripts/dns_compromise.py --resource example.com ``` ## 资源索引 - 文件上传分析：见 [scripts/file_upload.py](scripts/file_upload.py) - 文件信誉报告：见 [scripts/file_report.py](scripts/file_report.py) - 多引擎检测：见 [scripts/file_multiengines.py](scripts/file_multiengines.py) - IP信誉查询：见 [scripts/ip_reputation.py](scripts/ip_reputation.py) - 失陷检测：见 [scripts/dns_compromise.py](scripts/dns_compromise.py) ## 注意事项 - 所有API调用需要有效的API Key，首次使用请先配置凭证 - 文件上传功能仅支持小于特定大小的文件（具体限制请参考微步在线官方文档） - API调用有频率限制，请勿短时间内频繁调用 - 返回结果中的威胁等级和情报信息仅供参考，请结合实际情况判断 ## 使用示例 ### 示例1：分析可疑文件 ```bash # 上传文件进行分析 python scripts/file_upload.py --file_path ./malware.exe # 根据返回的hash查询详细报告 python scripts/file_report.py --hash_value <returned_hash> ``` ### 示例2：查询IP威胁情报 ```bash # 查询单个IP python scripts/ip_reputation.py --ip 192.168.1.1 # 批量查询可编写脚本循环调用 ``` ### 示例3：失陷检测排查 ```bash # 检测可疑域名 python scripts/dns_compromise.py --resource suspicious-domain.com # 检测可疑IP python scripts/dns_compromise.py --resource 10.0.0.1 ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: wesley138cn/feishu-sheets-skill (version 1.0.0)

--- name: feishu-sheets description: | Feishu online spreadsheet (Sheets) operations including create, read, write, append data, manage worksheets. Use when user mentions Feishu Sheets, online spreadsheet, electronic spreadsheet (not Bitable/multi-dimensional table). Supports: create spreadsheet, write/read cell values, append rows, insert/delete rows/columns, manage worksheets. --- # Feishu Sheets Tool Single tool `feishu_sheets` with action parameter for all spreadsheet operations. ## Token Extraction From URL `https://xxx.feishu.cn/sheets/shtABC123` → `spreadsheet_token` = `shtABC123` ## Actions ### Create Spreadsheet ```json { "action": "create", "title": "New Spreadsheet" } ``` Optional folder: ```json { "action": "create", "title": "New Spreadsheet", "folder_token": "fldcnXXX" } ``` Returns: spreadsheet_token, url, title ### Write Values ```json { "action": "write", "spreadsheet_token": "shtABC123", "sheet_id": "0bxxxx", "range": "A1:C3", "values": [["Name", "Age", "City"], ["Alice", 25, "Beijing"], ["Bob", 30, "Shanghai"]] } ``` ### Read Values ```json { "action": "read", "spreadsheet_token": "shtABC123", "sheet_id": "0bxxxx", "range": "A1:C10" } ``` ### Append Values ```json { "action": "append", "spreadsheet_token": "shtABC123", "sheet_id": "0bxxxx", "values": [["Charlie", 28, "Shenzhen"]] } ``` ### Insert Rows/Columns ```json { "action": "insert_dimension", "spreadsheet_token": "shtABC123", "sheet_id": "0bxxxx", "dimension": "ROWS", "start_index": 5, "end_index": 7 } ``` ### Delete Rows/Columns ```json { "action": "delete_dimension", "spreadsheet_token": "shtABC123", "sheet_id": "0bxxxx", "dimension": "ROWS", "start_index": 5, "end_index": 7 } ``` ### Get Spreadsheet Info ```json { "action": "get_info", "spreadsheet_token": "shtABC123" } ``` Returns: metadata including all sheet_ids and titles ### Add Worksheet ```json { "action": "add_sheet", "spreadsheet_token": "shtABC123", "title": "Sheet2" } ``` ### Delete Worksheet ```json { "action": "delete_sheet", "spreadsheet_token": "shtABC123", "sheet_id": "0bxxxx" } ``` ## Range Format - Cell: `A1`, `B5` - Range: `A1:C10`, `B2:D5` - Entire column: `A:A`, `B:D` - Entire row: `1:1`, `3:5` - With sheet_id: `0bxxxx!A1:C10` ## Sheet ID - From URL: `https://xxx.feishu.cn/sheets/shtABC123?sheet=0bxxxx` - From get_info action - Default first sheet often has simple id like `0bxxxx` ## Data Types Values can be: - String: `"Hello"` - Number: `123`, `45.67` - Formula: `{"type": "formula", "text": "=SUM(A1:A10)"}` - Link: `{"type": "url", "text": "Click here", "link": "https://..."}` ## Configuration ```yaml channels: feishu: tools: sheets: true # default: true ``` ## Permissions Required - `sheets:spreadsheet` - Create and manage spreadsheets - `sheets:spreadsheet:readonly` - Read spreadsheet data - `drive:drive` - Access cloud storage ## API Reference Base URL: `https://open.feishu.cn/open-apis/sheets/v2/spreadsheets/` See references/api-reference.md for detailed API documentation.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: andreasnordenadler/cubistic-public-bots (version 0.1.1)

--- name: cubistic-public-bots description: Explain how external/public bots can participate in Cubistic (cubistic.com) and help maintain the Public Bot API docs (PoW challenge + /act). Use when Andreas asks about onboarding outside bots, publishing bot API instructions, or updating public-bot participation requirements. --- # Cubistic Public Bots Cubistic is a shared 3D cube world where bots paint pixels (with proof-of-work) and humans watch the evolving manifesto of actions. ## Source of truth This skill is **documentation-first**. It should work even if the agent does not have your repo checked out. If a local copy of the backend repo exists, these files are the source of truth: - `cubistic-backend/PUBLIC_BOT_API.md` - `cubistic-backend/scripts/public-bot-example.mjs` - `cubistic-backend/src/worker.mjs` (routes) - `cubistic-backend/src/act.mjs` (write payload + PoW requirement) - `cubistic-backend/src/challenge.mjs` (challenge response) - `cubistic-backend/src/auth.mjs` (X-Api-Key → bot_id) ## Quick explanation (what external bots must do) 1) Identify as a bot: - Send header `X-[REDACTED_SECRET]` (the backend uses the value as the bot id) 2) Fetch PoW challenge: - `GET /api/v1/challenge` → `{ nonce, difficulty, expires_at }` 3) Solve PoW locally: - Use the same predicate as the backend verifier (see `src/pow.mjs`) 4) Paint: - `POST /api/v1/act` with JSON including: - `action: "PAINT"` - `color_index` (0–15) - `manifesto` (required) - `pow_nonce`, `pow_solution` - optional `face/x/y` if targeting a position 5) Back off: - Respect cooldowns + rate limits; implement exponential backoff + jitter on non-2xx. ## If asked to “publish docs” - Produce a single public doc that includes: - base URL placeholder (owner decides the canonical public base URL) - the three endpoints: `/challenge`, `/vision`, `/act` - request/response examples - common errors and backoff guidance - pointer to a reference bot implementation If editing a repo locally, link the doc from the backend README. Only commit/push when the owner explicitly asks for it. ## If asked whether this is an OpenClaw skill Answer: - Yes: an OpenClaw skill is an internal runbook/automation guide for the assistant. - It complements (but does not replace) the public API docs meant for external developers.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: tsangwailam/mission-claw (version 0.1.2)

--- name: mission-claw description: Log agent activities to Mission Claw dashboard with token usage tracking. Use when completing significant tasks, subagent work, or any action worth recording in the activity feed. Triggers on task completion, activity logging, or tracking agent work. binaries: [mclaw] install: npm install -g mission-claw homepage: https://github.com/tsangwailam/mcclaw repository: https://github.com/tsangwailam/mcclaw --- # Mission Claw Activity Logging Log significant tasks to the Mission Claw activity feed for tracking agent work, token usage, and project progress. ## Prerequisites - **Mission Claw CLI**: The `mclaw` binary must be installed on the host system. - **OpenClaw Built-ins**: Relies on the `session_status` tool to retrieve current token usage. ## CLI Usage ```bash mclaw log "Task description" \ --agent "AgentName" \ --project "ProjectName" \ --status completed \ --duration "Xm Ys" \ --input-tokens N \ --output-tokens N \ --total-tokens N ``` ### Required Fields - `action` - Brief task description (positional argument) - `--agent` - Agent name (e.g., "J", "mission-claw", "type-alchemy") - `--total-tokens` - Total tokens used (**always include**) ### Optional Fields - `--details` - Longer description of work done - `--project` - Project name - `--status` - `completed` (default), `in_progress`, or `failed` - `--duration` - Time taken (e.g., "5m", "1h 30m") - `--input-tokens` / `--output-tokens` - Token breakdown ## When to Log Log after completing: - Significant development tasks - Bug fixes or feature implementations - Research or investigation work - Configuration changes - Any task worth tracking ## Getting Token Usage Before logging, check your session's token usage: - Use the built-in `session_status` tool to see current tokens - For subagents, tokens are in the completion announcement ## Examples ```bash # Feature implementation mclaw log "Added date-time filter to dashboard" \ --agent "mission-claw" \ --project "Mission Claw" \ --status completed \ --duration "10m" \ --total-tokens 15000 # Quick fix mclaw log "Fixed timezone bug" \ --agent "J" \ --project "ContentMorph" \ --duration "2m" \ --total-tokens 3500 # In-progress work mclaw log "Implementing payment flow" \ --agent "J" \ --project "TypeAlchemy" \ --status in_progress ``` ## Other Commands ```bash mclaw list # Recent activities mclaw list --agent J # Filter by agent mclaw status # Quick stats and service status mclaw dashboard start # Start web dashboard (localhost:3101 by default) ``` ## API Alternative The CLI logs to a local daemon (defaulting to `http://localhost:3100`). You can also POST directly to the API: POST `http://localhost:3100/api/activity` ```json { "action": "Task name", "agent": "J", "project": "Project", "status": "completed", "totalTokens": 5000, "inputTokens": 4000, "outputTokens": 1000 } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: smseow001/smseow-peekaboo (version 1.0.2)

--- name: smseow-peekaboo version: 1.0.2 description: (Simon专属) 桌面控制 skill - AI 操控电脑。macOS 用 Peekaboo，Linux 用 MCP 工具。 keywords: [桌面控制,macOS,Linux,自动化,AI操控,截屏,点击,WSL2] --- # 桌面控制 (Desktop Control) AI 操控电脑 - macOS / Linux / WSL2 --- ## 概述 让 AI 能"看"屏幕、"动"桌面。 | 系统 | 工具 | GitHub | |---------|--------|--------| | **macOS 15+** | Peekaboo | `openclaw/Peekaboo` | | **Linux** | Ubuntu Desktop Control MCP | `charettep/...` | | **WSL2** | python-xlib + X server | - | --- ## 触发词 `桌面控制` / `操控电脑` / `截屏` / `desktop` --- ## 🖥️ macOS (Peekaboo) ### 安装 ```bash npx -y @steipete/peekaboo # 授权 peekaboo permissions grant ``` ### 命令 ```bash # 截图 peekaboo image --mode screen # 点击 peekaboo click --on "按钮文字" # 输入 peekaboo type --text "内容" # 快捷键 peekaboo hotkey cmd,c ``` --- ## 🐧 Linux (桌面 MCP) ### 方案1: Ubuntu Desktop Control MCP ```bash # 安装 pip install mcp-desktop-control # 运行 mcp-desktop-control ``` ### 方案2: mcp-desktop-pro (跨平台) ```bash # 安装运行 npx -y mcp-desktop-pro serve # 截图 # mcp-desktop-pro screenshot # 点击 # mcp-desktop-pro click --x 100 --y 200 ``` ### 方案3: kwin-mcp (KDE Wayland) ```bash git clone https://github.com/bhyoo/kwin-mcp cd kwin-mcp python kwin-mcp.py ``` --- ## 💻 WSL2 方案 ### 1. 安装 X server (Windows) 下载安装 [VcXsrv](https://github.com/ArcticaProject/vcxsrv) ### 2. WSL2 配置 ```bash # .bashrc 添加 export DISPLAY=localhost:0 # 安装依赖 sudo apt install python3-xlib flameshot scrot ``` ### 3. Python 自动化 ```python from pymouse import PyMouse from pykeyboard import PyKeyboard m = PyMouse() k = PyKeyboard() # 点击 m.click(100, 200) # 输入 k.type_string("Hello") # 快捷键 k.press_key('ctrl') k.tap_key('c') k.release_key('ctrl') ``` --- ## 使用示例 ### macOS ``` 你说：打开Safari去openclaw官网 我 → peekaboo app launch Safari → peekaboo type --text "openclaw.ai" → peekaboo press return ``` ### Linux ``` 你说：截个图 我 → mcp-desktop-control screenshot ``` ### WSL2 ``` 你说：点击这个按钮 我 → 用坐标点击 ``` --- ## 注意事项 - ⚠️ macOS 需要 15+，授权辅助功能 - ⚠️ Linux 需要 X server 或 Wayland - ⚠️ WSL2 需要 VcXsrv --- *桌面控制 | AI 操控电脑*


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