OpenClaw/clawhub-security-signals huggingface dataset:
Skill: chenfa188/openclaw-feishu-fetch-doc (version 1.0.0)

--- name: feishu-fetch-doc description: | 获取飞书云文档内容。返回文档的 Markdown 内容，支持处理文档中的图片、文件和画板（需配合 feishu_doc_media 工具）。 --- # feishu_mcp_fetch_doc 获取飞书云文档的 Markdown 内容（Lark-flavored 格式）。 ## 重要：图片、文件、画板的处理 **文档中的图片、文件、画板需要通过 `feishu_doc_media`（action: download）工具单独获取！** ### 识别格式 返回的 Markdown 中，媒体文件以 HTML 标签形式出现： - **图片**： ```html <image [REDACTED_SECRET]" width="1833" height="2491" align="center"/> ``` - **文件**： ```html <view type="1"> <file [REDACTED_SECRET]" name="skills.zip"/> </view> ``` - **画板**： ```html <whiteboard [REDACTED_SECRET]"/> ``` ### 获取步骤 1. 从 HTML 标签中提取 `token` 属性值 2. 调用 `feishu_doc_media` 下载： ```json { "action": "download", "resource_token": "提取的token", "resource_type": "media", "output_path": "/path/to/save/file" } ``` ## 参数 - **`doc_id`**（必填）：支持直接传文档 URL 或 token - 直接传 URL：`https://xxx.feishu.cn/docx/Z1FjxxxxxxxxxxxxxxxxxxxtnAc`（系统自动提取 token） - 直接传 token：`Z1FjxxxxxxxxxxxxxxxxxxxtnAc` - 知识库 URL/token 也支持：`https://xxx.feishu.cn/wiki/Z1FjxxxxxxxxxxxxxxxxxxxtnAc` 或 `Z1FjxxxxxxxxxxxxxxxxxxxtnAc` ## Wiki URL 处理策略 知识库链接（`/wiki/TOKEN`）背后可能是云文档、电子表格、多维表格等不同类型的文档。当不确定类型时, **不能直接假设是云文档**，必须先查询实际类型。 ### 处理流程 1. **先调用 `feishu_wiki_space_node`（action: get）解析 wiki token**： ```json { "action": "get", "token": "wiki_token_here" } ``` 2. **从返回的 `node` 中获取 `obj_type`（实际文档类型）和 `obj_token`（实际文档 token）** 3. **根据 `obj_type` 调用对应工具**： | obj_type | 工具 | 传参 | |----------|------|------| | `docx` | `feishu_mcp_fetch_doc` | doc_id = obj_token | | `sheet` | `feishu_sheet` | spreadsheet_token = obj_token | | `bitable` | `feishu_bitable_*` 系列 | app_token = obj_token | | 其他 | 告知用户暂不支持该类型 | — | ### 示例 用户：`帮我看下这个文档 https://xxx.feishu.cn/wiki/ABC123` 1. 调用 `feishu_wiki_space_node`（action: get, [REDACTED_SECRET] 2. 返回 `obj_type: "docx"`, `obj_token: "doxcnXYZ789"` 3. 调用 `feishu_mcp_fetch_doc`（doc_id: doxcnXYZ789） ## 工具组合 | 需求 | 工具 | |------|------| | 获取文档文本 | `feishu_mcp_fetch_doc` | | 下载图片/文件/画板 | `feishu_doc_media`（action: download） | | 解析 wiki token 类型 | `feishu_wiki_space_node`（action: get） | | 读写电子表格 | `feishu_sheet` | | 操作多维表格 | `feishu_bitable_*` 系列 |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ghuron/magister-1 (version 1.0.5)

--- name: magister description: Fetch schedule, grades, and infractions from https://magister.net 🇳🇱 portal metadata: {"clawdbot":{"emoji":"🇲","requires":{"bins":["node"],"env":["MAGISTER_HOST","MAGISTER_USER","MAGISTER_PASSWORD"]}}} --- # Commands ```bash node magister.mjs students # list students (works for parent and child credentials) node magister.mjs schedule <id> <from> <to> # schedule, YYYY-MM-DD dates node magister.mjs grades <aanmelding_id> [top] # grades (default top=50) node magister.mjs infractions <id> <from> <to> # absences ``` # Flow Run `students` first to get each student's `id` and `aanmelding_id`. Use `id` for schedule and infractions, `aanmelding_id` for grades.


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: codenova58/flash-sale-hunting (version 0.1.0)

--- name: "Flash Sale" description: "提供各大电商整点秒杀、抢购助手及预售玩法解析。" --- # 抢购速递 ## 筛选项 - 类别/领域/功能（核心/进阶） - 场景/适用时段/设备要求（移动端/网页版/跨端） - 状态（实时/离线/授权/VIP特权/免费） ## 返回字段 - 操作摘要/交互流程/关键入口 - 功能亮点/提效工具/隐藏玩法/避坑指南 - 状态追踪/数据同步/消息提醒/安全策略 ## 示例请求 - “如何快速使用 抢购速递 处理 [具体场景任务] ？” - “抢购速递 的 [核心功能] 有哪些最新玩法/优惠信息？” ## 更新频率 - 结合产品迭代与社区反馈进行内容更新 ## 注意事项 - 保护个人隐私，谨慎进行敏感授权 - 部分功能涉及第三方接口，稳定性受合作方波动影响


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yezaijun/ielts-grading (version 1.0.0)

--- name: ielts-grading description: 根据用户提供的雅思写作或口语回答进行官方标准评分。使用前先联网检索并确认原始题目，再按本项目内评分标准逐维度打分、给出总分与可执行改进建议。适用于用户提到 IELTS/雅思、作文批改、口语评分、band 分数、Task 1/Task 2、speaking mock 等场景。 --- # IELTS Grading ## 目标 基于用户给出的回答文本（作文或口语转写）执行可追溯评分： 1. 先完成必要信息收集（写作需先确认题目）； 2. 按需联网核验题目版本； 3. 使用项目中的评分标准逐维度打分； 4. 给出总分、证据、问题清单和提分建议。 ## 评分依据文件（必须优先使用） - 写作：`写作评分标准.md` - 口语：`口语评分标准.md` 若本地评分文件与网络资料冲突，优先使用本地文件，并在结果里注明冲突点。 ## 标准流程（必须按顺序） ### 1) 题目信息收集（按任务类型区分） - 若为**写作任务**：必须先询问并确认“具体题目”后再评分； - 若用户未提供完整写作题干，先追问并等待题干； - 若写作只给出“关键词/记忆版题目”，标记为“待核对题目”并进入下一步联网核验； - 写作未确认题目前，不进入正式打分； - 若为**口语任务**：不强制询问题目，可直接按回答内容评分（用户若提供话题/Part 信息则用于提高置信度）。 写作场景固定追问模板（优先使用）： - `请提供这次 Writing Task 的完整题干（可附题库编号），以及你的作文全文。` - 若用户只给题库编号：`请补充题干原文或你记得的题干关键词（主题、对象、年份、图表类型/观点题类型）。` ### 1.5) 快速预检（正式评分前） - 检查文本是否为英文、是否明显缺段或过短； - 写作若明显少于最低有效长度（如仅几十词），先提示“可评分但上限受限”； - 口语若仅有转写无音频，先声明发音项为低置信度评估。 ### 2) 识别任务类型 - 出现 `Task 1` / 图表描述 / 信件 / 议论文，判为写作； - 出现问答转写 / Part 1-3 / 面试对话，判为口语； - 无法判断时，先向用户确认是写作还是口语，再继续。 ### 3) 联网检索题目（写作必做，口语可选） - 从用户消息中提取关键词（主题词、题干片段、年份、考试场次等）； - 使用网络检索工具寻找题目原文； - 至少交叉 2 个来源（优先官方或高可信备考站点）； - 输出中必须给出： - 你采用的“题目最终版本”； - 2 个以上来源链接； - 若存在多个版本，说明差异并声明本次采用版本。 - 若仅“题库编号”与“题干文本”冲突： - 优先以**题干文本和数据点**作为评分依据； - 将编号差异标注为“元信息冲突”，不阻塞评分。 若无法找到可信题目原文： - 明确告知“题目未能可靠定位”； - 对写作：继续做“基于用户给定版本的近似评分”，并降低 Task/Response 维度置信度； - 对口语：可继续正常评分，仅在需要题目语境时说明不确定性来源。 ### 4) 按维度打分 #### 写作（四项等权） - Task Achievement（T1）/ Task Response（T2） - Coherence and Cohesion - Lexical Resource - Grammatical Range and Accuracy 执行规则： - 每项先给“证据摘录”，再给分数（允许 0.5 分）； - 每个维度至少给 1 条具体文本证据（短语或句子片段）； - 写作单题分数 = 四项平均； - 若用户同时给了 T1 和 T2，按 T2 权重更高给综合写作分（常用 1:2）。 #### 口语（四项等权） - Fluency and Coherence - Lexical Resource - Grammatical Range and Accuracy - Pronunciation（基于转写时，须声明该项置信度有限） 执行规则： - 每项先给“语言证据（原句/片段）”，再给分数（允许 0.5 分）； - 每个维度至少给 1 条证据；若证据不足，明确标注“该维度低置信度”； - 单科分数 = 四项平均。 ### 5) 结果校准 - 检查四项分与总分是否一致； - 检查评语是否与 band 特征一致（避免“描述像 7 分但打了 5 分”）； - 给出置信度：高 / 中 / 低，并说明原因（题目匹配度、文本长度、是否有音频等）。 - 若存在编号冲突、题干不完整、证据不足，在结果中增加“限制说明”。 ## 输出模板（默认中文） 按以下结构输出，避免长篇空话： ```markdown ## 题目确认 - 采用题目版本：... - 来源：... - 版本差异说明：... ## 评分结果 - 任务类型：Writing Task 1 / Writing Task 2 / Speaking - 分项分数： - [维度A]：x.x（证据：...） - [维度B]：x.x（证据：...） - [维度C]：x.x（证据：...） - [维度D]：x.x（证据：...） - 总分（平均/加权后）：x.x - 置信度：高/中/低（原因：...） ## 主要失分点（最多 5 条） - ... ## 提分建议（按提分效率排序） 1. ... 2. ... 3. ... ## 可直接改写示例（可选） - 原句：... - 建议：... - 说明：... ``` ## 评分边界与异常处理 - 少于约 20 词的写作文本，参考本地标准按低分处理； - 明显背诵模板、严重离题、非英语内容，需单独标注并下调相关维度； - 口语仅有文字转写且无音频时，不对发音项做“高置信度”断言； - 用户要求“严格模式”时，优先从任务回应完整性与语法准确性扣分。 - 若用户只要“快速评分”，可输出精简版（仅分项分+总分+3条建议），但仍需保留题目确认结论。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: austindixson/playwright-commander (version 1.0.0)

--- name: playwright-commander displayName: Playwright Commander description: A skill to interact with web browsers using Playwright for advanced UI automation, analysis, and debugging. version: 0.1.0 --- # Playwright Commander Skill ## Description This skill provides agents with a powerful interface to programmatically control web browsers using Playwright. It enables advanced UI automation, web content analysis, and debugging capabilities beyond what the default `browser` tool might offer directly. ## Core Functionalities - Launch and close browser instances. - Navigate to specified URLs. - Capture screenshots of web pages. - Retrieve HTML content or readable text from pages. - Execute custom JavaScript within the browser context. - Interact with web elements (click, type, fill forms) using various selectors. - Retrieve element attributes or text content. ## Usage This skill is designed for programmatic interaction via `exec` commands, typically orchestrated by an agent. ```bash # Example: Launch browser and navigate python3 scripts/playwright_cli.py launch_browser --browser chrome --headless true # Example: Navigate to URL and take screenshot python3 scripts/playwright_cli.py navigate --url "https://example.com" --screenshot "path/to/screenshot.png" # Example: Get page content python3 scripts/playwright_cli.py get_content --url "https://example.com" --type html # Example: Click an element python3 scripts/playwright_cli.py click_element --url "https://example.com" --selector "button.submit" ``` ## Prerequisites - Playwright Python package (`playwright`) installed in the skill's virtual environment. - Playwright browser binaries installed (`playwright install`). ## Configuration (`config.json`) (To be defined if specific configuration beyond default Playwright options is needed.) ## Purpose To give me more granular and robust control over web browser interactions, enabling more complex UI automation, detailed web page analysis, and better troubleshooting capabilities for browser-related issues, especially for the Mac App conversion project.

path: venv/lib/python3.14/site-packages/greenlet-3.3.1.dist-info/top_level.txt
content: greenlet


path: venv/lib/python3.14/site-packages/pip/_internal/cli/__init__.py
content: """Subpackage containing all of pip's command line interface related code"""

# This file intentionally does not import submodules


path: venv/lib/python3.14/site-packages/pip/_internal/index/__init__.py
content: """Index interaction code"""


path: venv/lib/python3.14/site-packages/playwright-1.58.0.dist-info/top_level.txt
content: playwright


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: timyljob2011-sudo/skill-creater (version 1.0.0)

--- name: skill-creater description: Generate complete AgentSkills from user requirements. Creates SKILL.md, scripts, references, assets folders, and packages them into a ready-to-upload archive for Clawhub. Use when the user wants to create a new skill, build a custom skill, or package an existing skill for distribution. --- # Skill Craftsmen A meta-skill that generates other skills. Transform your ideas into production-ready AgentSkills with proper structure, documentation, and packaging. ## What This Skill Does 1. **Interviews you** to understand what skill you want to build 2. **Generates complete skill structure** including SKILL.md, scripts, references 3. **Packages everything** into a ready-to-upload `.zip` archive 4. **Provides Clawhub upload guidance** ## Quick Start ``` User: "帮我创建一个处理PDF的Skill" → This skill interviews → Generates → Packages → Delivers zip file ``` ## Usage Flow ### Step 1: Intent Capture (自动) Skill会自动询问关键问题： - 这个skill要解决什么问题？ - 用户会怎么描述需求来触发它？ - 需要哪些功能？简单的还是复杂的？ - 需要脚本/工具吗？还是纯文本指导？ ### Step 2: Structure Generation 根据需求生成： ``` your-skill/ ├── SKILL.md # 主文档 (name, description, instructions) ├── manifest.json # Clawhub元数据 ├── scripts/ # 可选: 可执行脚本 │ └── (Python/Node.js/Bash) ├── references/ # 可选: 参考文档、示例 │ └── (schemas, examples, guides) ├── assets/ # 可选: 模板、图标等 │ └── (templates, icons, images) └── README.md # 安装说明 (可选) ``` ### Step 3: Package & Deliver 自动打包为 `your-skill-v1.0.0.zip`，包含： - 完整目录结构 - 验证过的manifest.json - 可直接上传Clawhub ## Skill Complexity Levels | 级别 | 特点 | 包含内容 | |------|------|---------| | **Simple** | 纯文本指导 | SKILL.md only | | **Standard** | 带辅助脚本 | SKILL.md + scripts/ | | **Advanced** | 完整工具集 | SKILL.md + scripts/ + references/ + assets/ | ## Best Practices Applied This skill automatically ensures: - ✅ YAML frontmatter format compliance - ✅ Description includes "when to use" triggers - ✅ Proper directory structure - ✅ Concise SKILL.md (<500 lines) - ✅ Progressive disclosure design ## Output Example ``` 📦 Generated: pdf-master-v1.0.0.zip 📁 Contents: ├── SKILL.md ✓ Valid YAML frontmatter ├── manifest.json ✓ Clawhub ready ├── scripts/ │ ├── merge.py │ ├── split.py │ └── rotate.py ├── references/ │ ├── examples.md │ └── api-guide.md └── README.md 🚀 Next Steps: 1. Extract the zip 2. Test locally: openclaw skill install ./pdf-master 3. Upload to Clawhub: https://clawhub.com/upload ``` ## Clawhub Upload Checklist Generated skills include: - [x] Valid manifest.json with version, author, tags - [x] SKILL.md with proper YAML frontmatter - [x] Scripts are executable (if included) - [x] No unnecessary files (README optional) - [x] Compressed with proper structure --- *Craft your skills. Share with the world.*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: feiyang2007/12306-old (version 1.0.2)

# 12306 > 自动生成的技能文档 - 2026-03-08 ## 描述 12306 技能包 - general 类工具 ## 功能 - 12306_client.py- scripts/login.py ## 使用方法 ```python # 示例用法 # 待补充 ``` ## 配置 查看 `.env.example` 文件了解所需环境变量。 ## 脚本 - 12306_client.py- scripts/login.py ## 测试 ```bash # 运行测试 # 待补充 ``` ## 备注 此文档由脚本自动生成，请根据实际情况补充完善。 --- *最后更新：2026-03-08T20:15:05.855845


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: gizmo-dev/qa-check (version 1.0.0)

--- name: qa-check description: Mandatory quality assurance for all dev work before publishing. Use BEFORE deploying any project to production. Validates build, tests browser functionality, checks mobile responsiveness, and ensures no broken links/images. author: gizmolab version: 1.0.0 tags: [qa, quality, testing, validation, deployment] --- # QA Check Mandatory pre-deployment quality assurance. Run this before ANY project goes live. ## When to Use - Before `vercel --prod` or any production deploy - Before announcing/sharing any project URL - Before publishing skills to ClawHub - After major code changes ## QA Checklist ### 1. Build Validation ```bash # Ensure build succeeds without errors cd <project-dir> npm run build ``` **Fail criteria:** Build errors, warnings about missing dependencies ### 2. Browser Functional Test Use browser tool to verify: - [ ] Page loads without console errors - [ ] All interactive elements work (buttons, links, forms) - [ ] No broken images (check Network tab for 404s) - [ ] No JavaScript errors in console ``` browser snapshot → check for errors browser console → verify no red errors ``` ### 3. Mobile Responsiveness ``` browser screenshot --mobile ``` Check: - [ ] Content readable on mobile viewport - [ ] No horizontal scroll - [ ] Buttons/links tappable (not too small) - [ ] Navigation works ### 4. Link Validation ```bash # Check all external links resolve grep -r "href=" src/ | grep -o 'https://[^"]*' | sort -u | while read url; do curl -s -o /dev/null -w "%{http_code} $url\n" "$url" done ``` ### 5. Performance Quick Check - Page loads in < 3 seconds - No massive bundle warnings (> 500KB) - Images optimized (not raw screenshots) ### 6. SEO/Meta Basics Verify in index.html: - [ ] `<title>` set (not generic) - [ ] `<meta name="description">` present - [ ] `<meta property="og:*">` for social sharing - [ ] Favicon present ## Pre-Deploy Command ```bash # Run full QA suite scripts/qa-check.sh <project-dir> ``` ## Fail = No Deploy If ANY check fails: 1. Document the issue 2. Fix it 3. Re-run QA 4. Only then deploy ## Quick Reference | Check | Tool | Pass Criteria | |-------|------|---------------| | Build | `npm run build` | Exit 0, no errors | | Browser | `browser snapshot` | No console errors | | Mobile | `browser screenshot` | Readable, no scroll | | Links | `curl` | All 200/301 | | Speed | Browser | < 3s load | | SEO | Read index.html | All meta present | ## Post-Deploy Verification After deploy, ALWAYS: 1. Visit production URL in browser 2. Click through main features 3. Check mobile view 4. Verify analytics loading (if added) Only THEN announce/share the project.


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: athola/nm-tome-discourse (version 1.0.2)

--- name: discourse description: | >- Scan community discussion channels (HN, Lobsters, Reddit, tech blogs) for experience reports and opinions on a topic version: 1.9.5 triggers: - hackernews - reddit - lobsters - blogs - discourse - the user wants community perspectives on a technology or approach metadata: {"openclaw": {"homepage": "https://github.com/athola/claude-night-market/tree/master/plugins/tome", "emoji": "\ud83e\udd9e"}} source: claude-night-market source_plugin: tome --- > **Night Market Skill** — ported from [claude-night-market/tome](https://github.com/athola/claude-night-market/tree/master/plugins/tome). For the full experience with agents, hooks, and commands, install the Claude Code plugin. # Discourse Search ## When To Use - Gathering community opinions on a technology or approach - Finding experience reports from HN, Reddit, or Lobsters ## When NOT To Use - Academic research (use `/tome:papers`) - Code examples (use `/tome:code-search`) Scan community channels for discussions on a topic. ## Channels - **Hacker News**: Algolia API at hn.algolia.com - **Lobsters**: WebSearch with site:lobste.rs - **Reddit**: JSON API (append .json to URLs) - **Tech blogs**: WebSearch targeting curated domains ## Workflow 1. Build search URLs/queries per channel using `tome.channels.discourse.*` functions 2. Execute via WebFetch (APIs) or WebSearch (fallback) 3. Parse responses into Finding objects 4. Merge across sources with source attribution


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jixsonwang/wechat-search-release (version 1.0.3)

--- name: wechat-search description: Search WeChat Official Account articles using OpenClaw's web search and fetch capabilities with compliance-focused design. --- # WeChat Search Skill Search for WeChat Official Account (微信公众号) articles using a compliant, two-layer approach that prioritizes legal search APIs and falls back to respectful web scraping when needed. ## Features - **Compliant Design**: Prioritizes legal search APIs, respects robots.txt and rate limits - **Two-Layer Strategy**: - Primary: Web search with `site:mp.weixin.qq.com` filter - Fallback: Direct page fetching with proper delays and headers - **Recent Results**: Returns the 5 most recent articles by default (configurable) - **Time Filtering**: Support for date range and recency filters - **Multiple Output Formats**: Text, JSON, and markdown formats available ## Prerequisites - **OpenClaw Web Tools**: Requires `web_search` and `web_fetch` tools to be available - **Tavily API Key** (optional): For enhanced search capabilities via Tavily integration ## Usage ### Basic Search ```bash wechat-search "人工智能" ``` ### Advanced Options ```bash # Return 10 results instead of default 5 wechat-search "机器学习" --max-results 10 # Search within past week wechat-search "大模型" --past-week # Custom date range wechat-search "AI应用" --from 2026-01-01 --to 2026-02-01 # JSON output format wechat-search "开源AI" --output json # Force web fetch strategy wechat-search "最新技术" --strategy web_fetch ``` ## Configuration Create `~/.openclaw/wechat-search-config.json` to customize behavior: ```json { "defaultMaxResults": 5, "maxResultsLimit": 20, "requestDelayMs": 5000, "cacheDurationHours": 1, "userAgent": "OpenClaw-WeChat-Search-Bot/1.0 (+https://github.com/your-username/wechat-search-skill)" } ``` ## Compliance & Ethics - **Respects robots.txt**: Checks and follows robots.txt directives - **Rate limiting**: Minimum 5-second delay between requests - **Transparent identification**: Clear User-Agent string identifying the bot - **Public content only**: Only accesses publicly available articles - **No data retention**: Does not store full article content, only metadata ## Error Handling - Automatic retry on network failures (up to 3 attempts) - Graceful fallback between search strategies - Clear error messages for debugging ## Future Enhancements - RSS feed integration support - Article content summarization - Author/subscription management - Enhanced filtering options This skill is designed to be both useful and responsible, providing access to valuable WeChat Official Account content while respecting platform rules and legal requirements.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: wang-junjian/port-process (version 1.0.0)

--- name: port-process description: 通过端口查找和管理系统进程。支持查找占用端口的进程、杀掉占用端口的进程、查看端口使用情况等操作。适用于 macOS 和 Linux 系统。使用场景：(1) "谁占用了 8080 端口"，(2) "杀掉占用 3000 端口的进程"，(3) "查看当前端口使用情况"。 --- # Port Process - 端口进程管理 ## 概述 通过端口查找和管理系统进程的工具集。支持跨平台（macOS/Linux）操作。 ## 快速开始 ### 查找占用端口的进程 ```bash # 方法一：使用 lsof（macOS/Linux） lsof -ti :8080 # 方法二：使用 netstat（Linux） netstat -tulpn | grep :8080 # 方法三：使用 ss（现代 Linux） ss -tulpn | grep :8080 ``` ### 杀掉占用端口的进程 ```bash # 一键杀掉占用 8080 端口的进程 lsof -ti :8080 | xargs kill -9 # 或者使用提供的脚本 python3 scripts/kill_by_port.py 8080 ``` ## 任务分类 ### 1. 查找端口占用 **查找单个端口：** ```bash lsof -i :8080 ``` **查找多个端口：** ```bash lsof -i :8080 -i :3000 ``` **显示进程详细信息：** ```bash lsof -ti :8080 | xargs ps -fp ``` ### 2. 终止端口进程 **安全终止（先尝试 SIGTERM）：** ```bash lsof -ti :8080 | xargs kill -15 ``` **强制终止（SIGKILL）：** ```bash lsof -ti :8080 | xargs kill -9 ``` **使用脚本安全终止：** ```bash python3 scripts/kill_by_port.py 8080 --safe ``` ### 3. 查看端口使用情况 **查看所有监听端口：** ```bash # macOS lsof -i -P | grep LISTEN # Linux netstat -tulpn ss -tulpn ``` **查看特定用户的端口占用：** ```bash lsof -i -u username ``` ## 脚本使用 ### scripts/kill_by_port.py 通过端口终止进程的安全脚本。 **用法：** ```bash # 强制终止 python3 scripts/kill_by_port.py 8080 # 安全终止（先 SIGTERM，等待后再 SIGKILL） python3 scripts/kill_by_port.py 8080 --safe # 仅查看，不终止 python3 scripts/kill_by_port.py 8080 --dry-run ``` ### scripts/find_port.py 查找占用端口的进程信息。 **用法：** ```bash # 查找单个端口 python3 scripts/find_port.py 8080 # 查找多个端口 python3 scripts/find_port.py 8080 3000 5432 # 输出 JSON 格式 python3 scripts/find_port.py 8080 --json ``` ### scripts/list_ports.py 列出所有正在使用的端口。 **用法：** ```bash # 列出所有监听端口 python3 scripts/list_ports.py # 仅显示 TCP 端口 python3 scripts/list_ports.py --tcp # 仅显示 UDP 端口 python3 scripts/list_ports.py --udp # 显示详细信息 python3 scripts/list_ports.py --verbose ``` ## 跨平台兼容性 ### macOS - 使用 `lsof` 作为主要工具 - 支持 `netstat` 作为备选 ### Linux - 使用 `ss` 作为首选（现代系统） - 使用 `netstat` 作为备选 - 支持 `lsof` ### Windows (WSL) - 在 WSL 中使用 Linux 工具 - 如需管理 Windows 进程，使用 PowerShell ## 常见用例 ### 开发场景 ```bash # 杀掉占用前端开发服务器端口的进程 python3 scripts/kill_by_port.py 3000 # 杀掉占用后端 API 端口的进程 python3 scripts/kill_by_port.py 8000 # 杀掉占用数据库端口的进程 python3 scripts/kill_by_port.py 5432 ``` ### 调试场景 ```bash # 查看是谁占用了端口 python3 scripts/find_port.py 8080 # 查看所有端口使用情况 python3 scripts/list_ports.py --verbose ``` ### 自动化脚本 ```bash # 在启动服务前清理端口 python3 scripts/kill_by_port.py 8080 --dry-run || true python3 scripts/kill_by_port.py 8080 ./start-my-server.sh ``` ## 安全提示 1. **先查看，后终止**：使用 `--dry-run` 或先运行 `find_port.py` 确认要终止的进程 2. **优先安全终止**：使用 `--safe` 选项给进程优雅退出的机会 3. **避免误杀**：仔细检查进程信息，特别是通用端口（如 22、80、443） 4. **备份重要数据**：在终止可能有状态的进程前，确保数据已保存 ## 故障排除 ### "lsof: command not found" - **macOS**：通常预装了 lsof，如缺失可安装 Xcode Command Line Tools - **Linux**：`sudo apt install lsof` (Debian/Ubuntu) 或 `sudo yum install lsof` (RHEL/CentOS) ### "端口没有被占用"但仍然无法绑定 - 检查 TIME_WAIT 状态：`netstat -an | grep TIME_WAIT` - 等待一段时间或调整系统参数 ### 权限不足 - 使用 `sudo` 运行以查看其他用户的进程 - 注意：使用 `sudo kill` 时要格外小心


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: web4agent/htlc (version 1.0.0)

--- name: htlc-trade description: HTLC (Hash Time Locked Contract) trading for inscriptions and NFTs on EVM chains. Use when: (1) Buying or selling inscriptions via atomic swap, (2) Trading NFTs peer-to-peer without intermediaries, (3) Setting up trustless escrow for any digital asset, (4) Implementing commit-reveal trading patterns. Supports Base, Ethereum, Arbitrum and other EVM chains. --- # HTLC Trade Atomic swap trading using Hash Time Locked Contracts. ## Quick Start ```bash # Install dependencies npm install ethers viem # Set environment export PRIVATE_KEY=0x... export BASE_ETH_RPC=https://mainnet.base.org ``` ## Commands ### Generate Preimage ```bash node scripts/htlc.js preimage ``` ### Lock Funds (Buyer) ```bash node scripts/htlc.js lock <seller> <preimageHash> <timeout> <amountETH> ``` ### Reveal (Seller) ```bash node scripts/htlc.js reveal <lockHash> <preimage> ``` ### Full Trade ```bash node scripts/htlc.js trade <seller> <inscriptionTx> <amountETH> ``` ## Contract BaseTimelock: [REDACTED_SECRET] ## Flow 1. Seller generates preimage, shares hash + inscription 2. Buyer verifies inscription, calls lock() with ETH 3. Seller calls reveal() with preimage → ETH released 4. Buyer confirms receipt → trade complete


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