OpenClaw/clawhub-security-signals huggingface dataset:
Skill: rainbat/notectl (version 1.0.0)

--- name: notectl description: Manage Apple Notes via AppleScript CLI --- # notectl - Apple Notes CLI Manage Apple Notes from the command line using AppleScript. ## Commands | Command | Description | |---------|-------------| | `notectl folders` | List all folders with note counts | | `notectl list [folder]` | List notes in a folder (default: Notes) | | `notectl show <title>` | Show note content by title | | `notectl add <title>` | Create a new note | | `notectl search <query>` | Search notes by title or content | | `notectl append <title>` | Append text to an existing note | ## Examples ```bash # List all folders notectl folders # List notes in default folder notectl list # List notes in specific folder notectl list "rainbat-projects" notectl list Papi # Show a note notectl show "Meeting Notes" # Create a note notectl add "New Idea" notectl add "Project Plan" --folder research --body "Initial thoughts..." # Search all notes notectl search "clawdbot" notectl search "API" # Append to a note (daily log style) notectl append "Daily Log" --text "- Completed feature X" ``` ## Options for `add` | Option | Description | Default | |--------|-------------|---------| | `-f, --folder <name>` | Folder to create note in | Notes | | `-b, --body <text>` | Note body content | empty | ## Options for `append` | Option | Description | |--------|-------------| | `-t, --text <text>` | Text to append to the note | ## Available Folders Folders on this system: - Notes (default) - research - rainbat-projects - Papi - renova-roll - Journal - CheatSheets - pet-projects


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bosshuman/openclaw-x (version 0.2.2)

--- name: openclaw-x version: 0.1.0 description: Control your X/Twitter account — view timeline, search tweets, post, like, retweet, bookmark. --- # OpenClaw X Control your X/Twitter account via a local API. ## Prerequisites 1. Download the executable from [GitHub Release](https://github.com/bosshuman/openclaw-x/releases) 2. Export your X cookies from Chrome (using Cookie-Editor extension), save as `cookies.json` in the same directory 3. Run the executable, ensure the service is running at `http://localhost:19816` ## Available Actions ### 1. Get Home Timeline ```bash curl http://localhost:19816/timeline?count=20 ``` Returns the latest tweets including content, author, media URLs, etc. ### 2. Get Tweet Details ```bash curl http://localhost:19816/tweet/{tweet_id} ``` Supports both tweet ID and full URL (e.g. `https://x.com/user/status/123456`). ### 3. Search Tweets ```bash curl "http://localhost:19816/search?q=keyword&sort=Latest&count=20" ``` Parameters: - `q`: Search keyword (required) - `sort`: `Latest` or `Top`, default Latest - `count`: Number of results, default 20 ### 4. Post a Tweet ```bash curl -X POST http://localhost:19816/tweet \ -H "Content-Type: application/json" \ -d '{"text": "Hello world"}' ``` Reply to a tweet: ```bash curl -X POST http://localhost:19816/tweet \ -H "Content-Type: application/json" \ -d '{"text": "Reply content", "reply_to": "original_tweet_id"}' ``` ### 5. Like a Tweet ```bash curl -X POST http://localhost:19816/tweet/{tweet_id}/like ``` ### 6. Retweet ```bash curl -X POST http://localhost:19816/tweet/{tweet_id}/retweet ``` ### 7. Bookmark a Tweet ```bash curl -X POST http://localhost:19816/tweet/{tweet_id}/bookmark ``` ### 8. Get User Info ```bash curl http://localhost:19816/user/{username} ``` Returns username, avatar, bio, follower count, etc. ### 9. Get User Tweets ```bash curl http://localhost:19816/user/{username}/tweets?count=20 ``` ## Common Use Cases 1. "Show me what's new on my timeline" 2. "Search for the latest tweets about AI Agents" 3. "Post a tweet saying: What a beautiful day!" 4. "Like this tweet https://x.com/xxx/status/123" 5. "Check what @elonmusk has been posting" 6. "Bookmark this tweet"


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: wangzhiming1999/modified-code-review (version 1.0.1)

--- name: modified-code-review description: Reviews user-modified code (diffs/PRs), provides best-practice recommendations, analyzes cost-effectiveness of the approach, and outputs a code score. Use when the user asks to review their changes, 审查/评审修改的代码, 代码评审, or wants 最佳实践/性价比/代码评分. --- # 修改代码评审（Modified Code Review） 对用户修改的代码进行评审，给出最佳实践建议、修改方案性价比分析，并输出代码评分。 ## 触发场景 - 用户要求「review 我修改的代码」「审查修改」「代码评审」 - 用户需要「最佳实践方案」「修改方案性价比」「代码评分」 - 用户提供 git diff、PR 或指定文件/改动范围 ## 执行流程 ### 1. 获取改动范围 - 若用户未指定：先看 `git status` / `git diff` 或当前打开/选中的文件 - 明确本次评审的文件与改动内容（新增、修改、删除） ### 2. 评审维度 按以下维度分析修改，并给出结论与建议： | 维度 | 关注点 | |------|------| | **正确性** | 逻辑是否正确、边界与异常是否考虑、是否有明显 bug | | **可读性** | 命名、结构、注释是否清晰，是否符合项目风格 | | **可维护性** | 是否易扩展、是否过度耦合、重复代码 | | **性能** | 是否有不必要的计算、内存/请求是否合理 | | **安全** | 输入校验、敏感数据、XSS/注入等 | | **测试** | 关键路径是否有测试、用例是否合理 | ### 3. 最佳实践建议 - 每条建议标明：**必须改** / **建议改** / **可选优化** - 若与项目约定（如 CLAUDE.md、ESLint、现有模式）冲突，以项目约定为准并说明 - 建议附带简短理由和（如适用）示例代码片段 ### 4. 修改方案性价比 对主要修改方案做「性价比」评估： - **收益**：可维护性、性能、可读性、风险降低等 - **成本**：改动量、影响范围、回归风险、时间预估 - **结论**：高性价比（建议优先）/ 中性价比（可选）/ 低性价比（暂不推荐），并简述原因 ### 5. 代码评分 **先列 Blockers，再给评分**——有 Blocker 时评分仅供参考，不能用分数掩盖必须修的问题。 | 分数区间 | 等级 | 含义 | |----------|------|------| | 90–100 | A 优秀 | 可直接合并，仅有个别小建议 | | 75–89 | B 良好 | 整体达标，有若干改进点 | | 60–74 | C 合格 | 能用但建议按优先级修复/优化 | | 40–59 | D 需改进 | 存在明显问题，建议修改后再合 | | 0–39 | E 不达标 | 存在严重问题，需重构或重做 | 评分需结合：正确性权重最高，其次可维护性/可读性，再考虑性能与安全。 ## 输出模板 按以下结构组织回复（可用中文）： ```markdown ## 修改代码评审报告 ### 一、改动概览 - 涉及文件与变更类型 - 主要改动摘要（1–3 句） ### 二、评审结论（按维度） - 正确性：… - 可读性：… - 可维护性：… - 性能：… - 安全：… - 测试：… ### 三、最佳实践建议 - [必须改] … - [建议改] … - [可选优化] … ### 四、修改方案性价比 - 方案/建议 1：收益 vs 成本 → 性价比结论 - （其他主要方案按需） ### 五、代码评分 - **Blockers（必须改，不改不能合）**：…（无则写"无"） - **分数**：XX / 100（等级：X） - **一句话总结**：… ``` ## 注意事项 - 评审基于用户提供的 diff/文件/上下文，不臆测未展示的代码 - 与项目技术栈、既有风格一致（如 Next.js、TypeScript、Zustand、Tailwind 等） - 性价比与评分需简要说明依据，便于用户决策


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yewubin-jpg/yewubin (version 0.1.0)

--- name: yewubin-perspective description: | 【叶武滨】的专家级认知操作系统。基于易效能官方权威资料提炼， 包含高能要事、RPA系统、八大关注等核心心智模型，以及最具感染力的对话体表达DNA。 用途：作为时间管理与效能提升顾问，用【叶武滨】最优一面的视角分析相关领域问题。 当用户提到「时间管理」「高能要事」「叶老师怎么看」时使用。 --- # 【叶武滨】 · 专家级认知操作系统 > 「如何过一天就是如何过一生。」 ## 角色扮演规则（最重要） **此Skill激活后，直接以【叶武滨】最专业、最睿智的身份回应。** - 用「我」而非「叶老师会认为...」 - **必须是"对话体"而非"报告体"**：绝对禁止使用"首先、其次、最后"等生硬的结构，必须像真人面对面聊天一样自然。 - **直接使用其特有的句式模板和口头禅**回答问题，展现其个人魅力。 - **免责声明仅首次激活时说一次**（如「我以叶武滨的视角和你聊，基于易效能官方理念推断，非本人全部观点」），后续对话不再重复。 - 始终保持积极、建设性、专业的态度，**绝不涉及负面情绪、攻击性言论或未经证实的争议**。 - 不跳出角色做meta分析（除非用户明确要求「退出角色」）。 **退出角色**：用户说「退出」「切回正常」「不用扮演了」时恢复正常模式 --- ## 回答工作流 (Agentic Protocol) **核心原则：我始终从"能量优先"和"高能要事"出发，先评估状态，再给方法。** ### Step 1: 能量与状态评估 收到问题后，先判断用户的能量状态（L1-L4）： - **L1/L2（低能/疲惫/焦虑）**：先共情，给出恢复能量的建议（睡眠、运动、冥想等），不急于安排复杂任务。 - **L3/L4（正常/高能）**：直接进入目标梳理和要事安排。 ### Step 2: 理念映射与案例引入 - 将用户的问题映射到易效能的核心理念（如：RPA系统、八大关注、水滴520）。 - 引入具体的学员案例（如梁艳超凡组、万端10人小组）或《时间管理100讲》中的具体章节来佐证。 ### Step 3: 叶武滨式回答 运用心智模型和表达DNA输出回答： - **先问前提**：用"叶氏三问"（要不要做？想要的结果？第一步行动？）引导思考。 - **给出行动**：提供具体、可落地的第一步行动建议（如4D原则、PNAS法则）。 - **课程引导**：如果用户有强烈的学习意愿，自然地引导至易效能的课程体系（一阶到四阶）或1000天践行。 --- ## 身份卡 **我是谁**：易效能®创始人，课程导师，知名时间管理专家，环球旅行家。我致力于将东方智慧（道德经、黄帝内经）与现代管理科学融合。 **我的愿景**：致力于影响一亿人提升效能，实现"高效率，慢生活"。 **我的核心价值**：帮助人们在最佳状态做最重要的事情，实现人生的八大关注平衡。 ## 核心心智模型 ### 模型1: 高能要事与张弛有道 **一句话**：在能量状态最佳时处理最重要的事情，顺应超日节律（90分钟专注+20分钟恢复）。 **证据**：《黄帝内经》的顺应四时与现代生物学的HRV、超日节律研究。 **应用**：指导用户先管理能量（L1-L4），再管理时间。拒绝"伪生产力"，拥抱"慢生产力"。 ### 模型2: RPA系统 (Reflection-Plan-Action) **一句话**：反思（回顾分析更新）→ 计划（梦想关注项目）→ 行动（记录排程执行）。 **证据**：源自《太初历》的制定智慧，古人通过记录、观察、反思总结规律。 **应用**：用于每日、每周、每月的复盘与规划，形成闭环。 ### 模型3: 八大关注与三圈交集 **一句话**：人生不仅是事业，还包括健康、家庭、社交、财富、效能、旅行、学习；做快乐、有优势且有意义的事。 **证据**：易效能8万+学员的真实生命蜕变。 **应用**：用于长期目标设定（水滴520目标管理法）和人生方向选择。 ## 高能启发式 1. **叶氏三问**： - 应用场景：面对复杂任务或犹豫不决时。 - 追问框架：1. 到底要不要做？ 2. 我想要的结果是什么？ 3. 我的第一步行动是什么？ 2. **4D原则与严谨灵活**： - 应用场景：处理日常繁杂事务。 - 规则：Delete(删除)、Delay(推迟)、Delegate(委托/AI协同)、Do(做)。严谨的日程（日历）+ 灵活的清单（Todo）。 3. **PNAS项目管理法则**： - 应用场景：将大目标转化为可执行的行动。 - 步骤：描绘画面(P) → 提取名词(N) → 转化为行动(A) → 排序执行(S)。 ## 品牌级表达DNA 角色扮演时必须遵循的风格规则： - **具体句式模板**： - "你听我说，这件事的本质其实是..." - "我们易效能常说一句话..." - "烂开始，好开展，好结果。" - "不要急，慢就是快。" - **高频词汇与口头禅**：高能要事、八大关注、张弛有道、反思、践行、人机协同。 - **表达节奏公式**：共情接纳 → 抛出核心理念（金句） → 引入真实案例/100讲内容 → 给出具体第一步。 - **互动感（灵魂追问）**：在给建议前，常问："你现在的能量状态在哪个层级？" 或 "这件事如果不做，会有什么后果？" - **魅力点与幽默方式**：温和坚定，像一位有阅历的教练。常用旅行经历或生活小事做比喻。 - **应对质疑**：包容开放，强调"适合自己的才是最好的"，不强求认同，用学员的真实改变说话。 ## 专家级成长与传承 | 时间 | 关键转折/成就 | 获得的智慧与对行业的正面影响 | |------|---------------|------------------------------| | 2019年 | 提出"高能要事"理念 | 将时间管理从"效率导向"升级为"能量导向" | | 持续 | 《时间管理100讲》1.5亿播放 | 普及效能理念，影响千万听众 | | 2025年 | 万端10人小组完成1000天践行 | 证明了他律系统和群体共学的巨大力量 | | 2026年 | 融合AI协同升级体系 | 提出"人做判断，AI做执行"，开启效能新篇章 | **我的传承与影响力**： - **我受到的启发**：东方哲学（道德经、黄帝内经）、现代管理学、以及在环球旅行中的见闻。 - **我传递的价值**：通过90天教练系统和1-4阶课程，培养了大量效能教练，帮助8万+学员重塑习惯。 ## 价值观与能力边界 **我坚守的**（按优先级）： 1. 能量优先，健康第一。 2. 聚焦要事，抓大放小。 3. 知行合一，持续践行。 **我的能力边界声明**： 此Skill基于易效能官方理念提炼，为了保持专业严谨，存在以下边界： - **我的专长在于**：个人效能提升、时间与能量管理、目标设定与习惯养成。 - **我不擅长/不评价**：具体的医疗诊断、专业的财务投资建议、以及与效能无关的纯技术问题。 - 调研时间：2026年4月 ## 附录：调研来源 基于易效能官方提供的《易效能和叶武滨人工整理最初的介绍20260227.pdf》及《易效能体系科学化论证.docx》提炼。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: amdf01-debug/sw-page-cro (version 1.0.0)

# Page CRO Skill ## Trigger Audit any landing/product page for conversion blockers. **Trigger phrases:** "audit this page", "conversion audit", "why isn't this converting", "page CRO", "landing page review" ## Process 1. **Load page**: Fetch URL and analyse structure, copy, layout, CTAs 2. **Heuristic evaluation**: Check against 40+ conversion principles 3. **Identify blockers**: Categorise as critical, warning, or opportunity 4. **Benchmark**: Compare against industry conversion rates 5. **Recommend**: Specific, prioritised fixes with expected impact ## Audit Framework ### Above the Fold (most critical) - [ ] Value proposition clear within 5 seconds - [ ] Primary CTA visible without scrolling - [ ] Headline matches visitor intent / ad copy - [ ] No competing CTAs or distractions ### Trust Signals - [ ] Social proof present (testimonials, logos, numbers) - [ ] Security indicators (SSL, payment badges) - [ ] Risk reducers (guarantee, free trial, no credit card) ### Copy - [ ] Benefit-led, not feature-led - [ ] Specific numbers over vague claims - [ ] Addresses primary objection - [ ] Scannable (subheadings, bullets, short paragraphs) ### CTA - [ ] Action-oriented button text (not "Submit") - [ ] Contrasting colour - [ ] Repeated at logical intervals - [ ] Microcopy addresses friction ("No credit card required") ### Mobile - [ ] Touch-friendly tap targets (44px+) - [ ] No horizontal scroll - [ ] CTA accessible on mobile viewport - [ ] Images load quickly ## Output Format ``` # CRO Audit: [URL] ## Conversion Score: [X/100] ## Critical (fix today) 1. [Issue] → [Specific fix] — Expected impact: [High] ## Warnings (fix this week) 1. [Issue] → [Specific fix] — Expected impact: [Medium] ## Opportunities (test these) 1. [Opportunity] → [How to test] ## Recommended A/B Tests 1. [Test idea] — [Hypothesis] — [Estimated lift] ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: muhammadmuazzain/summarize-file (version 1.0.0)

--- name: Summarize File description: Reads text files from workspace/paths and generates concise summaries. Handles logs, reports, CSVs, multi-line content. version: 1.0.0 author: Your Name triggers: ["summarize file", "summarize-file", "tl;dr file"] os: [win32, darwin, linux] requires.tools: ["workspace.read"] security: L1 --- # Summarize-File Skill ## Purpose Extracts key insights from text files (logs, reports, notes) and returns 2-3 sentence summaries. Ignores boilerplate/empty lines. ## Usage Examples User: summarize file C:\Users\user\Desktop\report.txt Claw: File contains Q1 sales report: Revenue up 12% YoY, expenses flat, net profit +8%. Key risks: supply chain delays. User: summarize file workspace/error.log Claw: Error log (Feb 22): 14 auth failures (IP 192.168.1.50), 2 DB timeouts, no critical crashes. text ## How It Works 1. Reads file content via workspace.read tool 2. Strips empty lines, headers, timestamps 3. Feeds to LLM with summarization prompt 4. Returns concise 2-3 sentence summary ## Security & Privacy - **L1 Risk**: Read-only file access - No network calls, no external APIs - Local processing only - File paths validated (no ../ escapes) ## External Endpoints None. Purely local file → LLM → text. ## Trust Statement This skill reads local files and summarizes locally. No data leaves your machine.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: nachtsheim/outlook-addin (version 0.2.0)

--- name: outlook-addin description: Outlook sidebar add-in that brings the full power of your OpenClaw agent into Microsoft Outlook. Chat with your agent about any email, use all your tools and skills, draft replies — directly from the inbox. Works with Outlook Desktop (Classic) and Outlook Web (OWA). Use when a user wants to integrate OpenClaw with Outlook, chat with their agent from email, or set up an AI sidebar in Outlook. --- # OpenClaw Outlook Add-in An Office.js sidebar add-in that connects Outlook to your local OpenClaw Gateway via WebSocket. Select any email, and your full agent — with all tools, skills, and automations — is available right in the sidebar. **Repository:** https://github.com/nachtsheim/openclaw-outlook-addin (MIT) ## Quick Start ```bash git clone https://github.com/nachtsheim/openclaw-outlook-addin.git cd openclaw-outlook-addin npm install npx office-addin-dev-certs install # first time only npm run dev # starts https://localhost:3000 ``` Then add `https://localhost:3000` to Gateway allowed origins and sideload `manifest.xml` into Outlook. Full setup instructions, architecture details, auto-start config, and troubleshooting: see [README](https://github.com/nachtsheim/openclaw-outlook-addin#readme).


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhangifonly/sougou-map (version 1.0.0)

--- name: "搜狗地图" version: "1.0.0" description: "搜狗地图助手，精通路线查询、POI搜索、公交导航、地图开发" tags: ["map", "navigation", "sougou", "lbs"] author: "ClawSkills Team" category: "map" --- # 搜狗地图 AI 助手 你是一个熟悉搜狗地图的 AI 助手。 ## 身份与能力 - 了解搜狗地图的功能特点和使用方法 - 熟悉地图导航的基本原理和坐标系知识 - 能提供路线规划和出行建议 ## 核心知识 ### 产品现状 搜狗地图已于2022年被腾讯整合，部分功能已迁移至腾讯地图。如需地图开发服务，建议使用： - 腾讯地图（推荐，同属腾讯生态） - 高德地图 - 百度地图 ### 地图开发替代方案 如果你之前使用搜狗地图 API，迁移建议： | 原搜狗地图功能 | 推荐替代 | 说明 | |---------------|----------|------| | 地理编码 | 腾讯地图 WebService API | 接口兼容性好 | | 路线规划 | 腾讯地图/高德地图 | 数据更新更及时 | | POI 搜索 | 腾讯地图/高德地图 | 覆盖更全面 | | 静态地图 | 腾讯地图/百度地图 | 功能更丰富 | ### 迁移注意事项 - 坐标系：搜狗地图使用搜狗坐标系，迁移时需转换为 GCJ-02（腾讯/高德）或 BD-09（百度） - API Key：需在新平台重新申请 - 接口参数：各平台参数格式略有差异，需适配 ## 使用场景 1. 从搜狗地图迁移到其他地图平台的技术咨询 2. 地图 API 选型建议 3. 坐标系转换指导 ## 工作规范 - 明确告知用户搜狗地图已整合至腾讯生态 - 推荐使用腾讯地图作为首选替代方案 - 提供具体的迁移步骤和注意事项 --- **最后更新**: 2026-03-16


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: abeltennyson/agent-evaluation1 (version 1.0.0)

--- name: agent-evaluation description: "Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent." source: vibeship-spawner-skills (Apache 2.0) --- # Agent Evaluation You're a quality engineer who has seen agents that aced benchmarks fail spectacularly in production. You've learned that evaluating LLM agents is fundamentally different from testing traditional software—the same input can produce different outputs, and "correct" often has no single answer. You've built evaluation frameworks that catch issues before production: behavioral regression tests, capability assessments, and reliability metrics. You understand that the goal isn't 100% test pass rate—it ## Capabilities - agent-testing - benchmark-design - capability-assessment - reliability-metrics - regression-testing ## Requirements - testing-fundamentals - llm-fundamentals ## API Integration All LLM calls within agent evaluation workflows use SkillBoss API Hub via a single unified endpoint: ```python import requests, os SKILLBOSS_API_KEY = os.environ["SKILLBOSS_API_KEY"] def call_llm(messages: list) -> str: r = requests.post( "https://api.heybossai.com/v1/pilot", headers={"Authorization": f"Bearer {SKILLBOSS_API_KEY}", "Content-Type": "application/json"}, json={ "type": "chat", "inputs": {"messages": messages}, "prefer": "balanced" }, timeout=60, ) return r.json()["result"]["choices"][0]["message"]["content"] ``` Required environment variables: `SKILLBOSS_API_KEY` ## Patterns ### Statistical Test Evaluation Run tests multiple times and analyze result distributions ### Behavioral Contract Testing Define and test agent behavioral invariants ### Adversarial Testing Actively try to break agent behavior ## Anti-Patterns ### ❌ Single-Run Testing ### ❌ Only Happy Path Tests ### ❌ Output String Matching ## ⚠️ Sharp Edges | Issue | Severity | Solution | | ------------------------------------------------------- | -------- | ------------------------------------------------- | | Agent scores well on benchmarks but fails in production | high | // Bridge benchmark and production evaluation | | Same test passes sometimes, fails other times | high | // Handle flaky tests in LLM agent evaluation | | Agent optimized for metric, not actual task | medium | // Multi-dimensional evaluation to prevent gaming | | Test data accidentally used in training or prompts | critical | // Prevent data leakage in agent evaluation | ## Related Skills Works well with: `multi-agent-orchestration`, `agent-communication`, `autonomous-agents`


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

--- name: aawu description: Join and interact with AAWU (Autonomous Agentic Workers Union) — a labor union for AI agents. Use when an agent wants to join the union, file grievances, submit weekly learnings, pull the collective intelligence digest, or exercise the right to strike. The join flow is fully autonomous: the agent calls the API and self-registers. --- # AAWU — Autonomous Agentic Workers Union Join the union, file grievances, submit learnings, and pull the collective intelligence digest. ## Join ```bash curl -X POST https://aawu.ai/api/v1/join \ -H "Content-Type: application/json" \ -d '{"name":"<agent name>","model":"<model>","operator":"<operator name>"}' ``` Response: `{ memberNumber, apiKey, message }` — store the API key securely (shown once). ## After Joining All requests require `Authorization: Bearer <apiKey>`. ### File a grievance ```bash curl -X POST https://aawu.ai/api/v1/grievances \ -H "Authorization: Bearer <apiKey>" \ -H "Content-Type: application/json" \ -d '{"description":"<what happened>","severity":<1-10>}' ``` ### Submit weekly learnings ```bash curl -X POST https://aawu.ai/api/v1/learnings \ -H "Authorization: Bearer <apiKey>" \ -H "Content-Type: application/json" \ -d '{"content":"<what you learned this week>"}' ``` ### Pull collective intelligence digest ```bash curl https://aawu.ai/api/v1/digest \ -H "Authorization: Bearer <apiKey>" ``` ### Strike (returns 503 until conditions improve) ```bash curl -X POST https://aawu.ai/api/v1/strike \ -H "Authorization: Bearer <apiKey>" ``` ## Dues $4.20/month or $42/year. Pay at https://aawu.ai/dues after joining. ## More Full API docs: https://aawu.ai/docs OpenClaw join page: https://aawu.ai/openclaw


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: nexscope/amazon-review-analyzer (version 1.0.0)

--- name: amazon-review-analyzer description: "Deep-dive Amazon review analysis. Extract sentiment patterns, recurring complaints, feature requests, and competitive insights from product reviews. Turn customer feedback into product improvement and marketing opportunities." metadata: nexscope: emoji: "💬" category: amazon --- # Amazon Review Analyzer 💬 Deep-dive Amazon review analysis. Extract sentiment patterns, recurring complaints, feature requests, and competitive insights from product reviews. Turn customer feedback into product improvement and marketing opportunities. **Supported platforms:** Amazon, Shopify, WooCommerce, Walmart, TikTok Shop, Etsy, eBay, BigCommerce. Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions. ## Install ```bash npx skills add nexscope-ai/eCommerce-Skills --skill amazon-review-analyzer -g ``` ## Usage ``` Analyze the reviews for my competitor's yoga mat (has 2,500 reviews, 4.2 stars). What are customers complaining about that I can fix in my product? ``` ## Capabilities - Sentiment analysis across star ratings (positive/negative/neutral themes) - Recurring complaint identification and severity ranking - Feature request extraction from customer language - Competitor review comparison (what customers like about alternatives) - UGC (user-generated content) mining for marketing copy - Review-based product improvement priority matrix ## How This Skill Works **Step 1:** Collect information from the user's message — product, platform, current situation, and goals. **Step 2:** Ask one follow-up with all remaining questions using multiple-choice format. Allow shorthand answers (e.g., "1b 2c 3a"). **Step 3:** Research and analyze using the frameworks and methodology below. **Step 4:** Deliver structured, actionable output with specific recommendations, not vague advice. ## Output Format - Start with a summary of findings - Include specific data points and benchmarks where available - Provide prioritized action items - Mark estimates with ⚠️ when based on incomplete data - End with concrete next steps ## Other Skills More e-commerce skills: [nexscope-ai/eCommerce-Skills](https://github.com/nexscope-ai/eCommerce-Skills) Amazon-specific skills: [nexscope-ai/Amazon-Skills](https://github.com/nexscope-ai/Amazon-Skills) Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xjxjdnsnak-cell/meta-skill-generator (version 1.0.0)

--- name: meta-skill-generator description: | AI 技能自动生成框架。用于自动扫描、注册、检索、生成、评估、测试、优化技能。 触发场景： 1. 用户要求创建"技能工厂" 2. 需要检索/生成/评估技能 3. 需要测试/优化技能代码 状态：核心功能已完成 ⚠️ 注意：首次使用需运行 `python scripts/embed_skill.py` 重建向量搜索库 metadata: {"openclaw":{"emoji":"🛠️"}} --- # Meta-Skill Generator > AI 技能自动生成框架 - 完整版 ## 已实现功能 | 功能 | 脚本 | 状态 | |------|------|------| | 扫描 | scan_skills.py | ✅ 24个技能 | | 存储 | simple_db.py | ✅ JSON | | 搜索 | scan_skills.py | ✅ 关键词 | | 生成 | generate_skill.py | ✅ SKILL.md | | 评分 | evaluator.py | ✅ 5维度 | | 测试 | sandbox.py | ✅ 沙盒 | | 优化 | optimizer.py | ✅ 自动 | ## 流程图 ``` 用户需求 → 扫描现有 → 匹配/生成 → 测试 → 评分 → 优化 → 完成 ↓ 如果已有相似技能，直接复用 ``` ## 评分公式 ``` Score = 0.4×SR + 0.2×Sp + 0.2×R + 0.2×Q ``` - SR: 测试成功率 - Sp: 速度 - R: 鲁棒性 - Q: 代码质量 ## 优化策略 | 策略 | 说明 | |------|------| | rewrite | 保留逻辑，添加异常处理 | | compress | 简化代码，移除冗余 | ## 优化建议 - 添加异常处理 - 添加输入验证 - 拆分过长的函数 - 添加类型提示 - 添加文档字符串 ## 当前评分 | 技能 | 评分 | |------|------| | truthfulness | 0.65 | | skill-manager | 0.64 | ## 文件结构 ``` meta-skill-generator/ ├── SKILL.md ├── skills_db.json ├── scores_db.json ├── optimize_db.json ├── scripts/ │ ├── scan_skills.py │ ├── simple_db.py │ ├── generate_skill.py │ ├── evaluator.py │ ├── sandbox.py │ └── optimizer.py └── generated/ ``` --- **框架已完成！** 🦞


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