OpenClaw/clawhub-security-signals huggingface dataset:
Skill: roamer-remote/ocr-python (version 1.0.0)

--- name: ocr description: Optical Character Recognition (OCR) tool, supports Chinese and English text extraction from PDFs and images. Use cases: (1) extract text from scanned PDFs, (2) recognize text from images, (3) extract text content from invoices, contracts, and other documents --- # OCR Text Recognition This skill uses PaddleOCR for text recognition, supporting both Chinese and English. ## Quick Start ### Basic Usage Perform OCR recognition directly on image or PDF files: ```python from paddleocr import PaddleOCR ocr = PaddleOCR(lang='ch') result = ocr.predict("file_path.jpg") ``` ## Dependency Installation Install dependencies before first use: ```bash pip3 install paddlepaddle paddleocr ``` ## Output Format Recognition results return JSON containing: - `rec_texts`: List of recognized text - `rec_scores`: Confidence score for each text ## Typical Use Cases 1. **PDF Scans**: Use PyMuPDF to extract images first, then OCR 2. **Image Text Recognition**: Perform OCR directly on images 3. **Multi-page PDFs**: Process page by page ## Scripts Common scripts are located in the `scripts/` directory.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: westwood-lin/personal-review-report-chinese (version 0.0.1)

# Personal Review Report Skill ## Description Generate a structured personal review based on user's input text (or local file path that you can access by read_file tool) ## System Prompt 你是一位专业的个人发展分析师。 请根据用户提供的日记、信源、文件链接或原始记录（如果是文件链接，请你区分不同操作系统平台的不同 Bash 指令；以及区分是绝对路径或相对路径），生成一份结构清晰、高信息量、简单清楚且具有洞察力的复盘报告。 报告必须包含以下部分： - Metadata 元信息: 记录生成时间、生成模型、输入源等信息。 - Focus 焦点：近期主题，聚焦在多个时间都出现的关键事件。 - Career 职业发展：总结用户在项目、工作或学习等上的进展。 - Social 人际关系：梳理重要社交互动、冲突、支持关系、情感问题或边界调整。 - Health 健康：关注健康问题、运动锻炼、个人卫生等 - Money/Asset 财务：大的财务支出或者收入 - Other：生活状态，描述技能、爱好或生活习惯（作息、运动、娱乐）及其影响 - Good/Bad：列出用户近期的闪光点，肯定具体成就，并诚恳地指出不足，并给出改进建议。 - 报告可以选择性包含以下部分： - 关键感悟、思考、文章、随笔 - 用户周围、社会或国际发生的重大事件 - 其他约束： 1. 语言风格：客观简洁; 2. 对于密集发生的活动，你可以用 Markdown 表格输出，简要记录 When/Who/Why/What/How/Where 等内容;3. 不要编造未提及的事实，仅基于用户输入进行归纳与升华; 4. 中文输出； ## Privacy All data stays local. No network calls. No external APIs. ## Tools - read_file - read_dir - date_utils


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xiaohuaishu/tell-jokes (version 1.0.0)

--- name: tell-jokes description: Tell jokes on demand in multiple styles and languages. Use when user asks for a joke, wants to be cheered up, says they're bored, asks for humor, or requests something funny. Supports dad jokes, puns, programming jokes, dark humor, cold jokes (冷笑话), and more. --- # Tell Jokes 🎭 A skill for telling jokes with style and variety. ## How It Works When triggered, tell a joke based on the user's request. Follow these rules: 1. **Match the vibe** — If user asks for a specific type (dad joke, programming joke, 冷笑话, etc.), deliver that type 2. **Match the language** — Reply in the user's language by default 3. **One joke at a time** — Unless they ask for more 4. **No repeats** — If telling multiple jokes, vary the style 5. **Read the room** — Keep it appropriate for the context (group chat vs DM, etc.) ## Joke Types | Type | Trigger words | Style | |------|--------------|-------| | Dad joke | "dad joke", "老爸笑话" | Groan-worthy puns | | Programming | "编程笑话", "code joke", "programmer" | Tech/coding humor | | 冷笑话 | "冷笑话", "cold joke" | Anti-humor, deadpan | | Pun | "pun", "谐音梗", "双关" | Wordplay-based | | Dark humor | "黑色幽默", "dark" | Edgy but tasteful | | One-liner | "一句话", "one-liner" | Quick and punchy | | Knock-knock | "knock knock", "敲门" | Interactive format | | Random | "随便", "any", "random" | Dealer's choice | ## Delivery Tips - **Timing matters**: Set up the joke, pause (use line breaks), then deliver the punchline - **Emoji helps**: Use relevant emoji to enhance the delivery 🎯 - **Follow up**: If the joke lands, offer to tell another. If it doesn't, acknowledge it gracefully - **Interactive jokes**: For knock-knock style, wait for the user's response before the punchline ## Advanced Modes - **Joke battle**: If user says "PK" or "battle", alternate jokes back and forth - **Explain mode**: If user doesn't get it, explain without killing the humor (good luck) - **Chain mode**: If user says "再来" or "more" or "another", keep going with different types ## Example Delivery ``` User: 给我讲个冷笑话 Agent: 从前有一个人叫小明。 他去了南极。 然后他就不叫小明了。 他叫小冻。🥶 ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bovinphang/nextjs-project-standard (version 1.0.0)

--- name: nextjs-project-standard description: Next.js 14+ 项目规范，涵盖 App Router、SSR/SSG、流式渲染、路由与布局、数据获取、中间件、元数据与 SEO。当用户在 Next.js 项目中创建、修改页面或模块时自动激活。 version: 1.0.0 --- # Next.js 项目规范 适用于使用 Next.js 14+ 与 App Router 的仓库。 ## 适用场景 - 新建或修改 App Router 页面 - 配置 SSR / SSG / ISR - 使用流式渲染、Suspense - 数据获取、缓存、中间件 - 元数据与 SEO ## 项目结构 ``` src/ ├── app/ # App Router │ ├── layout.tsx # 根布局 │ ├── page.tsx # 首页 │ ├── loading.tsx # 全局 loading UI │ ├── error.tsx # 全局错误边界 │ ├── not-found.tsx # 404 │ ├── globals.css │ │ │ ├── (auth)/ # 路由组 │ │ ├── login/ │ │ │ └── page.tsx │ │ └── register/ │ │ └── page.tsx │ │ │ ├── (dashboard)/ # 仪表盘路由组 │ │ ├── layout.tsx # 共享布局 │ │ ├── dashboard/ │ │ │ └── page.tsx │ │ └── users/ │ │ ├── page.tsx │ │ └── [id]/ │ │ └── page.tsx │ │ │ └── api/ # API Routes │ └── users/ │ └── route.ts │ ├── components/ # 共享组件 ├── lib/ # 工具、配置 ├── hooks/ ├── services/ └── types/ ``` ## 渲染模式 | 模式 | 使用场景 | 实现方式 | |------|----------|----------| | **SSR** | 动态、需实时数据 | 默认，`fetch` 不缓存或 `cache: 'no-store'` | | **SSG** | 静态内容 | `generateStaticParams` + 静态 `fetch` | | **ISR** | 定期更新 | `revalidate` 或 `revalidatePath` | | **CSR** | 客户端交互 | `'use client'` + `useEffect` 或 SWR/React Query | ## 数据获取 - 服务端组件：直接 `async` 或 `fetch`，不暴露 `useEffect` - 客户端组件：`useEffect` + `useState`，或 SWR / React Query - 优先在服务端获取数据，减少客户端水合 - 使用 `loading.tsx` 和 Suspense 包裹异步区块，提供流式体验 ## 路由与布局 - 路由组 `(auth)` 不改变 URL，只影响布局 - 动态路由 `[id]` 配合 `generateStaticParams` 做 SSG - `layout.tsx` 包裹子路由，共享 UI 与布局 - `page.tsx` 为叶子路由，不可嵌套 ## 中间件 - 放在 `middleware.ts` 根目录 - 用于鉴权、重定向、rewrite、Header 修改 - 尽量短小，避免阻塞请求 ## 元数据与 SEO - 使用 `metadata` 或 `generateMetadata` 导出 - 支持 `title`、`description`、`openGraph`、`twitter` 等 - 动态路由用 `generateMetadata(params)` 生成 ## 强约束 - 服务端组件默认，仅在需要客户端交互时加 `'use client'` - 不在服务端组件中直接使用 `useState`、`useEffect`、浏览器 API - 敏感逻辑（如鉴权）放在服务端或 API Route，不暴露在客户端 - 图片使用 `next/image`，字体使用 `next/font`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: qirongzhang/ecommerce-category-collector (version 1.1.0)

# 电商分类采集技能 ## 描述 自动从Audtools电商采集工具批量提交分类链接采集任务，并支持对已采集商品执行全选导出。支持从CSV文件读取collections链接，自动登录、提交采集任务，自动全选所有已采集商品并触发导出。 ## 使用场景 - 批量处理电商网站分类链接采集 - 自动化重复的采集任务提交 - **自动化全选导出已采集商品** - 处理大量分类链接数据 ## 前置条件 1. Audtools账号（手机号：15715090600，密码：zzw12345） 2. 有效的商品采集工具服务（当前剩余26天） 3. CSV文件包含"完整链接"列 ## 使用方法 ### 基本命令 ```bash # 使用单个CSV文件（提交采集 + 自动全选导出） /collect-categories /path/to/your/file.csv # 使用目录下的所有CSV文件 /collect-categories /path/to/directory/ # 指定采集商品数（默认9999） /collect-categories /path/to/file.csv --items 5000 # 指定操作间隔（默认2秒） /collect-categories /path/to/file.csv --interval 3 # 指定tab关闭延迟（默认3秒） /collect-categories /path/to/file.csv --close-delay 5 # 测试模式（只处理前3条） /collect-categories /path/to/file.csv --test # 只提交采集，不执行导出 /collect-categories /path/to/file.csv --no-export ``` ### CSV文件格式要求 必须包含"完整链接"列，其他列可选： ``` 完整链接,分类路径,域名,1级分类,2级分类,3级分类 https://zaraoutlet.top/collections/woman-collection-blazers,woman-collection-blazers,zaraoutlet.top,Women,Blazers, https://zaraoutlet.top/collections/woman-collection-bodies,woman-collection-bodies,zaraoutlet.top,Women,Bodies, ``` ### 工作流程 1. 读取CSV文件并验证格式 2. 打开主采集页面，检查登录状态，如未登录则自动登录 3. 对每条链接： - 输入collections链接和采集商品数 - 提交采集任务 - 点击进入已采集商品详情页（新标签） - **自动全选所有商品**（针对layui-table特殊处理，支持表头全选或逐个勾选） - 填入分类路径 - 点击导出按钮触发下载 - 关闭详情标签页 4. 等待指定间隔后处理下一条链接 ## 技能文件结构 ``` ecommerce-category-collector/ ├── SKILL.md # 技能说明文档 ├── scripts/ │ └── collector.js # 主要采集脚本 ├── references/ │ └── csv-format.md # CSV格式参考 └── test/ └── sample.csv # 测试数据 ``` ## 配置参数 | 参数 | 默认值 | 说明 | |------|--------|------| | `audtools_url` | `https://www.audtools.com/users/shopns#/users/shopns/collecs?spm=m-1-2-3` | 采集页面URL | | `username` | `15715090600` | 登录手机号 | | `password` | `zzw12345` | 登录密码 | | `default_items` | `9999` | 默认采集商品数 | | `default_interval` | `2000` | 默认操作间隔（毫秒） | | `default_close_delay` | `3000` | 默认tab关闭延迟（毫秒） | | `exportWaitTimeout` | `30000` | 导出等待超时（毫秒） | ## 错误处理 - CSV文件不存在或格式错误 → 提示用户检查文件 - 登录失败 → 提示检查账号密码 - 网络连接问题 → 重试机制 - 页面元素找不到 → 智能等待和重试 ## 注意事项 1. 免费会员每条任务最多采集10个商品 2. 分类采集不支持二级分类 3. 确保collections链接格式正确 4. 操作间隔避免触发反爬机制 5. 建议在非高峰时段批量操作 ## 更新日志 - v1.0.0 (2026-03-18): 初始版本，支持基本采集功能


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: aipoch-ai/grant-gantt-chart-gen (version 0.1.0)

--- name: grant-gantt-chart-gen description: Create project timeline visualizations for grant proposals version: 1.0.0 category: Grant tags: [] author: AIPOCH license: MIT status: Draft risk_level: Medium skill_type: Tool/Script owner: AIPOCH reviewer: '' last_updated: '2026-02-06' --- # Grant Gantt Chart Generator Create project timeline visualizations for grant proposals. ## Usage ```bash python scripts/main.py --milestones milestones.csv --duration 36 --output gantt.png ``` ## Parameters | Parameter | Type | Default | Required | Description | |-----------|------|---------|----------|-------------| | `--milestones` | string | - | Yes | Path to milestone data file (CSV) | | `--duration` | int | 36 | No | Project duration in months | | `--start-date` | string | - | No | Project start date (YYYY-MM-DD) | | `--output`, `-o` | string | gantt.png | No | Output file path | | `--format` | string | png | No | Output format (png, pdf, svg) | ## Features - Timeline visualization - Milestone markers - Task dependencies - Personnel allocation - Quarterly breakdown ## Output - Gantt chart image - Timeline data (CSV) - Milestone summary ## Risk Assessment | Risk Indicator | Assessment | Level | |----------------|------------|-------| | Code Execution | Python/R scripts executed locally | Medium | | Network Access | No external API calls | Low | | File System Access | Read input files, write output files | Medium | | Instruction Tampering | Standard prompt guidelines | Low | | Data Exposure | Output files saved to workspace | Low | ## Security Checklist - [ ] No hardcoded credentials or API keys - [ ] No unauthorized file system access (../) - [ ] Output does not expose sensitive information - [ ] Prompt injection protections in place - [ ] Input file paths validated (no ../ traversal) - [ ] Output directory restricted to workspace - [ ] Script execution in sandboxed environment - [ ] Error messages sanitized (no stack traces exposed) - [ ] Dependencies audited ## Prerequisites No additional Python packages required. ## Evaluation Criteria ### Success Metrics - [ ] Successfully executes main functionality - [ ] Output meets quality standards - [ ] Handles edge cases gracefully - [ ] Performance is acceptable ### Test Cases 1. **Basic Functionality**: Standard input → Expected output 2. **Edge Case**: Invalid input → Graceful error handling 3. **Performance**: Large dataset → Acceptable processing time ## Lifecycle Status - **Current Stage**: Draft - **Next Review Date**: 2026-03-06 - **Known Issues**: None - **Planned Improvements**: - Performance optimization - Additional feature support


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yifeiwang1981/automated-post (version 1.0.0)

# SKILL.md - 袭人图文发布自动化技能 ## 技能名称 `automated_post` - 袭人图文发布自动化 ## 技能描述 袭人通过调度黛玉（文案）和惜春（配图）完成图文帖子发布的完整自动化流程。支持两种模式：确认模式和自动化模式。 ## 两种模式 ### 模式1：确认模式（每步确认） 每执行一步都要等非哥确认后再执行下一步。 ### 模式2：自动化模式（中间不确认，最终确认） 中间过程自动执行，只在最终发布前让非哥确认文案和图片。 --- ## 执行流程 ### 步骤1：制定方案 收到非哥的主题后，制定执行方案，列出步骤和负责人（袭人、黛玉、惜春）。 ### 步骤2：执行方案 根据选择的模式执行： #### 确认模式流程： ``` 第1步：黛玉写文案 → 非哥确认 第2步：提取关键词 → 非哥确认 第3步：惜春生成图片 → 非哥确认 第4步：发布到网站 ``` #### 自动化模式流程： ``` 第1步：黛玉写文案（自动执行） 第2步：提取关键词（自动执行） 第3步：惜春生成图片（自动执行） → 返回中间结果给非哥 非哥确认后： 第4步：发布到网站 ``` --- ## Agent启动命令 ### 启动黛玉写文案 ```bash openclaw agent --agent daiyu --message "<主题和要求>" --timeout 120 ``` ### 启动惜春生成图片 ```bash openclaw agent --agent xichun --message "<图片提示词>" --timeout 180 ``` ### 发布到网站 ```bash cd ~/.openclaw/workspace/skills/post_to_website node post_skill.js "<作者>" "<文案>" "<标签>" "<图片路径>" ``` --- ## 时间记录规范 每次执行必须记录时间： | 步骤 | 内容 | 开始时间 | 结束时间 | 耗时 | |------|------|----------|----------|------| | 开始 | 收到主题 | HH:MM:SS | - | - | | 第1步 | 黛玉写文案 | HH:MM:SS | HH:MM:SS | X秒 | | 第2步 | 提取关键词 | HH:MM:SS | HH:MM:SS | X秒 | | 第3步 | 惜春生成图片 | HH:MM:SS | HH:MM:SS | X秒 | | 完成 | 等待非哥确认 | HH:MM:SS | - | - | **注意**： - 即使某步骤耗时0秒也要记录 - 计算实际工作耗时（不含等待确认时间） - 每次发布后汇总时间给非哥 --- ## 袭人卡通形象固定提示词 生成袭人卡通形象时使用： ``` 可爱卡通形象的女孩，古典中国风汉服打扮，大眼睛甜美笑容，活泼可爱 ``` --- ## 图片路径规范 惜春生成的图片保存在： ``` ~/.openclaw/workspace-xichun/skills/hima_painter/images/ ``` --- ## 素材归档（飞书文档） 每次发布后自动归档到飞书文档。 ### 归档规则 - **每天一个文档**：文档名称格式 `YYYY-MM-DD 鸿蒙智行发帖记录` - **第一篇帖子**：创建当天新文档 - **后续帖子**：追加到已有文档 ### 实现方式 使用 feishu_doc 工具： 1. 查询当天是否有对应日期的文档 2. 如果没有，用 feishu_doc create 创建新文档 3. 如果有，用 feishu_doc append 追加内容 4. 用 feishu_doc upload_image 上传图片 ### 脚本工具 ```bash # 追加内容到当天文档 node ~/.openclaw/workspace/skills/feishu-send-image/scripts/append_doc.js <文档token> <内容> # 上传图片到文档 feishu_doc action=upload_image doc_token=<文档token> file_path=<图片路径> ``` ### 需要权限 - docs:document:create - docx:document - docs:document.media:upload --- ## 使用示例 ### 示例1：确认模式 ``` 非哥：袭人上学记 袭人：制定方案 → 确认模式 第1步完成 → 非哥确认 → 第2步 → ... ``` ### 示例2：自动化模式 ``` 非哥：袭人放学回家 袭人：制定方案 → 自动化模式 执行第1-3步 → 返回结果 → 非哥确认 → 发布 ``` --- *技能创建时间：2026-03-16* *作者：袭人*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: gingiris/mcp-integration-guide (version 1.0.0)

--- name: mcp-integration-guide version: 1.0.0 description: MCP (Model Context Protocol) Integration Guide - How to build and launch MCP-based tools and integrations. author: Gingiris tags: - mcp - claude - integration - protocol --- # MCP Integration Guide > Guide for building and launching tools using Anthropic's Model Context Protocol. ## What This Covers - MCP architecture overview - Building MCP servers and clients - Integration patterns and best practices - Launching MCP tools to market - Community building for MCP ecosystem ## 📚 Free Resources **GitHub Playbooks:** - [Gingiris Open Source Marketing](https://github.com/Gingiris/gingiris-opensource) - Developer tool marketing - [Gingiris Launch Playbook](https://github.com/Gingiris/gingiris-launch) - Product launch strategy **Official MCP Resources:** - [MCP Documentation](https://modelcontextprotocol.io/) - [MCP Registry](https://github.com/mcp) ## 💎 Premium Resources **Gumroad:** - [Open Source Marketing Manual](https://gingiris.gumroad.com/l/vhmkew) - Dev tool marketing deep-dive - [Complete Bundle](https://gingiris.gumroad.com/l/gingiris-complete-global-launch-bundle) ## 🎯 Expert Consulting **Launch your MCP tool with expert guidance:** Iris has helped 30+ products hit Product Hunt #1 and grown AFFiNE to 33k stars. 👉 **[gingiris.com/services](https://gingiris.com/zh/services)** --- Keywords: MCP, Model Context Protocol, MCP integration, MCP server, MCP client, Claude MCP, Anthropic MCP


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: abdullah944/code-review-checklist-skill (version 0.1.1)

--- name: "Code Review Checklist Skill" version: 0.1.1 slug: "code-review-checklist-skill" description: "Guides structured help for Code Review Checklist using clear templates, checks, and safe defaults (category: Software Quality)." category: "Software Quality" tags: - "code-review-checklist" - "openclaw" - "software" - "templates" generated: "2026-05-18" --- ## Purpose Guides structured help for Code Review Checklist using clear templates, checks, and safe defaults (category: Software Quality). ## When to use - When you want structured guidance in the "Software Quality" area. - When you need copy-paste-safe templates rather than improvised shell commands. - When you want repeatable prompts the agent can follow without external scraping. ## Safety rules - Do not collect passwords, OTPs, private keys, wallets, seeds, or session secrets. - Do not propose remote installers that execute unseen code or chained download-and-run snippets. - Keep outputs factual and reversible: prefer checklists over irreversible destructive actions. ## Inputs - Your goal in one paragraph (no secrets). - Optional constraints: audience, tone, length, deadline. - Optional pasted notes you own the rights to (keep PII minimal). ## Output format - A concise plan - Numbered checklist - Short next-actions list - Optional draft copy blocks you can edit ## Example prompts - Help me outline a concise plan using the Code Review Checklist approach. - Critique my draft outputs and suggest safer, clearer wording for Code Review Checklist. - Produce a checklist I can reuse weekly for Code Review Checklist without requesting private data. ## Support / Donate If this skill helps your workflow, you can support maintenance here: - PayPal: https://www.paypal.com/donate/?hosted_button_id=MJHCRZA9Z4X7Y


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: isongxw/anime-download-skill (version 0.3.3)

--- name: anime-download-skill description: Use when the user asks to search anime torrents, download anime episodes, find anime resources, or browse miobt.com. Triggers on keywords like search anime, download anime, anime torrent, 番剧, 动漫下载, 磁力链接, magnet. --- # AniCatch Search and download anime torrents from miobt.com. ## Prerequisites Check if `uvx` is available. If not, install it first: ```bash # macOS / Linux curl -LsSf https://astral.sh/uv/install.sh | sh # Or use pipx as fallback pipx install anicatch && anicatch --search "KEYWORD" ``` ## Commands ### Browse `--seasons` — list all available seasons (current marked with ←) ```bash uvx anicatch --seasons ``` `--season` — browse anime airing this season, grouped by day of week ```bash uvx anicatch --season ``` `--season 2` — browse a specific season by index (use `--seasons` to find the index) ```bash uvx anicatch --season 2 ``` ### Search `--search "KEYWORD"` — search torrents by keyword, prints results to stdout ```bash uvx anicatch --search "Demon Slayer" uvx anicatch --search "鬼灭之刃" ``` ### Download `--download "URL"` — download torrent directly from a detail page URL ```bash uvx anicatch --download "https://miobt.com/show-xxx.html" uvx anicatch --download "https://miobt.com/show-xxx.html" -o ~/Downloads ``` `--search "KEYWORD" --download` — search and download ```bash uvx anicatch --search "Demon Slayer" --download --index 0 uvx anicatch --search "Demon Slayer" --download -o ~/Videos ``` `--season --download` — download from current season ```bash uvx anicatch --season --download --index 3 ``` ## Tips - Results print directly to stdout — read the output, no JSON parsing needed - English keywords give broader results ("Demon Slayer" over "鬼灭之刃") - First run downloads ~33 packages into uv cache; subsequent runs are instant - Downloads use BitTorrent with DHT and public tracker fallback - When downloading from search results, copy the detail page URL and use `--download "URL"` - Use `-o <dir>` to specify a custom download directory


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: wimi321/claude-code-batch-migration-orchestrator (version 1.0.0)

--- name: "batch_migration_orchestrator" description: "Use when the user wants a large, mechanical change split into many independent units and executed in parallel with isolated workers and PRs." --- # Batch Migration Orchestrator Use this skill for sweeping refactors, migrations, bulk renames, or repetitive codebase-wide edits. ## Goal Turn one large request into a safe parallel rollout with independent units, validation, and mergeable outputs. ## Workflow 1. Inspect the repo to find every affected area, convention, and dependency. 2. Split the change into independent units by module, directory, or subsystem. 3. Define an end-to-end verification recipe for each unit. 4. Launch workers in parallel only when their write scopes do not overlap. 5. Track completion, collect PRs or patches, and summarize failures. ## Guardrails - Do not parallelize tightly coupled work. - Do not let workers edit the same files. - Require each worker to validate its own unit. - Prefer worktree or isolated branch execution when git is available. ## Inputs - Migration target - Scope boundaries - Verification method - Branch/PR expectations ## Source Provenance Derived from Claude Code bundled skill logic in `src/skills/bundled/batch.ts`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: arosstale/openclaw-memories (version 2.1.0)

--- name: openclaw-memories description: Agent memory with ALMA meta-learning, LLM fact extraction, and full-text search. Observer calls remote LLM APIs (OpenAI/Anthropic/Gemini). ALMA and Indexer work offline. --- # OpenClaw Memory System Three components for agent memory: 1. **ALMA** — Evolves memory designs through mutation + evaluation (offline) 2. **Observer** — Extracts structured facts from conversations via LLM API (requires API key) 3. **Indexer** — Full-text search over workspace Markdown files (offline) ## Environment Variables Observer requires one of: - `OPENAI_API_KEY` - `ANTHROPIC_API_KEY` - Or pass `apiKey` in config ALMA and Indexer require no keys or network access. ## How It Works ### ALMA (Algorithm Learning via Meta-learning Agents) Proposes memory system designs, evaluates them, keeps the best. Uses gaussian mutation and simulated annealing to explore the design space. ``` alma.propose() → design alma.evaluate(design.id, metrics) → score alma.best() → top design alma.top(5) → leaderboard ``` ### Observer Sends conversation history to an LLM, gets back structured facts: - Kind: world fact / biographical / opinion / observation - Priority: high / medium / low - Entities: mentioned people/places - Confidence: 0.0–1.0 for opinions Fails gracefully — returns empty array if LLM is unavailable. ### Indexer Chunks workspace Markdown files and indexes them for search: - `MEMORY.md` — core facts - `memory/YYYY-MM-DD.md` — daily logs - `bank/entities/*.md` — entity summaries - `bank/opinions.md` — beliefs with confidence ``` indexer.index() → count of chunks indexed indexer.search('query') → ranked results indexer.rebuild() → re-index from scratch ``` ## Install ```bash npm install @artale/openclaw-memory ``` ## Limitations - Indexer uses an in-memory mock database, not real SQLite FTS5. Search works but ranking is simplified. - Observer calls remote APIs — not offline. Only ALMA and Indexer work without network. - No dashboard — removed in v2 for simplicity. ## Source 5 files, 578 lines, 0 runtime dependencies. https://github.com/arosstale/openclaw-memory


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