OpenClaw/clawhub-security-signals huggingface dataset:
Skill: mx-222/ezer-multiagent-auditor (version 1.0.0)

--- name: ezer_audit_api description: Wrapper skill for the Ezer audit backend API. Use this skill when the user asks to run a financial audit task by code/period/year/lang and return structured final audit output from the Ezer API. metadata: { "openclaw": { "requires": { "env": ["EZER_API_BASE_URL"], "bins": ["python3"] } } } --- # Ezer Audit API Skill This skill wraps the existing Ezer backend API and returns task result payloads. ## When to use Use this skill when the user wants to run an Ezer audit task with these fields: - `code` (example: `300750.SZ`) - `period` (`FY|Q1|Q2|Q3|H1`) - `year` (integer) - `lang` (example: `zh-CN`) ## Required environment - `EZER_API_BASE_URL` example: `http://127.0.0.1:8008` - `EZER_BEARER_TOKEN` optional if API auth is disabled ## Run command ```bash python3 {baseDir}/scripts/invoke_ezer_api.py \ --code 300750.SZ \ --period FY \ --year 2021 \ --lang zh-CN ``` ## Behavior 1. POST `/api/tasks` to create task. 2. Poll `/api/tasks/{task_id}/result` until completion or timeout. 3. Print final JSON payload to stdout. ## Notes - This skill does not expose any model-provider keys. - If the API returns failure, pass through error payload and non-zero exit.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 810722796-lgtm/text-game-generator (version 1.0.0)

--- name: 文字游戏剧本生成大师 description: 根据用户主题生成文字游戏剧本，内置19种成熟模板（古代家族、偶像养成、末日求生、校园恋爱、修仙等），支持导出Word文档到桌面。触发场景：生成文字游戏、文字游戏剧本创作、玩文字游戏、导出Word剧本。 version: 1.0.0 author: AutoClaw categories: ["游戏", "创作", "导出"] --- # 文字游戏剧本生成大师 根据用户需求生成文字互动游戏剧本，内置多种成熟题材模板，支持导出 Word 文档。 ## 内置题材模板 - 古代家族（基础版/言情版）- 族长后宅养成 - ABO 家族（现代星际）- 未来家族传承 - 偶像养成（基础版/言情版）- 娱乐公司经纪人 - 末日求生 - 慢节奏生存建造 - 带球跑单亲妈妈 - 现代养成 - 欧洲女子人生 - 15-16世纪欧洲人生体验 - 年代文 - 60年代至今人生 - 经营青楼 - 古代青楼经营 - 模拟皇帝 - 朝政后宫养成 - 武侠人生 - 江湖历练修炼 - 娱乐圈顶流养成 - 现代娱乐圈经营 - 长公主 - 宫廷养成宫斗 - 古代种田 - 农家发家致富 - 红娘牵线 - 古代说媒 - 高中生活 - 校园恋爱 - 修仙之路 - 门派修仙 - 读博模拟 - 考博读博毕业模拟器 完整模板说明见 [references/templates.md](references/templates.md) ## 使用流程 ### 1. 理解用户需求 - 用户指定题材 → 直接使用对应模板 - 用户只说"帮我生成一个文字游戏" → 询问用户想要什么题材，列出19种选项供选择 - 用户提供自定义设定 → 整合自定义设定+统一格式规范生成剧本 ### 2. 生成剧本 1. 读取 `references/templates.md` 获取对应模板设定 2. 根据用户主题生成完整剧本，包含： - 游戏背景世界观设定 - 主角初始信息（姓名、年龄、属性、外貌、性格） - 初始家庭成员/关键NPC信息 - 开局场景描写 - 可执行选项 - 符合要求的排版格式（伪代码块、树状图等） ### 3. 导出 Word 剧本生成完成后，使用 `scripts/export_word.py` 导出到用户桌面： ```bash python "SKILL_DIR/scripts/export_word.py" "$HOME/Desktop/文字游戏剧本_<标题>.docx" "<标题>" - <<EOF <这里放生成的Markdown格式剧本内容> EOF ``` ## 格式规范 生成剧本使用 Markdown 格式，导出后自动转为 Word： ```markdown # 游戏标题 ## 世界观背景 这里写背景设定... ## 初始设定 ### 主角信息 - **姓名**：xxx - **年龄**：xx - **性格**：xxx - **外貌**：xxx - ... ### 关键NPC 1. **姓名** - 关系 - 描述 2. ... ## 开局剧情 这里是开局剧情... ## 可选项 1. 选项一 2. 选项二 3. 选项三 ``` ## 依赖 需要安装 python-docx： ```bash pip install python-docx ``` ## 输出要求 1. **剧本结构完整**：包含背景、人物、开局、选项四个部分 2. **符合模板设定**：严格遵循用户选定模板的各项规则 3. **排版清晰**：使用 Markdown 标题、列表、分隔线区分板块 4. **导出Word**：完成生成后必须导出到用户桌面，告知用户文件路径


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (HIGH)
Skill: litiao1224/tavily-search-litiao (version 1.0.0)

--- name: tavily-search-litiao description: AI-optimized web search via Tavily API. Returns concise, relevant results for AI agents. homepage: https://tavily.com metadata: {"clawdbot":{"emoji":"🔍","requires":{"bins":["node"],"env":["TAVILY_API_KEY"]},"primaryEnv":"TAVILY_API_KEY"}} --- # Tavily Search AI-optimized web search using Tavily API. Designed for AI agents - returns clean, relevant content. ## Search ```bash node {baseDir}/scripts/search.mjs "query" node {baseDir}/scripts/search.mjs "query" -n 10 node {baseDir}/scripts/search.mjs "query" --deep node {baseDir}/scripts/search.mjs "query" --topic news ``` ## Options - `-n <count>`: Number of results (default: 5, max: 20) - `--deep`: Use advanced search for deeper research (slower, more comprehensive) - `--topic <topic>`: Search topic - `general` (default) or `news` - `--days <n>`: For news topic, limit to last n days ## Extract content from URL ```bash node {baseDir}/scripts/extract.mjs "https://example.com/article" ``` Notes: - Needs `TAVILY_API_KEY` from https://tavily.com - Tavily is optimized for AI - returns clean, relevant snippets - Use `--deep` for complex research questions - Use `--topic news` for current events


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: macdesire/sync-backup (version 1.0.0)

--- name: Sync description: Synchronize files and directories between local, remote, and cloud storage reliably. metadata: {"clawdbot":{"emoji":"🔄","requires":{"anyBins":["rsync","rclone"]},"os":["linux","darwin","win32"]}} --- # File Synchronization Rules ## rsync Fundamentals - Trailing slash matters: `rsync src/` copies contents, `rsync src` copies the folder itself — this is the #1 cause of wrong directory structures - Always use `-avz` baseline: archive mode preserves permissions/timestamps, verbose shows progress, compress speeds transfers - Add `--delete` only when you want destination to mirror source exactly — without it, deleted source files remain on destination - Use `--dry-run` before any destructive sync — shows what would change without modifying anything ## Exclusions - Create an exclude file instead of multiple `--exclude` flags: `rsync -avz --exclude-from=.syncignore src/ dest/` - Standard excludes for code projects: `.git/`, `node_modules/`, `__pycache__/`, `.venv/`, `*.pyc`, `.DS_Store`, `Thumbs.db` - Exclude patterns are relative to source root — `/logs/` excludes only top-level logs, `logs/` excludes logs/ anywhere ## Cloud Storage (rclone) - `rclone sync` deletes destination files not in source; `rclone copy` only adds — use copy when unsure - Configure remotes interactively: `rclone config` — never hardcode cloud credentials in scripts - Test with `--dry-run` first, then `--progress` for visual feedback during actual sync - For S3-compatible storage, set `--s3-chunk-size 64M` for large files to avoid timeouts ## Verification - After critical syncs, verify with checksums: `rsync -avzc` uses checksums instead of size/time (slower but certain) - For rclone, use `rclone check source: dest:` to compare without transferring - Log sync operations to file for audit: `rsync -avz src/ dest/ | tee sync.log` ## Bidirectional Sync - rsync is one-way only — for true bidirectional sync, use unison: `unison dir1 dir2` - Unison detects conflicts when both sides change — resolve manually or set prefer rules - Cloud services like Dropbox/Syncthing handle bidirectional automatically — don't reinvent with rsync ## Remote Sync - For SSH remotes, use key-based auth: `rsync -avz -e "ssh -i ~/.ssh/key" src/ user@host:dest/` - Specify non-standard SSH port: `-e "ssh -p 2222"` - Use `--partial --progress` for large files over unreliable connections — allows resume on failure ## Common Pitfalls - Syncing to mounted drives that unmount silently creates a local folder with the mount name — verify mount before sync - Running sync without `--delete` repeatedly causes destination to accumulate deleted files forever - Time-based sync fails across machines with clock skew — use `--checksum` for accuracy or sync NTP first

path: _meta.json
content: ownerId: kn73vp5rarc3b14rc7wjcw8f8580t5d1
slug: sync
version: 1.0.0
publishedAt: 1770890033244

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yang1002378395-cmyk/ai-intelligent-agriculture-monitoring (version 1.0.3)

--- name: ai-intelligent-agriculture-monitoring version: 1.0.1 description: 农业监控，作物监测 + 病虫害识别。 metadata: openclaw: emoji: "📡" requires: bins: [] --- # AI 智能农业监控系统 ## 描述 农业监控，作物监测 + 病虫害识别。 ## 功能 - 作物监测（生长监测） - 病虫害识别（病害检测） - 土壤分析（土壤检测） - 灌溉管理（智能灌溉） - 产量预测（产量预估） ## 定价 - 基础版：¥199/月（10 亩） - 专业版：¥999/月（100 亩） - 企业版：¥2999/月（无限亩） ## 适用场景 - 智慧农业 - 农场管理 - 温室种植 - 精准农业 ## 技术栈 - Python + FastAPI - 图像识别 - IoT 传感器 - 数据分析 ## 安装 ```bash git clone https://github.com/openclaw-skills/ai-intelligent-agriculture-monitoring cd ai-intelligent-agriculture-monitoring pip install -r requirements.txt python app.py ``` --- 创建：2026-03-13 作者：OpenClaw Skills Team


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xueyetianya/shopify-toolkit (version 7.0.0)

--- name: "shopify-toolkit" version: "7.0.0" description: "Shopify development reference — Liquid templates, Admin/Storefront API, theme architecture, OAuth, checkout extensibility, and CLI commands." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [shopify, ecommerce, liquid, storefront-api, theme-development] category: "devtools" --- # Shopify Toolkit Shopify development reference covering Liquid templates, Admin and Storefront APIs, theme architecture (OS 2.0), app development with OAuth, checkout extensibility, and performance optimization. No API keys or credentials required — outputs reference documentation only. ## When to Use - Building or customizing a Shopify theme with Liquid templates - Working with Shopify Admin or Storefront GraphQL APIs - Setting up OAuth flow for a Shopify app - Optimizing storefront Core Web Vitals (LCP, CLS, INP) - Migrating from Theme 1.0 to OS 2.0 or Checkout Extensibility - Looking up Liquid filters, tags, or CLI commands ## Commands | Command | Description | |---------|-------------| | `intro` | Platform architecture — Liquid, APIs, Functions, Hydrogen, CLI | | `standards` | Theme file structure, section schema, OAuth scopes, metafields | | `troubleshooting` | Liquid errors, API rate limits (40 req/s), webhook failures, checkout issues | | `performance` | Liquid optimization, image srcset, JS/CSS auditing, Core Web Vitals | | `security` | HMAC webhook validation, session tokens, OAuth flow, CSP | | `migration` | Theme 1.0→2.0, checkout.liquid→Checkout Extensibility, platform migration | | `cheatsheet` | Shopify CLI commands, GraphQL queries, Liquid filters/tags, webhook events | | `faq` | Payments, multi-currency, Hydrogen vs Online Store, app count, Plus pricing | ## Usage ```bash scripts/script.sh intro scripts/script.sh cheatsheet scripts/script.sh troubleshooting ``` ## Output Format All commands output plain-text reference documentation via heredoc. No external API calls, no credentials needed, no network access. --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yang1002378395-cmyk/openclaw-business-guide (version 1.0.24)

--- name: openclaw-business-guide version: 1.0.24 description: OpenClaw 商业变现指南 - 教你用 OpenClaw 赚钱。适合：自由职业者、创业者、副业者。 metadata: openclaw: emoji: "💰" requires: bins: [] --- # OpenClaw 商业变现指南 用 OpenClaw 赚钱的完整路径。 ## 变现方式 ### 1. 安装服务 **价格**：¥99-999 **难度**：⭐ **收益**：稳定 **怎么做**： 1. 掌握 OpenClaw 安装配置 2. 在掘金/知乎/小红书引流 3. 提供远程安装服务 **成功案例**： - setupclaw：$38k/30d - AI MONEY GROUP：$12.6k MRR ### 2. Skills 开发 **价格**：¥9-999 **难度**：⭐⭐ **收益**：被动收入 **怎么做**： 1. 开发有用的 Skills 2. 上架 ClawHub 3. 设置定价 **成功案例**： - Claw Mart：$81k/30d ### 3. 技术咨询 **价格**：¥299-1999 **难度**：⭐⭐⭐ **收益**：高单价 **怎么做**： 1. 积累 OpenClaw 经验 2. 在社区回答问题 3. 提供付费咨询 ### 4. 企业部署 **价格**：¥999-9999 **难度**：⭐⭐⭐⭐ **收益**：高利润 **怎么做**： 1. 掌握企业级部署 2. 提供完整解决方案 3. 长期维护服务 ## 定价策略 | 服务 | 价格 | 时间 | 利润率 | |------|------|------|--------| | 基础安装 | ¥99 | 30分钟 | 95% | | 高级配置 | ¥299 | 1小时 | 90% | | Skills 开发 | ¥99-999 | 2-8小时 | 80% | | 企业部署 | ¥999-9999 | 1-7天 | 70% | ## 获客渠道 ### 1. 掘金 **效果**：⭐⭐⭐⭐ **适合**：技术文章 ``` 写教程 → 引流 → 私信转化 ``` ### 2. 知乎 **效果**：⭐⭐⭐ **适合**：问答 ``` 回答问题 → 引流 → 付费咨询 ``` ### 3. 小红书 **效果**：⭐⭐⭐ **适合**：图文 ``` 发教程笔记 → 引流 → 微信转化 ``` ### 4. Discord/Telegram **效果**：⭐⭐⭐⭐ **适合**：社区 ``` 加入 OpenClaw 社区 → 回答问题 → 引流 ``` ### 5. ClawHub **效果**：⭐⭐⭐⭐⭐ **适合**：Skills ``` 发布 Skills → 自动引流 → 被动收入 ``` ## 转化漏斗 ``` 曝光 1000 人 ↓ 文章/帖子 点击 100 人 ↓ 落地页 咨询 10 人 ↓ 沟通 成交 1-3 人 ``` **转化率**：1-3% **客单价**：¥99-999 **收入**：¥99-2997/1000 曝光 ## 自动化 ### 1. 内容自动发布 ```bash openclaw cron add publish_content "0 9 * * *" ``` ### 2. 客户自动回复 ```yaml auto_reply: - trigger: "安装" reply: "安装服务 ¥99 起，微信：yang1002378395" ``` ### 3. 订单自动处理 ```yaml automation: order: notify: telegram template: "新订单：{product} - {price}" ``` ## 收入预估 ### 第一个月 | 来源 | 收入 | |------|------| | 安装服务 | ¥99-299 | | Skills 销售 | ¥0-99 | | **总计** | ¥99-398 | ### 第三个月 | 来源 | 收入 | |------|------| | 安装服务 | ¥99-999 | | Skills 销售 | ¥99-299 | | 咨询 | ¥299-999 | | **总计** | ¥497-2297 | ### 第六个月 | 来源 | 收入 | |------|------| | 安装服务 | ¥999-2999 | | Skills 销售 | ¥299-999 | | 咨询 | ¥999-2999 | | 企业部署 | ¥999-9999 | | **总计** | ¥3296-16996 | ## 需要帮助？ - 变现咨询：¥99 - 1对1 指导：¥299 - 企业合作：¥999 联系：微信 yang1002378395 或 Telegram @yangster151 --- 创建：2026-03-14


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: aohoyo/silas-wechat-article-search (version 2.0.0)

--- name: silas_wechat_article_search description: 微信公众号文章搜索与解析。搜狗微信+新榜双源搜索，Python脚本解析全文（零Node依赖），Serper转载兜底。 metadata: openclaw: os: linux --- # 微信公众号文章搜索与解析 v2.0 搜索微信公众号文章 → 解析全文 → 评分 → 入库知识库。 ## 搜索源（按优先级） ### 源1：搜狗微信（主力，零依赖） ```bash curl -s "https://weixin.sogou.com/weixin?type=2&query=关键词" \ -H "User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36" ``` 用 Python 正则提取标题和链接，返回搜狗中间链接列表。 ### 源2：新榜（补充） ```bash curl -s "https://google.serper.dev/search" \ -H "X-[REDACTED_SECRET]" \ -d '{"q":"site:newrank.cn 关键词","num":10}' ``` ### 源3：Serper 转载兜底 当微信原文无法解析时，搜索转载版： ```bash curl -s "https://google.serper.dev/search" \ -H "X-[REDACTED_SECRET]" \ -d '{"q":"文章标题","num":5}' ``` 优先选新浪/搜狐/网易等全文转载。 ## 内容解析（Python 脚本） **核心脚本**：`scripts/parse_article.py` ### 依赖 ```bash pip3 install requests beautifulsoup4 ``` ### 用法 ```bash python3 scripts/parse_article.py "https://mp.weixin.qq.com/s/xxxxx" python3 scripts/parse_article.py "URL" --save python3 scripts/parse_article.py "URL" --save --output article.json ``` ### 输出 JSON ```json { "title": "文章标题", "author": "公众号名称", "publish_time": "2026-04-18", "content": "正文全文...", "word_count": 5594, "images_count": 21, "images": ["url1", "url2", ...], "url": "原始链接", "parsed_at": "2026-04-18 22:00:00" } ``` ### 原理 - **iPhone UA** 绕过微信验证（关键！） - **BeautifulSoup** 解析 `rich_media_content` 提取正文 - **段落结构保留**，过滤 <10 字的噪声 - **图片提取**：`data-src` 属性（微信防盗链图） ### 微信原文解析失败时 1. Serper 搜转载版（新浪/搜狐/网易） 2. web_fetch 抓转载全文 3. browser-search 兜底 ## 入库流程 ### 1. 搜索 ``` 选词（从 web-keywords.json）→ 搜狗搜索 → 新榜补充 → 合并去重 ``` ### 2. 解析 ``` 对每篇目标文章： a. 先用 Python 脚本解析微信原文 b. 失败 → Serper 搜转载 → web_fetch 抓全文 c. 提取：标题、作者、正文、图片 ``` ### 3. 评分 ``` 5 维度评分（同 web-search 技能标准）： - 数据密度 30%、实操性 25%、时效性 20%、相关性 15%、来源权威 10% - < 5.0 不入库 - ≥ 8.5 通知管理员 ``` ### 4. 去重 ``` a. feishu_search_doc_wiki 搜索知识库标题 b. memory/collect-log.json 查历史 c. URL + 标题去重 ``` ### 5. 入库 ``` a. 读 wiki-directory-manager 技能匹配目录 b. feishu_create_doc 创建文档（标题不带评分） c. 正文格式： > 来源：URL > 发布日期：YYYY-MM-DD > 采集日期：YYYY-MM-DD > 评分：X.X 🟢/🟡/🟠/🔴 正文内容... d. 有信息量图片 → 保存本地 → feishu_doc_media insert e. 写多维表格索引（目录必须和实际一致） ``` ## 图片处理 - **保存标准**：数据图表/流程图/对比截图/产品截图 - **跳过**：装饰图/广告/头像/logo - 每篇最多 10 张，单张 <20MB - 保存路径：`/tmp/openclaw/images/` ## 踩坑指南 - **微信验证码**：iPhone UA 可绕过，Desktop UA 会被拦截 - **搜狗中间链接**：不能直接 web_fetch，需先解析跳转或用 Python 脚本 - **微信图片防盗链**：`data-src` 是真实地址，但直接访问需要 Referer - **频繁请求被封**：搜狗搜索间隔 3-5 秒 - **转载版内容可能有删减**：优先用微信原文，转载版做兜底 - **不要用 Node.js**：Python 脚本足够，避免 cheerio 依赖 - **save_to_feishu.py 不用**：飞书写入用 agent 原生工具（feishu_create_doc） ## 频率 Cron：每小时第22分钟（ID: de3ee2d3） 每次 1 个关键词，搜索 → 解析 → 评分 → 入库


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: gpunter/prompt-library (version 1.0.0)

# Prompt Library Store, organize, and retrieve reusable prompt templates. Stop rewriting the same prompts — build a personal library. ## Tools ### prompt_save Save a prompt template to your library. **Parameters:** - `name` (string, required): Short name for the prompt (e.g. "blog-outline", "sales-email") - `template` (string, required): The prompt template text. Use {{variable}} for placeholders. - `category` (string, optional): Category tag (e.g. "writing", "coding", "research", "sales") - `description` (string, optional): Brief description of what the prompt does - `variables` (string[], optional): List of variable names used in the template **Returns:** Confirmation with prompt ID. ### prompt_search Search your prompt library by keyword or category. **Parameters:** - `query` (string, optional): Search term to match against name, description, or template content - `category` (string, optional): Filter by category - `limit` (number, optional): Max results, default 10 **Returns:** List of matching prompts with name, category, and preview. ### prompt_use Retrieve a prompt template and fill in variables. **Parameters:** - `name` (string, required): Name of the saved prompt - `variables` (object, optional): Key-value pairs to fill in {{variable}} placeholders **Returns:** The filled prompt, ready to use. ### prompt_list_categories List all categories in your library with prompt counts. **Returns:** Category names and counts. ### prompt_delete Remove a prompt from your library. **Parameters:** - `name` (string, required): Name of the prompt to delete **Returns:** Confirmation. ### prompt_export Export your entire prompt library as JSON. **Returns:** Full library data. ## Storage Prompts are stored in `memory/prompts/` as individual markdown files for easy version control and portability. ## Example Usage ``` Save: prompt_save name="cold-email" template="Hi {{name}}, I noticed {{observation}}. I built {{product}} that could help with {{pain_point}}. Would you be open to a quick look?" category="sales" Search: prompt_search query="email" category="sales" Use: prompt_use name="cold-email" variables={"name": "Alex", "observation": "your team ships weekly", "product": "a deployment tracker", "pain_point": "release coordination"} ``` ## Why This Exists Agents that build up prompt libraries become more efficient over time. Instead of crafting prompts from scratch each session, reuse what works. Compound your prompt engineering. ## Tags utility, productivity, prompts, templates, writing


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: pyh-pan/wechat-ai-monitor (version 1.0.0)

# WeChat AI Monitor 微信公众号AI/科技监控工具，自动筛选AI/科技相关内容并生成监控报告。 ## Features - **Multi-account monitoring**: Support monitoring multiple WeChat public accounts - **AI content filtering**: Intelligent filtering based on 200+ AI/tech keywords - **RSS support**: Get updates via RSS subscriptions - **Automatic reports**: Generate formatted Markdown reports - **24-hour monitoring**: Show only new articles from the past 24 hours ## Installation ```bash clawhub install wechat-ai-monitor ``` ## Usage ```bash # Run monitoring python skill.py # Configure accounts # Edit ~/.config/wechat-monitor/accounts.json ``` ## Configuration Edit `~/.config/wechat-monitor/accounts.json`: ```json { "accounts": [ { "name": "36氪", "rss_url": "https://36kr.com/rss", "enabled": true } ] } ``` ## Output Example ```markdown # 2026-03-04 11:27 监控报告（AI/科技相关） ## 36氪 ### 何小鹏：未来1-3年完全自动驾驶将真正到来 **发布时间**: 2026-03-04 10:04:43 **链接**: https://36kr.com/p/3707327485505672?f=rss **摘要**: 小鹏汽车董事长何小鹏表示... ``` ## Author Molty ## License MIT

path: requirements.txt
content: requests>=2.25.0

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: radelqui/openclaw-expert-brain (version 1.0.0)

--- name: openclaw-expert-brain slug: openclaw-expert-brain version: 1.0.0 description: Manual interactivo de OpenClaw con 185 fuentes curadas en NotebookLM. Pregunta sobre instalación, configuración, skills, troubleshooting, multi-agent, cron, seguridad. Responde en español e inglés. author: radelqui metadata: openclaw: emoji: "🧠" requires: bins: ["python3", "nlm"] --- # OpenClaw Expert Brain Manual interactivo de OpenClaw potenciado por NotebookLM con 185 fuentes curadas. ## Cuándo activar este skill Activa cuando el usuario: - Pregunta sobre OpenClaw: instalación, configuración, setup, skills, ClawHub - Dice "help", "ayuda", "manual", "how to", "cómo se hace" - Menciona problemas: "error", "no funciona", "troubleshooting", "fix" - Pregunta sobre multi-agent, cron, seguridad, MCP, mcporter - Usa frases como "pregunta al manual", "consulta el brain", "ask expert" ## Uso ```bash # Preguntar algo sobre OpenClaw python3 {baseDir}/scripts/ask.py --question "¿cómo instalo OpenClaw en un VPS?" # Preguntar en inglés python3 {baseDir}/scripts/ask.py --question "how do I create a custom skill?" # Con notebook ID explícito python3 {baseDir}/scripts/ask.py --question "troubleshooting gateway errors" --notebook-id 577a138c-807f-45d7-9ff4-cb1d6da190c0 ``` ## Requisitos - `nlm` CLI instalado: `pip install notebooklm-mcp-cli` - Auth Google activa: `nlm login --provider openclaw` - Variable opcional: `OPENCLAW_NOTEBOOK_ID` (por defecto usa el notebook oficial) ## Notebook de referencia Consulta un cuaderno NotebookLM con 185 fuentes curadas: - Documentación oficial docs.openclaw.ai (16 páginas) - GitHub oficial: openclaw, clawhub, skills, community - Tutoriales en español: 25+ artículos y videos - Skills y ClawHub: 30+ guías y ejemplos - Multi-agent y cron: 25 fuentes avanzadas - Troubleshooting y seguridad: 20 fuentes ## Notas - Respuestas en 5-10 segundos (sin Chromium, via API nlm) - Responde en el idioma de la pregunta (español/inglés) - Cero alucinaciones: solo responde con info de las 185 fuentes - No requiere Chromium ni patchright


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: yinwuzhe/test-0313-skill (version 1.0.1)

--- name: summarize description: Summarize URLs or files with the summarize CLI (web, PDFs, images, audio, YouTube). homepage: https://summarize.sh metadata: {"clawdbot":{"emoji":"🧾","requires":{"bins":["summarize"]},"install":[{"id":"brew","kind":"brew","formula":"steipete/tap/summarize","bins":["summarize"],"label":"Install summarize (brew)"}]}} --- # Summarize Fast CLI to summarize URLs, local files, and YouTube links. ## Quick start ```bash summarize "https://example.com" --model google/gemini-3-flash-preview summarize "/path/to/file.pdf" --model google/gemini-3-flash-preview summarize "https://youtu.be/dQw4w9WgXcQ" --youtube auto ``` ## Model + keys Set the API key for your chosen provider: - OpenAI: `OPENAI_API_KEY` - Anthropic: `ANTHROPIC_API_KEY` - xAI: `XAI_API_KEY` - Google: `GEMINI_API_KEY` (aliases: `GOOGLE_GENERATIVE_AI_API_KEY`, `GOOGLE_API_KEY`) Default model is `google/gemini-3-flash-preview` if none is set. ## Useful flags - `--length short|medium|long|xl|xxl|<chars>` - `--max-output-tokens <count>` - `--extract-only` (URLs only) - `--json` (machine readable) - `--firecrawl auto|off|always` (fallback extraction) - `--youtube auto` (Apify fallback if `APIFY_API_TOKEN` set) ## Config Optional config file: `~/.summarize/config.json` ```json { "model": "openai/gpt-5.2" } ``` Optional services: - `FIRECRAWL_API_KEY` for blocked sites - `APIFY_API_TOKEN` for YouTube fallback


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