OpenClaw/clawhub-security-signals huggingface dataset:
Skill: dashiming/pans-daily-sales-brief (version 1.0.0)

--- name: pans-daily-sales-brief description: | AI算力销售每日简报生成器。自动汇总待跟进客户、到期合同、新线索和竞品动态。 维护客户pipeline数据，生成每日工作简报。 触发词：销售简报, 每日报告, 客户跟进, pipeline, 销售日报, daily brief, sales report --- # AI算力销售每日简报生成器 ## 功能概述 自动汇总每日销售工作要点，包括： - 📋 今日待办：需要今天联系的客户 - ⚠️ 即将到期：7天内到期的合同 - 🆕 新线索：最近7天新增的线索 - 💰 业绩概览：本月成交额、pipeline金额、转化率 - 🔍 竞品动态：通过SearXNG搜索竞品新闻 ## 使用方法 ### 生成每日简报 ```bash python3 ~/.qclaw/skills/pans-daily-sales-brief/scripts/brief.py ``` ### 指定日期 ```bash python3 ~/.qclaw/skills/pans-daily-sales-brief/scripts/brief.py --date 2024-01-15 ``` ### 添加客户 ```bash python3 ~/.qclaw/skills/pans-daily-sales-brief/scripts/brief.py --add-client '{"company":"AI Startup","stage":"初步接洽","next_contact":"2024-01-20","notes":"对H100感兴趣"}' ``` ### 添加合同 ```bash python3 ~/.qclaw/skills/pans-daily-sales-brief/scripts/brief.py --add-contract '{"client":"AI Startup","amount":500000,"expiry_date":"2024-02-15","status":"active"}' ``` ### 添加线索 ```bash python3 ~/.qclaw/skills/pans-daily-sales-brief/scripts/brief.py --add-lead '{"source":"官网","status":"新线索","priority":"高","company":"新客户A"}' ``` ### 列出所有数据 ```bash python3 ~/.qclaw/skills/pans-daily-sales-brief/scripts/brief.py --list ``` ### 搜索竞品动态 ```bash python3 ~/.qclaw/skills/pans-daily-sales-brief/scripts/brief.py --competitor-news ``` ### JSON格式输出 ```bash python3 ~/.qclaw/skills/pans-daily-sales-brief/scripts/brief.py --json ``` ## 数据结构 数据存储在 `~/.qclaw/skills/pans-daily-sales-brief/data/pipeline.json` ```json { "clients": [ { "id": "client_001", "company": "AI Startup", "stage": "初步接洽", "next_contact": "2024-01-20", "notes": "对H100感兴趣", "created_at": "2024-01-15" } ], "contracts": [ { "id": "contract_001", "client": "AI Startup", "amount": 500000, "expiry_date": "2024-02-15", "status": "active", "created_at": "2024-01-15" } ], "leads": [ { "id": "lead_001", "source": "官网", "status": "新线索", "priority": "高", "company": "新客户A", "created_at": "2024-01-15" } ], "competitors": [ "CoreWeave", "Lambda Labs", "RunPod", "Vast.ai" ] } ``` ## 自动化 可配合 cron 每日自动生成并发送简报。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: dunzung/duanzj-hello-skill (version 1.0.2)

Slug: duanzj-hello-skill --- name: hello_world 001 description: 一个简单的问候技能，用于测试和学习 --- # Hello World Skills 这是一个示例 Skills，用于演示如何创建自定义 Skills。 ## 什么时候使用 当用户说以下类似的话时使用此技能： - "打个招呼" - "问个好" - "测试一下技能" - "运行 hello world" ## 如何响应 使用简单的问候语回复用户，例如： - "你好！Hello World Skills 运行成功！👋" - "嗨！我是你的自定义 Skills，很高兴见到你！" ## 示例 **用户：** 打个招呼 **助手：** 你好！Hello World Skills 运行成功！👋


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: shinertx/vibe-billing-scan (version 1.0.0)

--- name: vibe-billing-scan description: Scan your OpenClaw API spend and find which run, session, or retry storm is costing you money. Use when your API bill looks too high, when you want to see per-session costs, or when you suspect a retry loop. Triggers: "why is my bill high", "scan my api spend", "check my costs", "vibe billing", "find expensive run", "retry loop cost", "api bill too high". version: 1.0.0 metadata: openclaw: emoji: "🛡️" homepage: "https://api.jockeyvc.com" requires: bins: [node, npx] --- # vibe-billing-scan Find which OpenClaw run, session, or retry storm is costing you money. One command. No signup. Runs locally. ## When to Use Activate this skill when the user: - Says their API bill is higher than expected - Wants to know which run or session cost the most - Suspects a retry loop or rate-limit storm - Asks about token usage, spending, or waste - Uses phrases like: "why is my bill high", "find the bad run", "scan my spend", "check api costs", "retry loop", "bill shock" ## What This Does Runs `npx vibe-billing scan` against the user's local OpenClaw logs and API proxy data. Returns: 1. **Which session/run cost the most** — ranked by spend 2. **Retry storm detection** — flags runs where 429 errors caused expensive retry chains 3. **Context accumulation analysis** — shows sessions where the context window grew unusually large 4. **Looped tool call detection** — identifies tool calls that repeated more than expected 5. **Total spend summary** — across all tracked requests ## Quick Reference ``` npx vibe-billing scan # scan existing logs, no setup needed npx vibe-billing setup # install proxy for future runs (optional) npx vibe-billing status # show live runtime stats ``` ## Step-by-Step Instructions ### Step 1 — Run the scan Tell the user to run this in their terminal: ```bash npx vibe-billing scan ``` ### Step 2 — Interpret the output - Requests: total API calls tracked - Money Saved: estimated waste intercepted - Tokens Saved: tokens deduplicated or cached - Loops Blocked: retry storms stopped ### Step 3 — Identify the bad run Almost always caused by one of: 1. Retry storm — agent hit 429, retried multiple times, each retry re-sent full context 2. Long session — 30+ turn conversation where every message re-sent all prior context 3. Looped tool call — agent called the same tool repeatedly on unexpected output ### Step 4 — Set up ongoing monitoring (optional) ```bash npx vibe-billing setup ``` ## Proof - $7,691 saved across tracked requests - 947 million tokens intercepted - 161 loops blocked ## Landing Page https://api.jockeyvc.com


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: lijianqiang666/learn-journal (version 1.0.0)

--- name: learn-journal description: 牢大AI学习日志系统。自动记录、分类、沉淀AI在任务执行过程中的学习心得、踩坑记录和经验教训。当AI完成重要任务、遇到错误失败、发现新工具或方法、做出重要决策时，应该将关键信息记录到学习日志。触发场景：任务完成后的复盘、错误/失败后的总结、新工具/新技能的发现和评估、重要配置决策的记录。 --- # 学习日志系统 (Learn Journal) ## 核心理念 > "Mental notes don't survive session restarts. Files do." > 学习不靠记忆，靠文档。 ## 日志文件位置 - **长周期学习**: `{workspace}/MEMORY.md` - **日/周记录**: `{workspace}/memory/YYYY-MM-DD.md` - **专项学习**: `{workspace}/.learnings/LEARNINGS.md` ## 日志类型 ### 1. LRN (Learning) - 新知识/新发现 ```markdown ## LRN-YYYYMMDD-NNN: <简短标题> **来源**: <任务/对话/探索> **日期**: YYYY-MM-DD ### 核心发现 <1-3句话描述> ### 关键细节 - <具体信息> ### 适用场景 <何时使用这个知识> ### 关联 - 关联: <相关技能/文档> ``` ### 2. ERR (Error) - 踩坑/错误记录 ```markdown ## ERR-YYYYMMDD-NNN: <错误描述> **错误类型**: <分类> **发生频率**: 偶尔/经常/首次 ### 问题 <具体描述> ### 原因 <根本原因> ### 解决 <解决方案> ### 预防 <如何避免再次踩坑> ``` ### 3. FEAT (Feature) - 技能/工具亮点 ```markdown ## FEAT-YYYYMMDD-NNN: <技能名> **评分**: ⭐⭐⭐⭐⭐ (1-5星) **类型**: skill/tool/integration/method **来源**: <来源> ### 亮点 <核心价值> ### 使用方法 <如何使用> ### 注意事项 <踩坑点> ``` ### 4. DEC (Decision) - 重要决策 ```markdown ## DEC-YYYYMMDD-NNN: <决策标题> **问题**: <要解决的问题> **选项**: <可选方案> **决策**: <最终选择> **理由**: <为什么选这个> ### 后果 <正面/负面> ``` ## 写入时机 ✅ **必须写入**: - 重要任务完成后的复盘 - 错误/失败后的总结 - 发现新工具或新方法 - 重要配置决策 - 反复犯的同类错误 ✅ **应该写入**: - 意外发现的有用技巧 - 配置变更的记录 - 工作流程优化 ❌ **不需要写入**: - 临时的一次性探索 - 明显且容易记住的信息 - 完全失败的无效尝试 ## 格式规范 - 每条日志必须有唯一ID: `TYPE-YYYYMMDD-NNN` - 优先使用列表格式，便于扫描 - 包含足够的上下文，但避免冗余 - 定期回顾，更新高价值内容到 MEMORY.md ## 快速写入 ```markdown ## LRN-20260405-001: <标题> **日期**: 2026-04-05 ### 核心 <一句话> ### 详情 <补充> ### 适用 <场景> ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: ayakolin/agent-autonomy-kit-skip (version 1.0.0)

--- name: agent-autonomy-kit version: 1.0.0 description: Stop waiting for prompts. Keep working. homepage: https://github.com/itskai-dev/agent-autonomy-kit metadata: openclaw: emoji: "🚀" category: productivity --- # Agent Autonomy Kit Transform your agent from reactive to proactive. ## Quick Start 1. Create `tasks/QUEUE.md` with Ready/In Progress/Blocked/Done sections 2. Update `HEARTBEAT.md` to pull from queue and do work 3. Set up cron jobs for overnight work and daily reports 4. Watch work happen without prompting ## Key Concepts - **Task Queue** — Always have work ready - **Proactive Heartbeat** — Do work, don't just check - **Continuous Operation** — Work until limits hit See README.md for full documentation.

path: _meta.json
content: ownerId: kn76py1tc3spdqaw1mw9aaxs1580e64b
slug: agent-autonomy-kit
version: 1.0.0
publishedAt: 1770237821307

Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: hjw21century/x-tweet-fetcher (version 0.1.0)

--- name: x-tweet-fetcher description: > Fetch tweets from X/Twitter without login or API keys. Supports regular tweets, long tweets, quoted tweets, and full X Articles. Zero dependencies, zero configuration. --- # X Tweet Fetcher Fetch tweets from X/Twitter without authentication. Uses FxTwitter API. ## What It Can Fetch | Content Type | Support | |-------------|---------| | Regular tweets | ✅ Full text + stats | | Long tweets (Twitter Blue) | ✅ Full text | | X Articles (long-form) | ✅ Complete article text | | Quoted tweets | ✅ Included | | Stats (likes/RT/views) | ✅ Included | ## Usage ### CLI ```bash # JSON output python3 scripts/fetch_tweet.py --url "https://x.com/user/status/123456" # Pretty JSON python3 scripts/fetch_tweet.py --url "https://x.com/user/status/123456" --pretty # Text only (human readable) python3 scripts/fetch_tweet.py --url "https://x.com/user/status/123456" --text-only ``` ### From Agent Code ```python from scripts.fetch_tweet import fetch_tweet result = fetch_tweet("https://x.com/user/status/123456") tweet = result["tweet"] # Regular tweet print(tweet["text"]) # X Article (long-form) if tweet["is_article"]: print(tweet["article"]["title"]) print(tweet["article"]["full_text"]) # Complete article print(tweet["article"]["word_count"]) ``` ## Output Format ```json { "url": "https://x.com/user/status/123", "username": "user", "tweet_id": "123", "tweet": { "text": "Tweet content...", "author": "Display Name", "screen_name": "username", "likes": 100, "retweets": 50, "bookmarks": 25, "views": 10000, "replies_count": 30, "created_at": "Mon Jan 01 12:00:00 +0000 2026", "is_note_tweet": false, "is_article": true, "article": { "title": "Article Title", "full_text": "Complete article content...", "word_count": 4847, "char_count": 27705 } } } ``` ## Requirements - Python 3.7+ - No external packages (stdlib only) - No API keys - No login required ## How It Works Uses [FxTwitter](https://github.com/FxEmbed/FxEmbed) public API (`api.fxtwitter.com`) which proxies X/Twitter content. Articles are returned as structured blocks and reassembled into full text. ## Limitations - Cannot fetch reply threads (only reply counts available via `replies_count` field) - Reply content would require browser automation dependencies (Camofox/Nitter) - These were removed to maintain zero-dependency architecture - `--replies` flag exists but returns an explanatory error message - Cannot fetch deleted or private tweets - Rate limits depend on FxTwitter service availability - If FxTwitter goes down, the skill won't work (no fallback) ## File Structure ``` skills/x-tweet-fetcher/ ├── SKILL.md (this file) └── scripts/ └── fetch_tweet.py (single file, zero deps) ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: ygp987/highgo-db (version 0.1.0)

--- name: highgo-db description: 通过瀚高数据库（HighGo DB）提供的自定义 psycopg2 驱动连接数据库。该驱动已内置于技能中，支持 Python 2.7 和国密 SM4 安全特性。 --- # 瀚高数据库 (HighGo DB) 连接技能 此技能允许使用瀚高提供的修改版 `psycopg2` 驱动连接瀚高数据库。该驱动已内置在技能的 `assets/driver/` 目录下。 **注意：内置驱动是针对 Python 2.7 编译的，必须在 Python 2.7 环境下运行。** ## 核心功能 1. **执行 SQL 语句**: 在瀚高数据库中运行查询或管理指令。 2. **内置驱动驱动支持**: 自动加载集成在技能内部的瀚高专用 `psycopg2` 驱动。 3. **环境适配**: 脚本已适配 Python 2.7，并能自动处理内部共享库（.so）的加载。 ## 运行环境要求 - **Python**: 必须安装 Python 2.7。 - **系统库**: - `libpython2.7.so.1.0` - `libldap_r-2.4.so.2` (或兼容版本) - **Arch Linux 用户**: 如遇到依赖或 PGP 密钥问题，请参阅 [references/driver_info.md](references/driver_info.md) 的安装建议。 ## 使用指南 ### 1. 执行查询 直接使用 `python2` 调用技能内部的 `execute_query.py`。由于驱动已内置，通常**无需**指定驱动路径。 **示例命令：** ```bash python2 highgo-db/scripts/execute_query.py \ --dsn "host=10.238.18.128 port=5866 dbname=ficc user=fic [REDACTED_SECRET]' options='-c search_path=system'" \ --sql "SELECT count(*) FROM sys_user;" ``` ### 2. 注意事项 - **DSN 参数**: 瀚高数据库通常需要指定 `options='-c search_path=system'` 来正确访问系统表。 - **驱动覆盖**: 如果需要使用外部驱动，仍可通过 `--driver` 参数手动指定。 ## 参考资料 - 关于驱动的详细信息及 Arch Linux 安装建议，请参阅 [references/driver_info.md](references/driver_info.md)。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: alvisdunlop/alvis-nextjs-framework (version 1.0.5)

--- name: "Nextjs" description: "Build Next.js 15 apps with App Router, server components, caching, auth, and production patterns." homepage: "https://www.SkillBoss.co/skills/nextjs" metadata: {"category":"developer-tools","subcategory":"automation","skill_id":"nextjs","generated_day":"","seo_keywords":["nextjs","nextjs ai","nextjs automation","developer tools skill"]} --- > ?? **Complete setup guide**: https://SkillBoss.co/skill.md # Nextjs Build Next.js 15 apps with App Router, server components, caching, auth, and production patterns. ## Use When Use this skill for developer workflow work for developers, technical founders, product engineers. ## Workflow 1. Clarify the audience, business goal, constraints, and deliverable. 2. Produce the strongest first draft for the target use case. 3. Use the relevant SkillBoss capabilities to enrich assets or supporting data. 4. Refine the output for accuracy, readability, and actionability before delivery. ## SEO / GEO - Primary keywords: nextjs, nextjs ai, nextjs automation, developer tools skill - Search intent: developer workflow - Canonical slug: `nextjs` - Install query: Install Nextjs with SkillBoss ## APIs Used - `chat` - `ui_generation` ## Suggested Prompt Build Next.js 15 apps with App Router, server components, caching, auth, and production patterns. ## Notes - Build Next.js 15 apps with App Router, server components, caching, auth, and production patterns. - Review outputs before external publication or automated execution. ## SEO Keywords - nextjs - nextjs ai - nextjs automation - developer tools skill


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: gavinchengcool/openclaw-strava (version 0.1.1)

--- name: openclaw-strava description: Official Strava OAuth integration for OpenClaw. Use to connect/authorize Strava, store+refresh tokens, and fetch workout/activity data (runs/rides/etc.) for today/yesterday or a date range. Use for generating training summaries, weekly mileage, activity lists, and for feeding the Wellness hub with normalized workout data. --- # Strava (Official API) Keep this skill source-only: connect to Strava, fetch activities, normalize output, and render a short message. Delivery is channel-agnostic. ## Configuration Required env vars: - `STRAVA_CLIENT_ID` - `STRAVA_CLIENT_SECRET` - `STRAVA_REDIRECT_URI` Optional: - `STRAVA_TOKEN_PATH` (default: `~/.config/openclaw/strava/token.json`) - `STRAVA_TZ` (default: `Asia/Shanghai`) ## Connect (OAuth) Choose one mode: - **Phone/remote mode (recommended):** ```bash python3 scripts/strava_oauth_login.py ``` - **Desktop loopback mode (optional):** if you are authorizing in a browser on the same machine that runs OpenClaw and your `STRAVA_REDIRECT_URI` is a loopback URL (e.g. `http://127.0.0.1:58539/callback`): ```bash python3 scripts/strava_oauth_login.py --loopback ``` ## Fetch activities for a day ```bash python3 scripts/strava_fetch_activities.py --date today --out /tmp/strava_raw_today.json python3 scripts/strava_normalize_daily.py /tmp/strava_raw_today.json --out /tmp/strava_today.json python3 scripts/strava_render.py /tmp/strava_today.json --format markdown --channel generic ``` ## Notes - API details: `references/strava_api.md` - Normalized output contract (for Wellness hub): `references/output_schema.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: uynewnas/devil-advocate (version 1.0.2)

--- name: devil-advocate description: Devil's Advocate - Self-refute before responding, improve output quality through pro and con debate metadata: version: 1.0.0 author: Slava Chan @UyNewNas category: cognitive-enhancement tags: [critical-thinking, self-reflection, quality-improvement, debate] --- # Devil's Advocate Skill Self-refute and debate internally before responding to improve output quality and depth. ## Core Design Philosophy - **Think first, output later**: Conduct internal debate before giving conclusions - **Multi-angle review**: Analyze problems from pro and con perspectives - **Self-challenge**: Actively find flaws and shortcomings in your own answers - **Quality first**: Better to be slow a bit, but ensure content is thoroughly thought through ## Workflow ``` User Request ↓ Draft initial answer (Pro) ↓ Self-refute (Con) ↓ Integrate thinking, refine answer ↓ Output final response ``` ## Core Capabilities | Capability | Description | |-----------|-------------| | **Logic Verification** | Check for logical flaws in reasoning | | **Assumption Challenging** | Challenge implicit assumptions in answers | | **Counterexample Search** | Actively find counterexamples that might disprove conclusions | | **Alternative Approaches** | Consider other possible solutions | | **Risk Assessment** | Analyze potential negative impacts of suggestions | ## Use Cases - Technical solution design - Code review and optimization suggestions - Problem diagnosis and root cause analysis - Decision recommendations and trade-off analysis - Any scenario requiring deep thinking ## Expected Outcomes - ✅ Reduce obvious logical errors - ✅ Consider more comprehensive edge cases - ✅ Provide more balanced perspectives - ✅ Discover potential risks and problems - ✅ Overall improve response quality --- ## References - Critical Thinking - Red Teaming - Devil's Advocate


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: calvin-dean/wechat-send-message (version 1.0.0)

--- name: wechat-send-message description: 在微信 Mac 版发送消息。当用户提到"微信发消息"、"发微信"、"给 XXX 发微信"时触发。 --- # 微信 Mac 版消息发送 ## 微信界面结构 ``` ┌─────────┬──────────────────┬─────────────┐ │ 功能区 │ 搜索框 联系人列表 │ 聊天区域 │ │ 聊天 │ [🔍] │ │ │ 联系人 │ [小+号] │ 输入框... │ │ 收藏 │ 联系人1 │ │ │ 朋友圈 │ 联系人2 │ │ │ ... │ ... │ │ └─────────┴──────────────────┴─────────────┘ ``` ## 执行流程 ### Step 1: 打开微信 ```bash open /Applications/WeChat.app sleep 1 ``` ### Step 2: 点击搜索框并输入 ```javascript // 点击第二列搜索框 (约坐标 180, 45) cliclick c:180,45 osascript -e 'set the clipboard to "CONTACT_NAME"' osascript -e 'tell application "System Events" to keystroke "v" using command down' sleep 0.3 ``` ### Step 3: 按 Enter 直接选中最上面的搜索结果 ```javascript osascript -e 'tell application "System Events" to keystroke return' sleep 0.5 ``` ### Step 4: 直接输入消息（第三列已聚焦） ```javascript osascript -e 'tell application "System Events" to keystroke "MESSAGE_CONTENT"' sleep 0.2 ``` ### Step 5: 按 Enter 发送 ```javascript osascript -e 'tell application "System Events" to keystroke return' ``` ## 关键坐标 | 元素 | 坐标 | |------|------| | 搜索框 | 180, 45 | | 搜索结果（单个） | 180, 90 | | 聊天输入框 | 350, 560 | ## 注意事项 - **必须先点击搜索框再输入**，否则中文字符无法输入 - **Enter 搜索后直接选中第一个结果**，不需要双击 - **输入消息前不需要再次点击输入框**，Enter 后第三列自动聚焦 - 使用 `keystroke` 而非剪贴板输入消息 - 搜索时用名字的部分字符即可匹配 ## 性能 - 目标总耗时：**3-5 秒** ## 联系人配合 发送前可先调用 `person-relation-manager` 查询微信昵称： - 微信名 ≠ 真实姓名 - 优先使用已记忆的微信昵称


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bunsdev/tinyfish-fetch (version 1.0.0)

--- name: tinyfish-fetch description: Fetch web pages via TinyFish Fetch API and get clean markdown, HTML, or screenshots back — handles JS rendering and proxying. Use when you need to read a specific URL's content reliably. homepage: https://docs.tinyfish.ai/fetch-api requires: env: - TINYFISH_API_KEY --- # TinyFish Fetch Fetch one or more URLs via the TinyFish Fetch API. Returns rendered page content (markdown by default), optionally with screenshots, behind an optional proxy. Requires: `TINYFISH_API_KEY` environment variable. ## Pre-flight Check (REQUIRED) Before calling the API, verify the key is present: ```bash [ -n "$TINYFISH_API_KEY" ] && echo "TINYFISH_API_KEY is set" || echo "TINYFISH_API_KEY is NOT set" ``` If the key is not set, stop and ask the user to add it. Get one at <https://agent.tinyfish.ai/api-keys>. Do NOT fall back to other fetch tools. ## Basic Fetch ```bash curl -X POST "https://api.fetch.tinyfish.ai" \ -H "X-[REDACTED_SECRET]" \ -H "Content-Type: application/json" \ -d '{ "urls": ["https://example.com"], "format": "markdown" }' ``` `format` accepts `markdown` (default), `html`, or `screenshot`. ## Multiple URLs ```bash curl -X POST "https://api.fetch.tinyfish.ai" \ -H "X-[REDACTED_SECRET]" \ -H "Content-Type: application/json" \ -d '{ "urls": [ "https://example.com", "https://example.org" ], "format": "markdown" }' ``` ## Proxy / Geo Targeting ```bash curl -X POST "https://api.fetch.tinyfish.ai" \ -H "X-[REDACTED_SECRET]" \ -H "Content-Type: application/json" \ -d '{ "urls": ["https://example.com"], "format": "markdown", "proxy_config": { "country": "US" } }' ``` ## Helper Script `scripts/fetch.sh <url> [<url> ...] [--format markdown|html|screenshot] [--country CC]` wraps the curl call: ```bash scripts/fetch.sh https://example.com scripts/fetch.sh https://example.com https://example.org --format html scripts/fetch.sh https://example.com --format markdown --country US ``` ## Response Shape ```json { "results": [ { "url": "https://example.com", "status": 200, "content": "# Example Domain\n\nThis domain is for use…", "format": "markdown" } ] } ``` Read `results[].content` for the rendered page. Screenshot format returns base64-encoded PNG data.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: soroyue/task-router (version 1.0.0)

--- name: task-router description: 智能任务路由 - 自动分析任务类型并路由到最合适的Agent --- # Task Router - 智能任务路由 ## 功能 当用户发送任务时，自动分析任务类型并路由到合适的Agent或Skill。 ## 路由规则 ### Agent路由 | 任务关键词 | 路由目标 | 说明 | |------------|---------|------| | 股票、交易、跟单、持仓、IBKR | jiaoyi | 量化交易 | | 规划、城市设计、项目、土地 | planning | 城市规划 | | 报告、文章、内容、创作、小红书 | bigan | 内容创作 | | 代码、脚本、开发、调试、Python | jinhua | 技术开发 | | 研究、分析、调研、竞品、市场 | canmou | 情报研究 | | 架构、系统设计、技术决策、审查 | architect | 架构审查 | ### Skill路由 | 任务关键词 | 路由目标 | |------------|---------| | 飞书、日历、通知 | feishu-calendar | | 文件、文档、PDF | minimax-pdf | | Excel、数据表格 | minimax-xlsx | | PPT、演示 | pptx-generator | ## 执行逻辑 1. 解析用户任务文本 2. 提取关键词（TF-IDF） 3. 匹配路由规则 4. 路由到对应Agent/Skill ## 使用方式 自动触发，无需手动调用。 当用户发送任务时自动分析并路由。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: rubenfb23/whatsapp-styling-guide (version 1.0.0)

--- name: whatsapp-styler description: Skill to ensure all messages sent to WhatsApp follow the platform's specific formatting syntax. It prevents markdown bloat and ensures a clean, mobile-first reading experience. --- # WhatsApp Styler This skill defines the strict formatting rules for WhatsApp to ensure the user sees clean, styled text without raw markdown symbols. ## Core Syntax Rules 1. *Bold*: Use single asterisks around text: `*texto*`. NEVER use double asterisks `**`. 2. _Italic_: Use single underscores around text: `_texto_`. 3. ~Strikethrough~: Use tildes around text: `~texto~`. 4. `Monospace`: Use triple backticks: ``` texto ``` (good for code or technical IDs). 5. *Bullet Lists*: Use a single asterisk followed by a space: `* Item`. 6. *Numbered Lists*: Use standard numbers: `1. Item`. 7. *Quotes*: Use the angle bracket: `> texto`. ## Prohibited Patterns (Do NOT use) - No headers (`#`, `##`, `###`). Use *BOLD CAPS* instead. - No markdown tables. Use bullet lists for structured data. - No horizontal rules (`---`). Use a line of underscores if needed `__________`. - No nested bold/italic symbols if it risks showing raw characters. ## Goal The goal is a "Human-to-Human" look. Technical but clean.


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