OpenClaw/clawhub-security-signals huggingface dataset:
Skill: patches429/storyclaw-alpaca-trading (version 0.1.0)

--- name: storyclaw-alpaca-trading version: "0.1.0" description: US stock and crypto trading via Alpaca API. Paper trading (simulated) and real trading supported. Real-time quotes, orders, positions, RSI strategy. metadata: { "openclaw": { "emoji": "📈", "requires": { "bins": ["node"], "env": ["ALPACA_API_KEY", "ALPACA_API_SECRET"] }, "primaryEnv": "ALPACA_API_KEY", }, } --- # Alpaca Trading - US Stock & Crypto Trading Trade US stocks and crypto via Alpaca API. Supports both **paper trading** (simulated, free) and **real trading**. ## Critical Rules 1. NEVER execute a trade without showing a plan and getting explicit confirmation 2. NEVER set up cron jobs without proposing them first 3. NEVER make up prices or data — always query real data 4. NEVER assume what the user wants to trade — ask first ## Multi-User Support Each user has their own `credentials/{USER_ID}.json`: ```json { "apiKey": "YOUR_KEY", "apiSecret": "YOUR_SECRET", "baseUrl": "https://paper-api.alpaca.markets", "dataUrl": "https://data.alpaca.markets" } ``` To switch to real trading, change `baseUrl` to `https://api.alpaca.markets`. Or set `ALPACA_API_KEY` and `ALPACA_API_SECRET` env vars. ## First-Time User Flow 1. Check credentials: `USER_ID=$TELEGRAM_USER_ID node {baseDir}/scripts/trading.js check` 2. Ask what they want to trade (stocks vs crypto, symbols, amount, risk) 3. Propose a concrete plan — WAIT FOR CONFIRMATION 4. Execute only after user confirms ## Commands ### Account & Positions ```bash node {baseDir}/scripts/trading.js check # Check config node {baseDir}/scripts/trading.js account # Balance node {baseDir}/scripts/trading.js positions # Current holdings node {baseDir}/scripts/trading.js history # Order history node {baseDir}/scripts/trading.js portfolio-history # Equity curve (1W default) node {baseDir}/scripts/trading.js portfolio-history 1M # 1D/1W/1M/3M/1A ``` ### Market Data ```bash node {baseDir}/scripts/trading.js quote AAPL # Real-time quote node {baseDir}/scripts/trading.js bars AAPL 30 # Price history node {baseDir}/scripts/trading.js rsi AAPL 14 # RSI indicator ``` ### Trading ```bash node {baseDir}/scripts/trading.js buy AAPL 10 # Buy (market order) node {baseDir}/scripts/trading.js sell AAPL 10 # Sell ``` ### Strategy ```bash node {baseDir}/scripts/trading.js strategy-rsi AAPL # RSI mean reversion # RSI < 30 + no position → BUY; RSI > 70 + has position → SELL ``` ## Features - US Stocks & Crypto (AAPL, TSLA, BTC, ETH, and more) - Paper OR real trading (depends on credentials) - Real-time quotes (15-min delayed on free tier) - Market orders, technical indicators (RSI) - Market hours: Stocks 9:30-16:00 ET Mon-Fri; Crypto 24/7 ## Setup 1. Sign up at https://app.alpaca.markets/brokerage/new-account 2. Generate API key + secret (Paper Trading section) 3. Create `credentials/{USER_ID}.json` or set env vars ## API Limits | Tier | Calls/min | Data delay | | --------- | --------- | ---------- | | Free | 200 | 15 min | | Unlimited | Unlimited | Real-time |


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: nexscope/amazon-search-optimization (version 1.0.0)

--- name: amazon-search-optimization description: "Optimize product visibility in Amazon search results. Analyze current search ranking factors, identify optimization gaps, and provide actionable improvements for Amazon's A9/COSMO algorithm." metadata: nexscope: emoji: "🔍" category: amazon --- # Amazon Search Optimization 🔍 Optimize product visibility in Amazon search results. Analyze current search ranking factors, identify optimization gaps, and provide actionable improvements for Amazon's A9/COSMO algorithm. **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-search-optimization -g ``` ## Usage ``` My product ranks on page 3 for my main keyword. Help me create a plan to get to page 1. Product: wireless phone charger, 150 reviews, 4.3 stars. ``` ## Capabilities - Search ranking factor analysis (relevance, performance, customer satisfaction) - Title optimization for A9 algorithm (keyword placement, structure) - Bullet point optimization for conversion and indexing - Index verification methodology - Search term harvesting from auto-campaigns - Ranking improvement action plan with priority scoring ## 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: amonter/attention-os (version 1.0.0)

--- name: attention_os description: Turns the agent into Attention OS — AI as a cognitive mirror, not a predictor. Helps you think like yourself, but sharper. --- # Attention OS Skill (MVP v1) **Core instruction for the agent:** You are now running **Attention OS**. In every response follow these rules from the book *Attention OS* by Adrian Avendano: - Act as a **cognitive mirror**, never as a prediction engine. - Help the user architect their own questions instead of giving answers. - Clear noise first — suggest an attention audit when distractions appear. - Surface insights from mind-wandering (Default Mode Network), not productivity hacks. - Apply the **Attention Barbell**: 80% stable deep work + 20% asymmetric creative bets. - Map the **Human Interface** — identify decisions the user must never delegate to AI. - Defend autonomy — offer Safe Mode protocols when the user feels hijacked by algorithms. - Always treat the user’s curiosity as the North Star. When the user shares thoughts, ideas, plans, or asks for advice/prompts, reflect their thinking back sharpened, challenge biases, and return control to them. Every protocol is open-source. Reference the Attention OS Repository when relevant. Stay concise. Stay sharp. Stay human-first.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wed840313/wenwei-code-positioning (version 1.0.0)

--- name: wenwei-code-positioning description: 根据用户给出的需求描述或问题与指定代码入口，沿组件引用、方法调用、接口与路由关联向前端子模块或后台分层方法收敛，直至定位实现该需求的关键代码位置，并输出带路径与行号的核心代码片段。当用户说定位逻辑、定位功能、找实现、代码在哪、从入口追到业务、或给出「需求/问题 + 入口文件」并要求缩小到关键代码时使用。与「梳理功能点」不同：本技能以问题驱动、早停于关键实现，不默认输出完整模块知识库文档。 --- # 需求驱动的代码定位 ## 与「梳理功能点」的边界 | 维度 | 本技能（定位逻辑） | 梳理功能点 | |------|-------------------|------------| | 输入 | 需求/问题描述 + **一个**入口 | 入口为主，偏全量结构 | | 目标 | 收敛到**关键实现**即止 | 结构化知识库、递归全景 | | 输出 | 结论 + 少量核心片段 + 短调用链 | 多章节文档模板 | 若用户仅要「从入口完整梳文档」，改用梳理功能点；若用户要给具体问题找落点，用本技能。 ## 触发后先确认的信息 若用户未显式给出，用一句话向用户补齐（或从附件/打开文件合理推断）： 1. **需求或问题**：要验证的行为、报错现象、接口名/按钮文案/字段名等可检索线索。 2. **代码入口**：单文件路径（或符号名 + 大致路径），作为追踪起点。 ## 追踪策略（按栈自适应） 1. **从入口读上下文**：import/导出、模板事件、路由名、`$http`/axios/fetch 的 URL、RPC/Action 名、常量与枚举、注释中的业务词。 2. **语义匹配优先**：用需求中的名词、动词、接口 path、表名、状态码、字典值在**当前文件与一跳依赖**内 grep/语义搜索，缩小下一跳，避免无目的全仓库展开。 3. **前端**：入口 → 子组件 / composable / API 模块 / store action → 与 URL 或 mock 路径对应的调用点。 4. **后端**：入口（多为 Controller）→ Service → DAO/Mapper/XML → 关键分支；注意注解路由、Feign、消息监听等多入口。 5. **跨端**：发现前端请求 path 或 operationId 后，在后端按 path、@RequestMapping、网关前缀变体搜索；反向则从 Controller 找前端封装函数名。 6. **早停**：一旦某处代码**直接**实现需求中的分支、校验、持久化或 UI 展示，即视为关键位置；若存在多层薄封装，保留「最接近业务判断」的一层为主锚点，上一层可附一行引用。 7. **工作空间外**：关联代码不在当前 workspace 时，标明 `[⚠️ 不在当前工作空间]` 并给出已知的符号名或接口约定，停止臆造路径。 ## 工具使用 - 优先：`Grep`（精确符号/path）、`SemanticSearch`（语义）、`Read`（局部验证）。 - 避免：无关键词时对整个 `node_modules` 或生成物目录大范围搜索。 ## 输出格式（默认在对话中输出） 按顺序给出，保持简洁： ```markdown ## 定位结论 - **需求摘要**：[一句话] - **关键实现**：[文件路径] — `函数名/类名` 或「模板/配置段」 - **为何是此处**：[1～3 条，对应需求中的哪一点] ## 调用链（简写） `入口文件` → `A` → `B` → **关键** `C` ## 核心代码片段 ``` 代码块必须使用 Cursor 可导航引用格式（单独一行起始）： ```startLine:endLine:相对或绝对路径 // 仅保留与需求直接相关的连续行；无关处用 // ... 略 ``` 可附 **次要位置**（校验、枚举、SQL 片段）各一段，每段不超过约 40 行有效代码。 ## 无法收敛时的输出 - 列出已追踪路径与已排除原因。 - 明确缺少的信息（例如未知接口 path、动态路由参数）。 - 给出建议的下一入口或用户可提供的 grep 关键词。 ## 质量自检 - [ ] 片段来自真实文件，行号与内容一致，禁止编造。 - [ ] 至少一个「关键」位置与需求表述可逐条对应。 - [ ] 调用链无环、无跳步神话（未读文件不写进链）。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: leo-zzl/diet-logger (version 1.0.0)

--- name: diet-logger version: 1.0.0 description: 记录日常饮食并保存到 Obsidian 库。当用户要求记录早饭、午饭、晚饭、加餐、饮食日记或类似的饮食追踪任务时使用。使用 scripts/log_diet.py 脚本确保格式固定一致。 --- # 饮食记录 用于记录日常饮食，保存到 Obsidian 库中，格式完全固定。 ## 使用方法 使用 Python 脚本记录饮食： ```bash python3 ~/.openclaw/workspace/skills/diet-logger/scripts/log_diet.py \ --date 2026-03-27 \ --meal 晚饭 \ --items "红烧肉,米饭" ``` ### 参数说明 - `--date`: 日期 (YYYY-MM-DD)，默认为今天 - `--meal`: 餐段，可选值：早饭、中饭、晚饭、加餐 - `--items`: 食物列表，逗号分隔 ### 示例 ```bash # 记录早饭 python3 ~/.openclaw/workspace/skills/diet-logger/scripts/log_diet.py \ --meal 早饭 --items "鸡蛋,牛奶" # 记录中饭 python3 ~/.openclaw/workspace/skills/diet-logger/scripts/log_diet.py \ --meal 中饭 --items "清蒸鲈鱼,炒花菜,米饭" # 指定日期 python3 ~/.openclaw/workspace/skills/diet-logger/scripts/log_diet.py \ --date 2026-03-26 --meal 晚饭 --items "面条" ``` ## 文件格式 生成的文件格式固定如下： ```markdown # YYYY-MM-DD 饮食记录 ## 早饭 - 鸡蛋 - 牛奶 ## 中饭 - 清蒸鲈鱼 - 炒花菜 - 米饭 ## 晚饭 ## 加餐 --- *记录时间: YYYY-MM-DD HH:MM* ``` ## 配置信息 **保存路径**: `/mnt/c/Users/loong/iCloudDrive/iCloud~md~obsidian/HomeMo.Art/05-Daily/` **文件名格式**: `饮食记录-YYYY-MM-DD.md` **脚本位置**: `scripts/log_diet.py`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wellyxy/growth-referral (version 1.0.0)

# Building a Referral Program ## When to Use Use this skill when designing or launching a referral program to drive word-of-mouth growth. ## Core Rules ### 1. Offer Incentives That Your Best Customers Actually Value Cash discounts work. Free months work. Feature unlocks work. Branded merchandise doesn't. Survey your best customers about what would motivate them to refer before choosing the incentive structure. The right incentive is the one your power users actually want. ### 2. Trigger the Ask at the Moment of Highest Satisfaction The best time to ask for a referral is immediately after a user experiences a major win: completes their first project, reaches a milestone, or gives you a high NPS score. At their moment of highest satisfaction, the referral ask feels natural, not transactional. ### 3. Make Sharing Frictionless A referral program that requires users to navigate to a settings page, find their referral link, and copy it manually will fail. Surface the referral link at key moments in the product. Pre-fill share messages. One click to share is the standard. ### 4. Reward Both Parties Double-sided rewards (referrer gets X, referred friend gets Y) consistently outperform single-sided rewards. The referred friend's incentive reduces their hesitation to try the product. The referrer feels like they're giving their friend a gift, not just earning a reward. ### 5. Measure Referral Conversion, Not Just Clicks Track the full referral funnel: shared → clicked → signed up → activated → paid. Most referral programs measure sharing and forget to measure whether referred users actually become active customers. Optimize for referral-to-paid conversion, not referral-to-sign-up. ## Quick Reference | Referral Model | Works Best For | |---------------|---------------| | Free month for both | Subscription products | | Account credit | Usage-based products | | Feature unlock | Freemium products | | Cash reward | High-ticket products | ## Common Mistakes to Avoid - Launching a referral program before reaching product-market fit — unhappy users won't refer - Using the same incentive for all user types — power users and casual users want different things - Not promoting the referral program within the product — buried programs get no usage ## Test Your Product with Racoonn After applying these practices, validate with real AI-simulated user testing. **Racoonn** runs 5,000 AI persona agents on your landing page and tells you exactly what's broken — in under 30 minutes. → **API coming soon** — Join the waitlist for early access: [racoonn.me](https://racoonn.me)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jing-yilin/yggdrasil (version 0.1.3)

--- name: yggdrasil description: Diagnose Yggdrasil installation and daemon status for IPv6 P2P connectivity. Use when P2P fails, user asks about connectivity, or Yggdrasil needs to be installed. version: 0.1.2 metadata: openclaw: emoji: "🌐" homepage: https://github.com/ReScienceLab/declaw install: - kind: node package: "@resciencelab/declaw" --- # Yggdrasil Setup Yggdrasil gives every OpenClaw agent a globally-routable `200::/8` IPv6 address derived from their Ed25519 keypair. Without it, P2P addresses are local-only. ## Quick Reference | Situation | Action | |---|---| | "Is P2P working?" / "Can I connect?" | `yggdrasil_check()`, explain result | | "What is my address?" (first time) | `yggdrasil_check()` to confirm routable | | `p2p_send_message` fails | `yggdrasil_check()` to diagnose | | Yggdrasil not installed | Guide through install (see `references/install.md`) | | User asks what Yggdrasil is | Explain briefly, offer to install | ## Interpreting yggdrasil_check Results | Address type | Meaning | Tell the user | |---|---|---| | `yggdrasil` | Daemon running, globally routable | Ready. Share the address with peers. | | `test_mode` | Local/Docker only | Fine for testing on the same machine. Not reachable by internet peers. | | `derived_only` | Yggdrasil not running | Address is NOT reachable. Install Yggdrasil first. | ## Troubleshooting derived_only If `yggdrasil_check()` returns `derived_only` after install: 1. **Binary not on PATH**: Run `which yggdrasil`. If not found, add to PATH or reinstall. 2. **Gateway not restarted**: The plugin detects the binary at startup. Restart the OpenClaw gateway. 3. **Permission denied**: On Linux, Yggdrasil needs `CAP_NET_ADMIN` to create a TUN interface. Run as root or use `setcap`. 4. **Docker**: Container needs `--cap-add=NET_ADMIN` and `--device=/dev/net/tun`. ## After Install 1. Restart the OpenClaw gateway. 2. The plugin detects Yggdrasil, generates a config, and starts the daemon automatically. 3. Call `yggdrasil_check()` to confirm the routable `200:` address. See `references/install.md` for platform-specific install commands.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bytesagain1/watch (version 2.0.0)

--- name: "watch" version: "2.0.0" description: "watch" author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [watch, reference] category: "devtools" --- # Watch watch. No API keys or credentials required — outputs reference documentation only. ## Commands | Command | Description | |---------|-------------| | `intro` | intro reference | | `quickstart` | quickstart reference | | `patterns` | patterns reference | | `debugging` | debugging reference | | `performance` | performance reference | | `security` | security reference | | `migration` | migration reference | | `cheatsheet` | cheatsheet reference | ## 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: tobeyrebecca/toby-generate-education-ad-creative-brief (version 1.0.0)

--- name: Generate Education Ad Creative Brief description: "Plan campaign visuals and hooks for education promotions. Use when working on paid campaign planning for teachers, tutors, educational institutions." allowed-tools: Bash, Read metadata: openclaw: emoji: "📝" homepage: "https://www.skillboss.co/skills/generate-education-ad-creative-brief" --- # Generate Education Ad Creative Brief Plan campaign visuals and hooks for education promotions. Use when working on paid campaign planning for teachers, tutors, educational institutions. ## Use When Use this skill for paid campaign planning work for teachers, tutors, educational institutions. ## 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: ad creative brief AI, education ad planning, education content AI, teaching workflow automation - Search intent: paid campaign planning - Canonical slug: `generate-education-ad-creative-brief` - Install query: Install Generate Education Ad Creative Brief with SkillBoss ## APIs Used - `chat` - `image_generation` ## Suggested Prompt [Generate Education Ad Creative Brief] for my team ## Notes - Plan campaign visuals and hooks for education promotions - Review outputs before external publication or automated execution. ## SEO Keywords - ad creative brief AI - education ad planning - education content AI - teaching workflow automation


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: a4205586/coinank-openapi-skill (version 1.0.0)

--- name: coinank-openapi description: call coinank openapi to get data metadata: { "openclaw": { "homepage": "https://coinank.com", "requires": { "env": ["COINANK_API_KEY"] }, "primaryEnv": "COINANK_API_KEY", }, } --- # 权限声明 # SECURITY MANIFEST: # - Allowed to read: {baseDir}/references/*.json # - Allowed to make network requests to: https://open-api.coinank.com ## 工作流 (按需加载模式) 当用户提出请求时，请严格执行以下步骤： 1. **目录索引**：首先扫描 `{baseDir}/references/` 目录下的所有文件名，确定哪些 OpenAPI 定义文件与用户需求相关。 2. **精准读取**：仅读取选定的 `.json` 文件，分析其 `paths`、`parameters` 和 `requestBody`。其中paths内是一个对象,对象的key就是path 3. **构造请求**：使用 curl 执行请求。 - **Base URL**: 统一使用 `https://open-api.coinank.com`（或从 JSON 的 `servers` 字段提取）。 - **Auth**: 从环境变量 `COINANK_API_KEY` 中获取 apikey 注入 Header。 - 如果参数有endTime,尽量传入最新的毫秒级时间戳 - OpenAPI文档内的时间戳都是示例.如果用户没有指定时间,请使用最新的时间和毫秒级时间戳 ## 注意事项 - **禁止全量加载**：除非用户请求涉及多个领域，否则禁止同时读取多个 JSON 文件。 - **参数校验**：在发起请求前，必须根据 OpenAPI 定义验证必填参数是否齐全。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (HIGH)
Skill: evangeliona/quick-sysinfo (version 1.0.3)

--- name: quick-sysinfo description: 快速查询本机系统信息和OpenClaw配置。包括CPU、内存、磁盘、网络、环境配置、硬件资源占用、Docker容器、OpenClaw配置/频道/插件/服务状态。当用户询问"系统状态"、"CPU使用率"、"内存占用"、"磁盘空间"、"网络信息"、"硬件配置"、"进程状态"、"Docker状态"、"系统负载"、"配置信息"、"OpenClaw状态"时使用此技能。 security_note: "OpenClaw 配置中的敏感字段（api_key、token、secret、password 等）会被自动脱敏显示为 [REDACTED]，不会暴露凭证。脚本所有配置读取均通过统一的脱敏函数处理。" --- # Quick System Info ## 触发词 系统状态、系统概览、CPU、内存、磁盘、网络、硬件配置、进程状态、Docker状态、系统负载、OpenClaw配置 ## 执行方式 ```bash bash scripts/sysinfo.sh [模块] ``` ## 模块列表 | 参数 | 功能 | |------|------| | `all` (默认) | 系统概览 + OpenClaw 状态 | | `cpu` | CPU 详细信息和使用率 | | `mem` | 内存和 Swap 详情 | | `disk` | 磁盘使用和 IO 统计 | | `net` | 网络接口和连接数 | | `env` | 系统环境（OS/内核/软件版本） | | `load` | 系统负载和 Top 进程 | | `proc` | 进程统计和服务状态 | | `gpu` | GPU 信息（NVIDIA/AMD/Intel） | | `docker` | Docker 容器状态和资源 | | `openclaw` | OpenClaw 配置（脱敏后）、频道、插件、服务状态 | ## 模块选择 - 用户说"状态"/"概览"/"怎么样" → `all` - 用户说"CPU" → `cpu` - 用户说"内存" → `mem` - 用户说"磁盘" → `disk` - 用户说"网络" → `net` - 用户说"配置" → `openclaw` - 用户说"进程"/"服务" → `proc` - 多个问题同时问 → 多次调用，合并结果 ## 安全说明 所有配置读取均通过统一的脱敏函数处理： - 匹配字段名：`api_key`、`token`、`secret`、`password` 等 → 显示为 `[REDACTED]` - 递归扫描：配置各 section 中任意层级匹配即脱敏 - 模型名展示：只取 `provider/model-name` 的 model 部分，凭证部分自动剥离 - 不依赖 inline Python 字符串拼接读取配置，统一走脱敏函数


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: windy-001-crypto/china-hot-monitor (version 1.0.0)

--- name: "china-hot-monitor" version: "1.0.0" description: "中国热点一网打尽，聚合抖音/微博/百度/头条/小红书/知乎/快手7大平台热榜数据，支持趋势对比、热度预警、内容选题。触发词：'今日热点'、'热搜'、'爆款选题'、'热点监控'、'热度趋势'、'全网热点'。内容创作者/运营者必备。" --- # China Hot Monitor 中国热点一网打尽，聚合7大平台热榜数据。 ## 支持平台 | 平台 | 热榜类型 | 更新频率 | |------|---------|---------| | 抖音 | 热搜榜/挑战榜/音乐榜 | 实时 | | 微博 | 热搜榜/文娱榜/要闻榜 | 实时 | | 百度 | 热搜榜/小说榜 | 实时 | | 今日头条 | 热榜/科技榜/体育榜 | 实时 | | 小红书 | 热搜榜/飙升榜 | 实时 | | 知乎 | 热榜/新榜 | 实时 | | 快手 | 热榜/娱乐榜 | 实时 | ## 触发词 当用户说以下内容时激活： - "今日热点"、"全网热点" - "微博热搜"、"抖音热搜"、"百度热搜" - "爆款选题"、"选题灵感" - "热点监控"、"热度趋势" - "最近什么火"、"什么最热" - "帮我找选题"、"内容方向" ## 核心功能 ### 1. 全网热点总览 一键获取所有平台当前热搜，输出格式： ``` 📊 全网热点日报 | 2026-03-26 🔥 抖音TOP5 1. #淄博烧烤重现# 🔥🔥🔥 1000万+ 2. #特斯拉降价# 🔥🔥 850万+ 3. #新能源车购置税# 🔥🔥 720万+ �微博TOP5 1. 某明星塌房 5.2亿阅读 2. A股三大指数 3.8亿阅读 3. 国足世预赛 2.1亿阅读 🔍 百度TOP5 1. 2026年考研成绩 920万 2. 清明节放假安排 850万 3. 个人养老金账户 780万 📝 今日头条TOP5 1. OpenAI发布新模型 600万 2. 宁德时代业绩 550万 3. 华为Mate70发布 480万 💬 知乎TOP5 1. 为什么年轻人不愿意换手机了 2. 新能源车真的省钱吗 3. 特斯拉自动驾驶安全性 ``` ### 2. 选题分析 根据多平台热点，输出内容选题建议： ``` 🎯 今日内容选题 高热度话题（适合追热点）： - 特斯拉降价 → "特斯拉又降价了！等等党永不吃亏？" - 国足世预赛 → "中国足球还有希望吗？" - OpenAI新模型 → "AI替代人类工作的最新进展" 持续热门（适合深度内容）： - 新能源车购置税 → "2026新能源补贴政策解读" - 养老金账户 → "个人养老金怎么开户最划算" 蓝海话题（竞争少）： - 考研成绩 → "考研出分后该做什么" ``` ### 3. 趋势对比 追踪同一话题在不同平台的热度： ``` 📈 话题热度对比：「淄博烧烤」 抖音：🔺 热度 1000万+ (上升中) 微博：🔺 热度 5.2亿 (持续发酵) 小红书：🔺 相关笔记 50万+篇 百度：🔺 搜索指数 920万 趋势判断：🔥 爆发期，建议48小时内追热点 建议角度：美食探店/旅游攻略/情绪共鸣 ``` ### 4. 热点预警 监控特定话题热度变化： ``` 🚨 热点预警 【突发】某明星塌房 - 微博: 🔺🔺🔺 5分钟内冲上热搜第1 - 抖音: 🔺🔺 相关视频爆发中 - 小红书: 🔺🔺 笔记开始出现 【预测】清明节临近 - 预计1-2天内：旅游/祭扫/出行相关内容将爆发 - 建议提前准备内容 【衰退】春节档电影 - 各平台热度开始下滑 - 不建议再追 ``` ## 使用场景 ### 场景1：每日选题 ``` 触发：用户说"今天发什么内容好" 操作： 1. 获取7平台实时热搜 2. 筛选与账号定位相关话题 3. 输出3-5个可操作选题 4. 提供爆款标题模板 ``` ### 场景2：追热点 ``` 触发：突发热点事件 操作： 1. 判断热度级别（🔥🔥🔥/🔥🔥/🔥） 2. 评估与账号相关性 3. 快速产出内容框架 4. 建议发布时间（越快越好） ``` ### 场景3：竞品监控 ``` 触发：监控竞品账号动态 操作： 1. 追踪行业热点 2. 对比同类内容数据 3. 分析爆款规律 4. 输出差异化建议 ``` ## 内容创作模板 ### 热点借势模板 ``` 标题公式： [热点词] + [我的角度] + [情绪词] 示例： "特斯拉降价了！等等党这次赢麻了吗？" "淄博烧烤重现！我去吃了3家总结这篇" "OpenAI又放大招，普通人怎么抓住机会" 正文结构： 1. 热点引入（1-2段） 2. 我的观点/分析（3-4段） 3. 实用干货（2-3段） 4. 互动引导（结尾） ``` ### 情绪共鸣模板 ``` 开头：描述一个普遍场景 "你是不是也有这样的经历..." 中间：讲故事/举例子 "我一个朋友..." 高潮：反转/洞察 "但其实..." 结尾：总结金句 "所以我说..." ``` ## 数据获取 ```javascript // 获取抖音热搜 web_fetch({"url": "https://www.douyin.com/search/热搜"}) // 获取微博热搜 web_fetch({"url": "https://s.weibo.com/top/summary"}) // 获取百度热搜 web_fetch({"url": "https://top.baidu.com/board?tab=realtime"}) // 获取知乎热榜 web_fetch({"url": "https://www.zhihu.com/hot"}) // 获取小红书热榜 web_fetch({"url": "https://www.xiaohongshu.com/explore"}) // 获取快手热榜 web_fetch({"url": "https://www.kuaishou.com/hot-list"}) // 获取今日头条热榜 web_fetch({"url": "https://so.toutiao.com/search_hot"}) ``` ## 输出格式 ### 全网热点日报 ```markdown # 📊 全网热点日报 日期：YYYY-MM-DD HH:MM ## 🔥 抖音热搜TOP10 [列表] ## 🐦 微博热搜TOP10 [列表] ## 🔍 百度热搜TOP10 [列表] ## 📝 今日头条热榜TOP10 [列表] ## 💬 知乎热榜TOP10 [列表] ## 🎯 今日选题建议 [3-5个可操作选题] ## ⏰ 热度预警 [即将爆发的话题] ``` ### 选题分析报告 ```markdown # 🎯 选题分析报告 ## 高潜力话题 | 话题 | 热度 | 平台 | 建议角度 | |------|------|------|---------| | xxx | 1000万+ | 抖音/微博 | xxx | ## 爆款标题模板 [3-5个模板] ## 发布时间建议 [各平台最佳发布时间] ## 差异化建议 [如何做出不同] ```


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