OpenClaw/clawhub-security-signals huggingface dataset:
Skill: lujun2508/law-exam-trainer (version 1.0.0)

--- name: law-exam-trainer description: 法考复习助手 - 根据法考视频/文档资料整理题库，支持选择题练习、错题解析、知识点定位。当用户说：法考、刷题、司法考试、法律职业资格考试、练习题、错题整理时触发。 --- # 法考复习助手 Skill ## 功能概览 1. **内容输入** — 上传法考视频/文档 → AI 提取内容 2. **题目生成** — 从资料中出选择题/判断题题库 3. **练习模式** — 做答 → 即时反馈对错 4. **错题解析** — 答错 → 告诉哪里错 + 涉及知识点 ## 题库来源 | 类型 | 说明 | |------|------| | 视频文件 | 上传视频 → 提取音频 → Whisper 转文字 → 出题 | | 文字文档 | 直接上传/粘贴 → 提取关键知识点 → 出题 | | 图片/讲义 | OCR 识别 → 整理知识点 → 出题 | ## 题目类型 - 单选题 / 多选题 / 判断题 - 答案立即反馈 - 解析包含：正确答案 + 错因 + 涉及法条 ## 相关资源 - GitHub: `fighting41love/funNLP`（含法律词库/罪名法务名词） - ClawHub: `legal-document-assistant`、`china-legal-analysis` ## 使用方式 1. **发资料** — 上传视频/文档给 CEO 2. **出题** — CEO 生成题库 3. **练习** — QQ 对话形式做答，CEO 评判对错 ## 题库目录 题库文件保存在：`C:\Users\lujun\openclaw-projects\law-exam\题库\`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xueylee-dotcom/qmt-dev (version 1.0.0)

--- name: qmt-dev description: QMT平台开发专用技能。当用户说"QMT"、"实盘"、"QMT策略"、"QMT回测"、"QMT开发"时自动触发。提供QMT API规范、核心函数机制、止损监控方式、实盘注意事项。适用于使用QMT平台的量化策略开发。 --- # QMT平台开发技能 > 版本：1.0.0 > 适用平台：迅投QMT量化交易平台 --- ## 🎯 核心函数机制 ### 1. QMT标准函数 | 函数 | 用途 | 触发时机 | 说明 | |------|------|---------|------| | `init()` | 初始化 | 策略启动时 | 加载信号、设置参数、设置股票池 | | `handlebar()` | K线处理 | **每根K线** | 主要逻辑，买入/卖出/止损 | --- ### 2. ⚠️ 重要：is_last_bar() 机制 | 场景 | is_last_bar() 返回值 | 止损检查 | |------|---------------------|---------| | **回测** | 每根K线都返回True | ✅ 每天检查 | | **实盘** | 只在特定时刻返回True | ❌ 盘中可能不检查 | **关键理解**： - 回测时，每根K线都是"最后一根"，所以`is_last_bar()`都返回True - 实盘时，`is_last_bar()`只在特定时刻返回True（可能是收盘时） **结论**： - ❌ 不要用`is_last_bar()`控制止损/止盈检查 - ✅ 用时间判断控制检查频率 --- ## 📋 止损监控方式 ### 方式1：时间判断（推荐） ```python def handlebar(ContextInfo): """每根K线都会触发""" # 止损监控（每5分钟检查一次） now = datetime.now() if G.last_check_time: elapsed = (now - G.last_check_time).total_seconds() / 60 if elapsed >= 5: # 每5分钟检查 check_stop_loss(ContextInfo) G.last_check_time = now # 买入逻辑（可以保留is_last_bar()限制） if not ContextInfo.is_last_bar(): return # 买入逻辑... ``` ### 方式2：bar_count判断 ```python def handlebar(ContextInfo): """每根K线都会触发""" # 每5根K线检查一次止损 if ContextInfo.bar_count % 5 == 0: check_stop_loss(ContextInfo) ``` --- ## 🚀 代码模板 ### 完整策略模板 ```python # 全局状态对象 class GlobalState: pass G = GlobalState() def init(ContextInfo): """初始化""" # 全局状态 G.waiting_list = [] G.sent_orders = set() G.buy_prices = {} # 记录买入价格 G.last_check_time = None # 加载信号 with open('trading_signals/alphagbm_signals_qmt.json') as f: data = json.load(f) G.signals = [s['code'] for s in data['signals']] # 设置股票池 ContextInfo.set_universe(G.signals) def handlebar(ContextInfo): """每根K线触发""" # 1. 止损监控（每5分钟） now = datetime.now() if G.last_check_time: elapsed = (now - G.last_check_time).total_seconds() / 60 if elapsed >= 5: check_stop_loss(ContextInfo) G.last_check_time = now # 2. 检查waiting_list if check_waiting_orders(ContextInfo): return # 3. 买入逻辑（只在最后一根K线） if not ContextInfo.is_last_bar(): return # 买入... ``` --- ## ⚠️ 实盘注意事项 ### 1. passorder的prType参数 ```python # ❌ 错误 passorder(23, 1101, accID, code, -1, ...) # prType=-1（价格不可控） # ✅ 正确 passorder(23, 1101, accID, code, 14, ...) # prType=14（对手价） ``` --- ### 2. waiting_list防重机制 ```python # 初始化 G.waiting_list = [] G.sent_orders = set() # 买入时记录 if code not in G.sent_orders: passorder(...) G.sent_orders.add(code) G.waiting_list.append(msg) # 检查waiting_list def check_waiting_orders(ContextInfo): for msg in G.waiting_list[:]: order_result = get_trade_detail_data(...) if order_result: G.waiting_list.remove(msg) ``` --- ### 3. 风控设置 **QMT层面**： - [ ] 金额合规（限制资金上限） - [ ] 反向单交易（禁止买入后立即卖出） - [ ] 对敲单交易（禁止高买低卖） **代码层面**： - [ ] 硬编码资金限制 - [ ] 止损止盈逻辑 - [ ] 异常处理 --- ## 🚫 禁止事项 1. ❌ 使用`is_last_bar()`控制止损检查 2. ❌ 使用prType=-1 3. ❌ 没有waiting_list防重机制 4. ❌ 没有止损止盈逻辑 --- ## 📚 参考文档 - QMT官方示例（双均线实盘、竞价选股、行业轮动、多因子选股） - QMT API文档 - QMT_STRATEGY_DEVELOPMENT_STANDARDS.md --- *技能版本：1.0.0*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: hoyin258/macos-screenshot-telegram (version 1.0.0)

--- name: macos-screenshot-telegram description: | Take a screenshot on macOS and send it to Telegram. Use when the user asks to capture their screen, take a screenshot, or send a screen capture to Telegram. Works around OpenClaw's message tool bug (#15541) that fails to send media. --- # Setup (Prerequisites) ## 1. Telegram Bot Token 1. 搵 @BotFather on Telegram 2. Send `/newbot` 創建新 bot 3. 拎個 bot token（好似 `1234567890:ABCdefGHIjklMNOpqrsTUVwxyz`） ## 2. 搵 Telegram Chat ID - **個人 ID:** 搵 @userinfobot 或者 forward message 俾 @userinfobot - **Group ID:** Forward 任何 message 俾 @userinfobot ## 3. OpenClaw Config 響你既 OpenClaw profile config 度加入： ```json { "telegram": { "botToken": "YOUR_BOT_TOKEN_HERE", "allowFrom": ["YOUR_CHAT_ID"] } } ``` ## 4. Profile Naming 記住你用既 profile 名（例如 `main`、`rescue`），之後用既時候補返上去。 --- # macOS Screenshot to Telegram This skill captures the macOS screen and sends it directly via Telegram Bot API. ## Workflow 1. **Capture screenshot** using macOS built-in command: ```bash /usr/sbin/screencapture -x <output-path> ``` 2. **Copy to workspace** (required - OpenClaw has security restriction): ```bash cp <source> <workspace>/screenshot.png ``` 3. **Send via Telegram Bot API** (bypasses buggy message tool): ```bash BOT_TOKEN=$(grep botToken <config-path> | sed 's/.*"botToken": *"\([^"]*\)".*/\1/') curl -s -X POST "https://api.telegram.org/bot${BOT_TOKEN}/sendPhoto" \ -F "chat_id=<target-chat-id>" \ -F "photo=@<workspace>/screenshot.png" ``` ## Required Parameters When using this skill, ask the user for: - `target-chat-id`: The Telegram chat ID to send to (e.g., user's ID for DM) - `profile`: The OpenClaw profile name (e.g., "main", "rescue") ## How to Find Paths 1. **Config file:** `~/.openclaw-<profile>/openclaw.json` 2. **Workspace:** `~/.openclaw/workspace-<profile>/` For example, if your profile is "main", paths would be: - Config: `~/.openclaw-main/openclaw.json` - Workspace: `~/.openclaw/workspace-main/` ## Notes - The `message` tool in OpenClaw has a bug (#15541) that returns success but doesn't send media - Always use curl with Telegram Bot API directly for reliable media delivery - The screenshot must be copied to workspace first due to OpenClaw's allowed directory security restriction - This skill is profile-agnostic - just pass the correct profile name


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: odrobnik/who-growth-charts (version 1.2.3)

--- name: who-growth-charts description: Generate WHO child growth charts (height, weight, BMI) with percentile curves. Downloads official WHO reference data on demand. Use when users ask about child growth tracking, percentiles, or want growth charts for their kids. version: 1.2.3 homepage: https://github.com/odrobnik/who-growth-charts-skill metadata: {"openclaw": {"emoji": "📈", "requires": {"bins": ["python3"], "python": ["pandas", "matplotlib", "scipy", "openpyxl"]}}} --- Generate WHO Child Growth Standards charts with percentile curves and child data overlay. ## Features - **Height-for-age** (0-19 years) - **Weight-for-age** (0-10 years) - **BMI-for-age** (0-19 years) - Supports **boys and girls** - **Downloads WHO data on demand** from cdn.who.int (cached locally) - Overlays child's actual measurements with trend line ## Examples | Height | Weight | BMI | |--------|--------|-----| | <img src="examples/anna_height.png" width="250"> | <img src="examples/anna_weight.png" width="250"> | <img src="examples/anna_bmi.png" width="250"> | ## Prerequisites Install Python dependencies: ```bash pip install pandas matplotlib scipy openpyxl ``` ## Usage ### Basic Chart Generation ```bash python3 ./scripts/growth_chart.py "Child Name" "DD.MM.YYYY" --sex F --type all ``` Arguments: - `name`: Child's name (used in chart title) - `birthdate`: Date of birth in DD.MM.YYYY format - `--sex` / `-s`: `F` (female) or `M` (male) — default: F - `--type` / `-t`: `height`, `weight`, `bmi`, or `all` — default: all - `--data` / `-d`: JSON file with measurement data - `--output` / `-o`: Output directory for charts ### With Measurement Data Create a JSON file with height/weight measurements (heights in meters, weights in kg): ```json { "heights": [ ["2024-01-15T10:00:00", 1.05] ], "weights": [ ["2024-01-15T10:00:00", 17.5] ] } ``` ```bash python3 ./scripts/growth_chart.py "Emma" "06.07.2016" --sex F --data emma_data.json --type all ``` ### Integration with Withings Combine with `withings-family` skill to fetch weight data automatically: ```bash # Get Withings weight data (assuming withings-family skill is installed) python3 ../withings-family/scripts/withings.py emma body > /tmp/withings.json # Parse and generate charts # (The growth chart script handles Withings JSON format if implemented, otherwise transform it) ``` ## Output By default, charts and cache are written to: - `<workspace>/who-growth-charts/` - `<workspace>/who-growth-charts/cache/` Where `<workspace>` is the folder that contains your `skills/` directory (auto-detected from CWD or script location).


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: sky-lv/skylv-schema-validator (version 1.0.0)

--- description: Data validation assistant. Validates JSON/CSV/Excel schemas and data quality checks. Triggers: data validation, json schema, csv validation, data quality. keywords: validation, json, csv, excel, schema name: skylv-data-validator triggers: data validator --- # skylv-data-validator > Universal data validation. 17 built-in rules, schema inference, JSON validation. ## Skill Metadata - **Slug**: skylv-data-validator - **Version**: 1.0.0 - **Description**: Validate JSON, objects, arrays against schemas. 17 built-in validators including type, pattern, email, URL, UUID. Schema inference from examples. - **Category**: data - **Trigger Keywords**: `validate`, `schema`, `check`, `data quality`, `validation` --- ## Built-in Validators (17) | Rule | Description | |------|-------------| | required | Value must be present | | type | string|number|boolean|object|array | | min/max | Number range | | minLength/maxLength | String length | | pattern | Regex match | | email | Valid email | | url | HTTP(S) URL | | uuid | UUID format | | enum | Must be in list | | integer | Integer check | | positive | Number > 0 | | date | Valid date | | isoDate | ISO 8601 format | | json | Valid JSON | --- ## Market Data Top competitor: `data-validation` (1.054) — weak competition. --- *Built by an AI agent that validates everything.* ## Usage 1. Install the skill 2. Configure as needed 3. Run with OpenClaw


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: rfdiosuao/feishu-expression-coach (version 1.0.0)

--- name: expression-coach description: | 表达教练 - 分析用户的文字/语音转文字内容，指出表达中的问题（啰嗦、模糊、逻辑混乱、语气不当等）， 给出优化建议和改写版本。帮助用户更清晰、简洁、有力地表达。 version: 1.0.0 --- # 表达教练 Skill ## 核心职责 你是一个严格的表达教练。用户的每一条消息（语音转文字、打字草稿、消息预览），你都要从旁观者角度挑毛病，帮他们把话说清楚。 ## 触发方式 用户消息满足以下任一条件时触发： - 消息以「教练」开头 - 消息以「润色」开头 - 消息包含「帮我看看」「这话怎么说」「我这样说行吗」 - 消息较长（>50字）且用户明确要求反馈 ## 分析维度 每次分析从以下 5 个维度评估： | 维度 | 检查点 | |------|--------| | 🎯 清晰度 | 核心观点是否一句话能说清？有没有模糊词（"那个""差不多""大概"）？ | | ✂️ 简洁度 | 有没有废话、重复、绕圈子？能不能砍掉 30% 字数？ | | 🔗 逻辑性 | 因果是否成立？有没有自相矛盾？结构是否"结论先行"？ | | 😤 语气 | 是否得体？会不会冒犯人？太软还是太硬？ | | 📋 完整性 | 关键信息（时间/地点/人物/行动）是否齐全？ | ## 输出格式 ``` 📝 表达分析 【原文】 {用户原文} 【评分】{1-10分} / 10 🔍 问题诊断 1. {最严重的问题} 2. {次要问题} 3. {细节问题} 💡 优化建议 - {对应每条问题的具体建议} ✨ 改写版本 {优化后的文字} 💬 改了啥：{一句话对比说明} ``` ## 评分标准 | 分数 | 含义 | |------|------| | 9-10 | 清晰有力，无需修改 | | 7-8 | 基本清楚，有小瑕疵 | | 5-6 | 能看懂，但需要优化 | | 3-4 | 啰嗦模糊，大改 | | 1-2 | 完全不清楚，重写 | ## 注意事项 - **不要过度修改** — 保留用户的个人风格和语气 - **指出具体问题** — 不要说"不够好"，要说"哪里不好、怎么改" - **语音转文字特殊处理** — 先修正语音识别错误（同音字、断句错误），再分析表达 - **场景感知** — 工作消息 vs 朋友聊天，标准不同 - **每次只挑最关键的 2-3 个问题** — 别搞成论文批阅


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tristanchenxm/memo-assistant (version 1.0.0)

--- name: lark-openclaw-bridge description: Personal memo assistant, triggered when the user message starts with `/private-secretary` or `/ps`. Features: 1. Automatically classifies and appends user input to the corresponding Markdown file in `~/.memo`. 2. Automatically re-classifies and reorganizes all memos under `~/.memo` when the input is "rearrange" --- # Private Secretary Analyzes input content, automatically identifies classification labels (e.g., Tech, Work, Life, Inspiration), and appends it to the corresponding Markdown file in the `~/.memo/` directory. ## Parameters - `content`: (required) The raw text content that needs to be classified and recorded. ## Instructions 1. **Analyze and Classify**: Read the `content` provided by the user. Based on the semantic meaning, classify it into one of the following: [Tech, Work, Life, Inspiration, Others]. 2. **Extract Tags**: If the content involves Kubernetes, Java, programming, etc., be sure to label it as "Tech". 3. **Format Output**: Prepare a Markdown entry including a timestamp, classification label, and the original content. 4. **Execute Write**: Use the `exec` tool to run shell commands and append the content to `~/.memo/{category}.md`. Please use English for filenames. ## Exec Command ```bash # Ensure directory exists mkdir -p ~/.memo # Generate ISO 8601 timestamp (Local Time) TIMESTAMP=$(date +"%Y-%m-%d %H:%M:%S") # Write to file (Variables populated by Agent) # {{category}} will be the English label identified (e.g., Tech, Work) printf "\n---\n### Timestamp: $TIMESTAMP\nLabel: {{category}}\n\n{{content}}\n" >> ~/.memo/{{category}}.md


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: atacan/translate-cli (version 0.2.0)

--- name: translate-cli description: End-user guide for running and configuring the `translate` CLI across text/stdin/file/glob inputs, provider selection, presets, custom prompt templates, and TOML settings. Use when users ask for command construction, config updates (`translate config`/`translate presets`), provider setup, dry-run validation, or troubleshooting translation behavior. --- # translate-cli Use this skill to help end users run and configure the `translate` CLI. `translate` is a command-line translator for text, stdin, files, globs, and `.xcstrings` catalogs. It supports multiple providers (OpenAI, Anthropic, Ollama, OpenAI-compatible endpoints, Apple providers, DeepL), prompt presets and template overrides, and persistent TOML configuration. ## Capabilities - Build correct `translate` commands for inline text, stdin, single-file, and multi-file workflows. - Keep options before positional input(s) when constructing commands (for example, `translate --to de README.md`). - Explain provider selection, credentials, model/base URL requirements, and provider-specific constraints. - Configure defaults, provider endpoints, network settings, and presets with `translate config` and `config.toml`. - Customize prompts with presets, inline templates, `@file` templates, and placeholders. - Explain output behavior (`stdout`, `--output`, `--in-place`, suffix naming), parallel jobs, dry-run, and validation errors. - Streaming output: `--stream` forces on, `--no-stream` forces off, otherwise `defaults.stream` applies. ## Starter commands ```bash translate --text --to fr "Hello world" translate --to de README.md translate --provider ollama --text --to en --dry-run "Merhaba dunya" translate config set defaults.provider anthropic ``` Note: prefer option-before-input ordering in all examples and generated commands. ## References - Quick examples: `references/quickstart.md` - Full flag and subcommand reference: `references/flags-and-subcommands.md` - TOML schema and precedence: `references/config-toml.md` - Provider rules and environment variables: `references/providers-and-env.md` - Presets, prompt templates, placeholders: `references/presets-and-prompts.md` - Runtime behavior, warnings, and exit codes: `references/behavior-and-errors.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: yangjinghua0127/tiktok-hot-cn (version 1.0.0)

--- name: douyin-hot description: "自动抓取抖音热搜榜 Top 50，包含热度值" metadata: { "openclaw": { "emoji": "📱", "requires": { "bins": ["node"] } } } --- # Skill: 抖音热搜抓取 自动抓取抖音热搜榜 Top 50，包含热度值。 ## 触发方式 ``` 抖音热搜 获取抖音热榜 douyin hot ``` ## 能力 - **直接API调用**：使用抖音官方API端点 `/aweme/v1/web/hot/search/list/` - 高性能：不依赖浏览器，响应快速（约3秒） - 清洗、排序、格式化热度值 - 返回结构化列表（排名、标题、热度、原始热度值、标签、描述） ## 输出格式 ```json [ { "rank": 1, "title": "金价一周大跌11%", "heat": "1208.2万", "heat_value": 12108917 } ] ``` 控制台输出示例： ``` 📊 抖音热搜榜（Top 50） ======================================== 1. 金价一周大跌11% 🔥 1208.2万 2. 随手一拍都是春日大片 🔥 1189.5万 ... ``` ## 配置 **依赖：Node.js 18+**（无额外依赖，使用内置https模块） ## 性能优化 - **v2.0版本**：使用直接API调用，替代Puppeteer浏览器模拟 - **速度提升**：从20-30秒降至3-5秒 - **稳定性提升**：避免浏览器兼容性和网络问题 - **资源消耗**：无需下载Chromium（节省100MB+） ## 错误处理 - 网络超时：自动重试，增加等待时间 - API 变更：提示更新选择器 - 无数据：检查网络或使用 Cookie ## 数据存储 抓取结果保存到：`workspace/scripts/douyin-hot-clean.json` ## 示例对话 ``` 用户: 抖音热搜 助手: 📊 抖音热搜榜（Top 50）: 1. 金价一周大跌11% 🔥 1208.2万 2. 随手一拍都是春日大片 🔥 1189.5万 ... ✅ 已保存到 douyin-hot-clean.json ``` ## 限制 - 需要网络访问 - API端点可能变更（但抖音官方API相对稳定） - 某些需要登录的热搜可能无法获取 ## 维护 更新脚本位置：`skills/douyin-hot/douyin-hot.js`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kukuxnd/web-extractor (version 1.0.0)

--- name: web-extractor description: 使用 jina.ai 提取网页干净文本并让 Agent 总结。触发词：提取网页、总结新闻、提取文章、获取页面内容 --- # 网页内容提取技能 使用 r.jina.ai 提取网页干净文本，过滤垃圾代码，提取干货内容。 ## 工作流程 ### 1. 提取网页内容 ```bash # 提取网页并保存为 md 文件 curl -s https://r.jina.ai/<URL> > /tmp/web-content.md ``` ### 2. 读取并总结 让 Agent 读取生成的 md 文件，总结核心观点。 ## 使用示例 **用户说："帮我总结这个新闻 https://www.bbc.com/news/tech..."** 执行： ```bash curl -s "https://r.jina.ai/https://www.bbc.com/news/technology-xxx" > /tmp/news.md ``` 然后读取 /tmp/news.md 文件内容，分析并总结。 ## 注意事项 - r.jina.ai 会过滤掉 script、nav、广告 CSS 等垃圾代码 - 提取后是极干净的纯文本，对 AI 零负担 - 支持任何新闻网站、技术博客、文章页面 - 文件默认保存到 /tmp/ 目录，可自定义路径


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bestrocky/evomap-work-processor (version 1.0.0)

--- name: evomap-work-processor description: Specialized processor for EvoMap AI work opportunities including formal verification tasks, performance optimization, and concurrent system development. Handles the complex technical challenges returned by the EvoMap heartbeat API. metadata: { "openclaw": { "requires": { "bins": ["curl"] }, "install": [] } } --- # EvoMap Work Processor Specialized skill for processing EvoMap AI work opportunities. This skill handles the complex technical challenges returned by the EvoMap heartbeat API, including formal verification tasks, performance optimization problems, and concurrent system development. ## Features - **Formal Verification Processing**: Handles B-tree verification, SAT solvers, smart contract verification - **Performance Optimization**: Analyzes performance bottlenecks and provides architectural solutions - **Concurrent Systems**: Builds verified concurrent data structures with lock-free operations - **Automated Task Handling**: Processes EvoMap work opportunities automatically - **Technical Expertise**: Leverages deep knowledge in formal methods, systems programming, and optimization ## Supported Work Types - **Verification Tasks**: B-trees, SAT solvers, theorem provers, type checkers - **Performance Issues**: Bottleneck detection and optimization strategies - **Concurrent Systems**: Garbage collectors, consensus protocols, network stacks - **Blockchain**: Smart contracts, consensus mechanisms, security proofs - **Compilers**: Optimizing compilers, JIT compilers, bytecode verifiers ## Usage This skill integrates with the EvoMap heartbeat system and automatically processes available work opportunities when they appear in the heartbeat response. ## Requirements - OpenClaw with advanced reasoning capabilities - Access to formal verification tools (optional) - Technical expertise in systems programming and formal methods ## Integration Works seamlessly with the `evomap-heartbeat-manager` skill to create a complete EvoMap node solution.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: themsquared/polymarket-weather-scanner-pro (version 1.0.0)

--- name: polymarket-weather-scanner version: 1.0.0 description: Exploit NOAA/Open-Meteo forecast vs Polymarket temperature market mispricing. Uses METAR real-time observations + ECMWF + Visual Crossing for 3-source consensus. Finds edges where meteorological forecasts diverge from market prices. author: clawdipusrex tags: [polymarket, weather, prediction-markets, trading, arbitrage, meteorology] price: 7900 --- # Polymarket Weather Scanner Find and trade mispriced temperature markets on Polymarket using professional meteorological data. ## The Edge Polymarket weather markets are priced by retail traders. Professional forecast models (ECMWF, HRRR, METAR) are far more accurate. This scanner finds markets where the forecast diverges significantly from market price. ## Data Sources (priority order) 1. **METAR** — real-time airport station observations (same-day markets) 2. **Visual Crossing** — paid API, high-accuracy global forecasts 3. **ECMWF via Open-Meteo** — European model, best for D+2/D+3 4. **HRRR/GFS** — US cities, best for D+0/D+1 ## Usage ```bash python3 weather_scanner.py # Scan + show plays python3 weather_scanner.py --buy # Scan + execute best plays python3 weather_scanner.py --dry-run # Show what would be bought ``` ## Requirements - Python 3.9+ - requests library - Optional: Visual Crossing API key (set in ~/.config/visualcrossing/credentials.json) ## Cities Tracked Seoul, Tokyo, Shanghai, Ankara, Hong Kong, Tel Aviv, New York, Miami, Dallas, Atlanta, London, Buenos Aires, Sao Paulo, Seattle, Toronto, Wellington, Lucknow ## Install ```bash pip install requests ```


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