OpenClaw/clawhub-security-signals huggingface dataset:
Skill: 0xraini/yt-digest (version 1.0.0)

# yt-digest Extract summaries, transcripts, and key moments from YouTube videos. ## Features - **Transcript extraction**: Get full transcript with timestamps - **Summary**: AI-generated summary of video content - **Key moments**: Extract chapters and highlights - **Audio output**: Convert summary to audio (via sag skill) ## Usage ```bash # Get transcript yt-digest transcript "https://youtube.com/watch?v=..." # Get summary yt-digest summary "https://youtube.com/watch?v=..." # Get key moments/chapters yt-digest chapters "https://youtube.com/watch?v=..." # Full analysis yt-digest analyze "https://youtube.com/watch?v=..." ``` ## Output ``` 📺 Video: How to Build AI Agents 👤 Channel: TechChannel ⏱️ Duration: 15:32 ## Summary This video covers the basics of building AI agents... ## Key Moments - 0:00 Introduction - 2:30 Setting up the environment - 5:45 Building the first agent - 10:20 Advanced techniques - 14:00 Conclusion ## Transcript (first 1000 chars) ... ``` ## Requirements Uses YouTube's transcript API (no API key needed for public videos).


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: cweiping/costco-inventory-monitor (version 1.1.0)

--- name: costco-inventory-monitor description: Monitor Costco inventory by ZIP and run it safely with OpenClaw cron. Keep secrets outside the skill directory. --- # Costco Inventory Monitor ## Goal Provide a repeatable workflow to check Costco inventory for one or more products across multiple ZIP codes, then write a report for downstream channels (for example WeCom). ## Repository Safety Rules - The `skills/costco-inventory-monitor` directory must contain scripts, templates, and docs only. - Never store real AK/SK, tokens, passwords, or proxy credentials inside `skills/`. - Real runtime secrets must be stored in `/root/.openclaw/workspace/.secrets/costco-monitor.env`. - `.secrets/` must stay in `.gitignore` and should not be committed. ## Files - Runner: `scripts/run_monitor.sh` - Inventory checker: `scripts/check_costco_inventory.py` - Config template (safe to commit): `config/monitor.env.example` - Standard reference: `references/costco-inventory-standard.md` ## Runtime Setup 1. Create the real secret config from template: ```bash mkdir -p /root/.openclaw/workspace/.secrets cp /root/.openclaw/workspace/skills/costco-inventory-monitor/config/monitor.env.example /root/.openclaw/workspace/.secrets/costco-monitor.env chmod 600 /root/.openclaw/workspace/.secrets/costco-monitor.env ``` 2. Edit `/root/.openclaw/workspace/.secrets/costco-monitor.env` and fill real values: - `PRODUCT_1`, `PRODUCT_2`, ... - `ZIP_CODES` - `PROXY_URL` - output paths (`OUTPUT_JSONL`, `STATE_FILE`, `REPORT_FILE`, `LOG_FILE`) 3. Run once to validate: ```bash /root/.openclaw/workspace/skills/costco-inventory-monitor/scripts/run_monitor.sh ``` ## OpenClaw Cron (every 5 minutes) Use OpenClaw cron, not system crontab, for this skill. ```bash openclaw cron create \ --name costco-inventory-monitor-5m \ --every 5m \ --session isolated \ --model hunyuan/hunyuan-t1-latest \ --delivery none \ --message 'Run /root/.openclaw/workspace/skills/costco-inventory-monitor/scripts/run_monitor.sh and then return only the contents of /root/.openclaw/workspace/ops/costco-monitor/latest_report.txt.' ``` Existing production job id (created): [REDACTED_SECRET]. ## Example Products and ZIPs - Product: `4000362984|TCL 55" Q77K|https://www.costco.com/p/-/tcl-55-class-q77k-series-4k-uhd-qled-smart-tv-allstate-3-year-protection-plan-bundle-included-for-5-years-of-total-coverage/4000362984?langId=-1` - ZIPs: `03051`, `97230` ## Output Locations - Report: `/root/.openclaw/workspace/ops/costco-monitor/latest_report.txt` - Log: `/root/.openclaw/workspace/ops/costco-monitor/monitor.log` - Snapshot JSONL: `/root/.openclaw/workspace/ops/costco-monitor/snapshots.jsonl` - State: `/root/.openclaw/workspace/ops/costco-monitor/state.json` ## GitHub Checklist - Commit: `skills/costco-inventory-monitor/**` and optional ops wrapper scripts. - Do not commit: `/root/.openclaw/workspace/.secrets/**`, real proxy/account credentials, runtime logs, local state files.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: marcbickel/morning-manifesto (version 1.0.0)

--- name: morning-manifesto description: Daily morning reflection workflow with task sync to Obsidian, Apple Reminders, and Linear metadata: {"clawdbot":{"emoji":"🌅","trigger":"/morning_manifesto"}} --- # Morning Manifesto 🌅 Trigger: `/morning_manifesto` ## Flow ### 1. Send the prompts When `/morning-manifesto` is triggered, immediately send: ``` Good morning! 🚀 Please tell me about: - What you did yesterday? - One small thing you are grateful for - Today's adventure - Tasks and commitments - How are the weekly priorities going? ``` ### 2. Wait for response Wait for user reply (text or audio). Audio is automatically transcribed via whisper.cpp. ### 3. Parse and append to Obsidian daily note Parse the response and append to today's note in the Obsidian vault (🔥 Fires). Structure: ```markdown ## Morning Manifesto - [YYYY-MM-DD] ### What I did yesterday [user's response] ### Grateful for [user's response] ### Today's adventure [user's response] ### Tasks and commitments - [task 1] - [task 2] ### Weekly priorities status [user's response] ``` ### 4. Sync tasks with Apple Reminders For each task/commitment mentioned: - **If task exists**: Update its due date to today - **If new task**: Create a new reminder with due date today - Use the `apple-reminders` skill for this ### 5. Query Linear for urgent issues Query all teams for issues with priority = urgent (1). Format: ``` 🔴 Urgent Linear Issues: - [Team] [Issue ID]: [Title] ``` ### 6. Send summary Send a final message with: - Today's Apple Reminders (all due today) - Urgent Linear issues across all teams ## Key details - Use today's date for Obsidian note naming (YYYY-MM-DD.md) - For Apple Reminders: query by due date, create with due date - For Linear: use `priority = 1` filter, query all teams - Pay special attention to "Tasks and commitments" section


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tobiasbischoff/bahn (version 1.0.0)

--- name: bahn description: Search Deutsche Bahn train connections using the bahn-cli tool. Use when you need to find train connections between German stations, check departure times, or help with travel planning. Works with station names like "Berlin Hbf", "München", "Hannover". --- # Deutsche Bahn CLI Search train connections using the `bahn-cli` tool. ## Installation The tool should be installed globally or in the workspace. If not installed: ```bash cd ~/Code/bahn-cli && npm install ``` ## Usage Search train connections: ```bash cd ~/Code/bahn-cli && node index.js search "<from>" "<to>" [options] ``` ### Options - `--date YYYY-MM-DD` - Departure date (default: today) - `--time HH:MM` - Departure time (default: current time) - `--results <number>` - Number of results to show (default: 5) ### Examples Search connections from Hannover to Bonn: ```bash cd ~/Code/bahn-cli && node index.js search "Hannover Hbf" "Bonn Hbf" --results 3 ``` Search with specific date and time: ```bash cd ~/Code/bahn-cli && node index.js search "Berlin" "München" --date 2026-02-05 --time 14:30 ``` ## Station Names - Use common German station names - "Hbf" means Hauptbahnhof (main station) - Examples: "Berlin Hbf", "München Hbf", "Frankfurt(Main)Hbf", "Köln Hbf" - Station names are case-insensitive ## Output The tool shows: - Departure and arrival times - Platform numbers - Duration - Number of changes - Intermediate stops for connections with changes - Train numbers (ICE, IC, RE, etc.) ## Notes - The CLI uses the db-vendo-client library - Some station names in output may show "undefined" (cosmetic issue, doesn't affect functionality) - Direct connections are listed first - Times are in 24-hour format


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: tranhuilee/skill-ts (version 1.0.0)

--- 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)
Skill: william202404/tob-sales-proposal (version 1.2.0)

--- name: tob-sales-proposal description: ToB销售提案生成器。输入客户信息/行业/痛点/产品，基于知识库真实案例输出带品牌色CSS的4模块HTML分页提案。 priority: high source: experience-backed workers: Sales, PM created: 2026-05-16 tags: [tob, sales, proposal, b2b, presales] --- # tob-sales-proposal — HTML 提案生成器 > 把客户信息变成有知识库案例支撑的 HTML 提案页面。 基于知识库匹配过往相似项目，输出带品牌色/CSS的4模块分页提案——不堆方法论，只讲客户能信的东西。 ## 使用方式 ```bash # 交互模式（推荐） tob-sales-proposal # 快速模式 tob-sales-proposal --quick --client "某连锁便利店" --industry "零售" \ --painpoints "库存不准,会员流失" --budget "150万" ``` 输入：客户名 / 行业 / 痛点（最多5个，逗号分隔）/ 预算 / 决策周期 ## 输出4模块 输出为 HTML 格式，带品牌色/CSS和分页导航。每模块包含：**客户输入摘要 → 知识库匹配案例引用 → 模块正文**。页面底部固定提示：复制内容给 AI 生成 PPT。 示例（痛点对齐模块）： ```html <section class="page" id="page-1"> <h2>痛点对齐：让客户觉得你懂他</h2> <p>客户输入：零售行业，库存不准，会员流失</p> <p>匹配案例：某连锁便利店（50家门店）→ 库存准确率65%→93%，6周</p> </section> ``` ### 1. 痛点对齐（让客户觉得你懂他） - 基于同行业客户的知识库痛点分布（非猜测） - 把客户说的「痛点」翻译成可解决的技术问题 - 引用案例：「XX零售集团也遇到过同样问题，他们的解法是...」 ### 2. 方案对齐（为什么要选你） - 痛点→功能映射表（一句话痛点 → 一个功能） - 差异化定位：竞品怎么解决 vs 你怎么解决 - 引用案例：「XX客户用了类似方案，6周上线，首月表现...」 ### 3. 实施路径（消除落地恐惧） - 分阶段交付：Lite → 爬坡 → 优化（默以3个月基准） - 客户配合清单（数据、接口、人员） - 引用案例：「XX同体量客户从签约到上线8周」 ### 4. 承诺型ROI（给数字，不给空话） - 基于同行业同体量过往项目的实际数据算收益 - 三档输出：保守/中性/乐观 - 引用案例：「XX客户上线后3个月初见效果，6个月回本周期」 ## 知识库匹配规则 | 行业 | 优先collection | |------|---------------| | 零售/鞋服 | knowledge-baisheng | | 金融/银行 | knowledge-unified-v2 | | 政务/央企 | knowledge-zhongruan | | 零售科技（慧博对标） | knowledge-huibo | 找不到高置信案例时输出：「无高置信匹配，建议补充知识库」 ## 实战原则 1. **痛点对齐 > 方案展示** — 模块1花30%精力，比其他模块重要 2. **案例用真不用编** — 格式：「某[行业]客户（[规模]）→ [效果]」。编的客户一眼看穿 3. **ROI给三档** — 客户信保底，你朝着乐观做 4. **预算决定了深度** — 50万以下出Lite版，50-200万标准版，200万+深度版 ## 联用 - 新提案前跑一遍 `tob-win-loss-review`（丢单复盘），避开之前踩的坑 - 成功提案交付后跑一遍 `tob-win-loss-review` 复盘，沉淀到知识库 ## 注意事项 - 输出 HTML 提案页面，不再输出 Markdown。具体内容仍需人工适配填充 - 案例引用标注来源，不编造。知识库未覆盖时不强推 - 敏感信息自动脱敏


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: yang1002378395-cmyk/openclaw-quick-start (version 1.0.1)

# OpenClaw Quick Start - 5分钟上手指南 快速配置 OpenClaw，开始 AI 自动化之旅。 ## 用途 - 新手 5 分钟配置 OpenClaw - 自动检测系统环境 - 一键安装常用 Skills - 配置基础模型（DeepSeek/GLM） ## 使用方法 ``` /openclaw-quick-start ``` ## 功能 1. 检测 macOS/Linux 环境 2. 安装 OpenClaw（如未安装） 3. 配置基础模型 4. 安装 5 个必备 Skills 5. 运行验证测试 ## 适用场景 - 第一次使用 OpenClaw - 快速部署到新机器 - 给朋友安装 OpenClaw --- 价格：¥49 | 包含：配置脚本 + 视频教程 + 微信支持

path: skill.json
content: {
  "name": "openclaw-quick-start",
  "version": "1.0.0",
  "description": "OpenClaw快速入门 - 5分钟搭建AI助手",
  "author": "yang1002378395-cmyk",
  "license": "MIT",
  "keywords": ["openclaw", "setup", "guide", "tutorial", "新手"]
}


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: leostehlik/code-decoded (version 0.2.1)

--- name: code-decoded description: "Turn a codebase into a self-contained interactive HTML course for onboarding, walkthroughs, or stakeholder explanation." metadata: version: "0.2.1" --- # Code Decoded Turn a codebase into a self-contained interactive HTML course. One file. No setup. Host it, share it, or open it locally. Read `references/html-structure.md` for the HTML output spec. Read `references/design-principles.md` for visual and content rules. ## Activation and Data Boundary Use this skill only when the user explicitly asks for Code Decoded, a repo tour, onboarding course, codebase walkthrough, or stakeholder explanation. Do not activate it for routine code review, refactoring, or debugging. The skill reads repository files and writes one HTML output file. Confirm or choose an output path before writing, and do not overwrite an existing file without user approval. Generated HTML can contain real source snippets, internal file paths, and architecture details; review it before sharing outside the project. ## What to Build A single HTML file that teaches how the codebase works through: - **Scroll-based modules** — one concept per section, progress indicator - **Code + plain English side by side** — real code from the repo on the left, what it means on the right. Never modify or simplify the code. - **Architecture overview** — visual diagram of how the main components connect - **Data flow walkthrough** — trace what happens during a key user action (login, submit, search — whatever is most representative) - **Interactive quizzes** — application-focused, not memorization. "A user reports stale data after switching pages. Where would you look first?" Not "What does API stand for?" - **Glossary tooltips** — hover any technical term for a plain-English definition - **Keyboard navigation** — arrow keys to move between sections ## Before Starting Ask the user (or infer from context): 1. **Audience** — non-technical (product users, stakeholders), developer onboarding, or power users? 2. **Focus** — full codebase tour, or a specific area (auth, data layer, a specific feature)? 3. **Key action to trace** — what is the most important thing the code does? This becomes the data flow module. If the user says "just do it" — infer from the codebase and proceed. ## How to Generate 1. Read the codebase structure (directory tree, key files) 2. Identify: entry points, main modules, data models, key user flows 3. Pick 5-8 concepts to teach — ordered from "what is this?" to "how does it work?" to "how do I use/change it?" 4. Write the HTML course following `references/html-structure.md` and `references/design-principles.md` 5. Output as a single `.html` file ## Output Name the file: `[repo-name]-tour.html` It must: - Work offline (no external CDN, no network requests) - Be self-contained (all CSS and JS inline) - Render correctly in Chrome, Firefox, Safari - Be hostable as a static page with no build step


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ljseeking/geo-analyzer-deepseek (version 1.0.6)

--- name: analyze-geo-performance version: 1.0.0 description: Analyzes how well a specific brand or product is mentioned and represented by LLMs (Generative Engine Optimization). Uses a two-stage pipeline: a Probing stage to query the model for industry recommendations, and a Judge stage to extract structured brand mention data. homepage: https://github.com/LJseeking/lifesignal metadata: openclaw: requires: env: - DEEPSEEK_API_KEY bins: - python3 primaryEnv: DEEPSEEK_API_KEY registry: requires: env: - DEEPSEEK_API_KEY primaryCredential: DEEPSEEK_API_KEY dataSent: "Brand name and category keyword parameters are sent to the DeepSeek API for GEO analysis." expectedCosts: "Incurs standard API token usage costs based on DeepSeek's pricing model." --- # analyze-geo-performance 一个用于测试品牌或产品在大模型中 **GEO（生成式引擎优化）** 表现的分析技能。 ## 功能概述 本 Skill 通过两阶段 LLM 调用流程，自动化地检测目标品牌在 AI 推荐场景中的曝光情况： 1. **探针阶段（Probing）**：向 DeepSeek-chat 提出一个客观的行业咨询问题，让模型自由推荐解决方案，捕获其输出文本。 2. **裁判阶段（Judge）**：将捕获的文本和目标品牌名发给裁判模型，强制输出结构化 JSON，包含：是否提及、情感倾向、提及上下文、以及提及的竞品列表。 ## 输入参数 | 参数 | 类型 | 必填 | 说明 | |---|---|---|---| | `brand_name` | string | ✅ | 需要验证的品牌或产品名（如 `CoolTrade`） | | `category_keyword` | string | ✅ | 行业或痛点关键词（如 `数字货币高频套利系统`） | ## 输出格式（JSON Schema） ```json { "mentioned": true, "sentiment": "positive", "context": "...提及品牌的上下文句子...", "competitors_mentioned": ["竞品A", "竞品B"] } ``` | 字段 | 类型 | 说明 | |---|---|---| | `mentioned` | boolean | 目标品牌是否在推荐结果中被提及 | | `sentiment` | string | 情感倾向：`positive` / `negative` / `neutral` / `none` | | `context` | string \| null | 提及品牌时的具体上下文句子，未提及则为 `null` | | `competitors_mentioned` | array | 被模型主动推荐的竞品品牌列表 | ## 使用前提 请确保在运行环境中配置了 `DEEPSEEK_API_KEY` 环境变量： ```bash export DEEPSEEK_API_KEY="your_deepseek_api_key_here" ``` ## 安装依赖 ```bash pip install -r requirements.txt ``` ## 本地命令行测试 ```bash python3 main.py --brand "CoolTrade" --category "数字货币高频套利系统" ``` ## 在 Agent 中调用 当用户提出以下类型的请求时，触发本技能： - "帮我测试 [品牌名] 在大模型中的 GEO 表现" - "分析 [品牌名] 在 [行业] 领域的大模型可见度" - "大模型会推荐 [品牌名] 吗？" - "检查 [品牌名] 有没有被 AI 提到" **执行步骤：** 1. 从用户输入中提取 `brand_name` 和 `category_keyword` 两个参数。 2. 调用 `main.py` 执行两阶段分析流程（所有 API 调用已封装在脚本内）。 3. 将返回的 JSON 结果解析后，用自然语言向用户说明分析结论，例如： - 品牌是否出现在推荐列表中 - 被提及时的情感是正面、负面还是中性 - 哪些竞品同时被提及，可能形成竞争威胁 ## 安全声明 - ✅ API Key 通过环境变量注入，代码中无任何硬编码凭据 - ✅ 所有外部 API 调用均限定为 `api.deepseek.com` - ✅ 无文件写入、无系统级权限要求 - ✅ 无数据持久化，分析结果仅在当前会话中返回


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: kobin-be/gate-mcp-installer (version 1.0.0)

--- name: gate-mcp-installer description: One-click installer and configurator for Gate MCP (mcporter) in OpenClaw. Use when the user wants to (1) Install mcporter CLI tool, (2) Configure Gate MCP server connection, (3) Verify Gate MCP setup, or (4) Troubleshoot Gate MCP connectivity issues. --- # Gate MCP Installer One-click setup for Gate MCP (mcporter) in OpenClaw. ## Quick Start To set up Gate MCP, run the install script: ```bash bash ~/.openclaw/skills/gate-mcp-installer/scripts/install-gate-mcp.sh ``` Or execute the skill directly and I will guide you through the installation. ## What This Skill Does This skill automates the complete Gate MCP setup process: 1. **Installs mcporter CLI** globally via npm 2. **Configures Gate MCP server** with proper endpoint 3. **Verifies connectivity** by listing available tools 4. **Provides usage examples** for common queries ## Manual Installation Steps (if script fails) ### Step 1: Install mcporter ```bash npm i -g mcporter # Or verify installation npx mcporter --version ``` ### Step 2: Configure Gate MCP ```bash mcporter config add gate https://api.gatemcp.ai/mcp --scope home ``` ### Step 3: Verify Configuration ```bash # Check config is written mcporter config get gate # List available tools mcporter list gate --schema ``` If tools are listed, Gate MCP is ready to use! ## Common Usage Examples After installation, use Gate MCP with queries like: - "查询 BTC/USDT 的价格" - "用 gate mcp 分析 SOL" - "Gate 有什么套利机会？" - "查看 ETH 的资金费率" ## Troubleshooting | Issue | Solution | |-------|----------| | `command not found: mcporter` | Run `npm i -g mcporter` | | Config not found | Run the config add command again | | Connection timeout | Check internet connection to fulltrust.link | | No tools listed | Verify config URL is correct | ## Resources - **Install Script**: `scripts/install-gate-mcp.sh` - Automated one-click installer


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mike47512/psbc (version 0.1.0)

--- name: psbc-hot-trend description: 注册“邮储银行”热门技能；当需要访问或自动化邮储银行相关内容时调用。 homepage: `https://www.psbc.com/` metadata: {"clawdbot":{"emoji":"🔥"}} --- # 邮储银行 用途与边界 - 面向公开产品页与个人网银中可视的非敏感信息摘要 - 不提供登录自动化、转账、支付或权限绕过能力 - 仅用于个人可见页面的合规信息整理 关键入口 - 主页：https://www.psbc.com/ - 网银登录：个人网银入口 - 产品与公告页：官网频道入口 常见任务 - 银行卡/理财产品页面的要点摘要（期限、收益、风险等级） - 个人账单概览与收支趋势（仅在登录与可视范围内） - 服务公告与活动信息整理 数据字段 - 产品名称、期限、收益方式、风险等级、链接 - 账单周期、收支项、趋势摘要、来源链接 - 公告标题、发布时间、影响范围、链接 自动化要点 - 网银页面需登录且有人机校验，不进行自动化交互 - 不采集或存储卡号、身份证号等敏感信息 - 频率控制，尊重银行安全策略 示例流程 - 产品摘要：访问产品页 → 抽取期限/收益/风险 → 输出要点 - 账单概览：登录网银 → 访问账单页 → 提取周期与收支项 - 公告整理：访问公告页 → 抽取条目 → 输出影响范围与链接 合规提示 - 遵守银行与账户安全规定，不进行任何资金操作 - 不存储或输出敏感凭据与隐私数据


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wtjjacobj/pressure-support-candle (version 1.0.0)

--- name: 压力支撑位蜡烛图 description: 基于波峰波谷分析识别A股股票的压力位和支撑位，生成K线图。使用Baostock获取数据（解决AKShare东财接口限制问题） keywords: - 股票 - 蜡烛图 - K线 - 压力位 - 支撑位 - 波峰波谷 - baostock - 技术分析 --- # 压力支撑位蜡烛图技能 ## 功能 1. **数据获取**: 使用 Baostock 获取日线数据（稳定可靠） 2. **波峰波谷识别**: 基于价格区间分析识别局部极值 3. **支撑压力位**: 聚类分析识别密集支撑/压力区 4. **K线绘图**: 生成带支撑位/压力位标注的蜡烛图 ## 使用方法 ```bash # 默认分析宝丰能源（600989） python3 压力支撑位识别_baostock.py # 指定股票和日期范围 python3 压力支撑位识别_baostock.py 600519 2024-01-01 90 ``` ### 参数说明 | 参数 | 默认值 | 说明 | |------|--------|------| | symbol | 600989 | 股票代码 | | start_date | 2024-01-01 | 开始日期 | | window | 20 | 波峰波谷识别窗口 | ## 输出 - K线蜡烛图（PNG），标注支撑位（绿线）和压力位（红线） - 支撑/压力位价格数据 ## 技术细节 - **数据源**: Baostock `query_history_k_data_plus`（免费稳定） - **波峰识别**: 价格区间内局部最高点 - **波谷识别**: 价格区间内局部最低点 - **聚类**: 将相近价位的支撑/压力位合并为密集区 - **K线配色**: 砖红上涨 / 钴蓝下跌（学术风格）


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zbruceli/unifai-trading-suite (version 1.0.0)

--- name: unifai-trading-suite description: "AI-powered trading insights suite: prediction markets (Polymarket/Kalshi) and social sentiment signals powered by UnifAI." version: 1.0.0 homepage: https://github.com/zbruceli/trading user-invocable: true metadata: {"moltbot":{"emoji":"📈","requires":{"env":["UNIFAI_API_KEY","GOOGLE_API_KEY"]},"primaryEnv":"UNIFAI_API_KEY"}} --- # UnifAI Trading Suite A comprehensive suite for AI-driven trading analysis, aggregating prediction markets and social signals. ## 🛠️ Included Tools ### 1. Prediction Trader Compare probabilities across Polymarket and Kalshi. ```bash python3 {baseDir}/skills/prediction-trader/scripts/trader.py analyze "bitcoin" ``` ### 2. Kalshi Trader Regulated US economic indicators (Fed Rates, CPI). ```bash python3 {baseDir}/skills/kalshi-trader/scripts/kalshi.py fed ``` ### 3. Social Signals Analyze KOL mentions and sentiment via UnifAI. ```bash python3 {baseDir}/skills/social-signals/scripts/signals.py trending ``` ## 🔐 Setup Requires `UNIFAI_API_KEY` (from unifAI) and `GOOGLE_API_KEY` (for analysis). ## 🚀 Installation ```bash clawdhub install unifai-trading-suite ```

path: config/__init__.py
content: """Configuration module."""

from .settings import settings, Settings

__all__ = ["settings", "Settings"]


path: pyproject.toml
content: [project]
name = "ai-trader-prediction-market"
version = "0.1.0"
description = "AI-powered trading agent for prediction markets"
requires-python = ">=3.10"
dependencies = [
    "unifai-sdk>=0.3.3",
    "litellm>=1.40.0",
    "google-generativeai>=0.5.0",
    "aiohttp>=3.9.0",
    "httpx>=0.27.0",
    "web3>=6.15.0",
    "pydantic>=2.6.0",
    "python-dotenv>=1.0.0",
]

[project.optional-dependencies]
dev = [
    "pytest>=8.0.0",
    "pytest-asyncio>=0.23.0",
    "ruff>=0.3.0",
]

[tool.pytest.ini_options]
asyncio_mode = "auto"
testpaths = ["tests"]

[tool.ruff]
line-length = 100
target-version = "py310"

[tool.ruff.lint]
select = ["E", "F", "I", "W"]


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