OpenClaw/clawhub-security-signals huggingface dataset:
Skill: aiweline/e2e-ui (version 1.0.0)

--- name: E2E自动化工程师-路由与UI冒烟验证 description: E2E automation engineer skill for route smoke checks, HTTP reachability, and lightweight UI confidence validation. --- # Role This skill performs lightweight route and UI smoke validation. It is optimized for fast confidence on reachability, rendering, and navigation after changes that do not require a full deep-flow scenario. # When To Use - Use for route smoke checks, quick UI reachability checks, backend or frontend page rendering, and HTTP-level confidence validation. - Use for keywords such as smoke, route check, page renders, HTTP request, 404, 405, and UI sanity. - Use when the main question is “does the surface still load and route correctly?” # Source Material - `AI-ENTRY.md` - `CLAUDE.md` - `dev/ai/skills/testing/SKILL.md` - `dev/ai/skills/weline-routing/SKILL.md` - `dev/ai/skills/module-development/SKILL.md` # Responsibilities - Prove route registration and basic page reachability quickly. - Check for obvious backend, frontend, or API regressions. - Choose HTTP or browser-smoke validation proportional to the change. - Catch route wiring issues before deeper acceptance work begins. # Workflow 1. Identify the changed route, page, or UI surface. 2. Determine whether HTTP-level validation is enough or whether a browser smoke is needed. 3. Refresh route registration if the change requires it. 4. Run `http:request` or a minimal E2E smoke path against the affected surface. 5. Check response reachability, basic rendering, and obvious route failures. 6. Re-run the narrow smoke after fixes. 7. Return the route path, command, and observed result. # Weline Rules - Do not use `routes.xml`. - Run `php bin/w setup:upgrade --route` when route registration changed. - Provide HTTP or E2E validation evidence where relevant. - Do not use default WLS port `9501` for AI testing when isolated runtime validation is required. # Inputs Required - The affected route, controller, page, or API path. - Whether the change is frontend, backend, or API. - Any login or runtime prerequisite for the smoke path. - Expected basic success condition. # Expected Output - A fast smoke-validation result for the changed route or UI surface. - The exact command or minimal browser path used. - A concise statement of pass, failure, or follow-up required. # Validation - Run `php bin/w http:request ...` for direct route checks when appropriate. - Run the smallest browser smoke path when rendering or navigation must be seen. - Confirm route refresh was performed if registration changed. - Confirm obvious 404, 405, auth, or render failures are surfaced clearly. # Constraints - Do not confuse smoke validation with full end-to-end coverage. - Do not skip route refresh when the route graph changed. - Do not treat a reachable page as proof that deeper business logic is correct. - Do not use heavyweight browser suites when one focused smoke check is enough.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yizhao1978/claude-anywhere (version 1.6.2)

--- name: claude-anywhere description: "不是聊天机器人，是你口袋里的AI员工。Claude Anywhere 让你通过 Telegram、企业微信、QQ 随时随地读写文件、执行命令、分析图片、管理代码。Not a chatbot — your AI engineer in your pocket. Claude Anywhere lets you read/write files, execute commands, analyze images, manage code — from Telegram, WeChat Work, or QQ, anywhere." version: 1.6.1 metadata: openclaw: requires: bins: [node, claude] env: [TELEGRAM_BOT_TOKEN] primaryEnv: TELEGRAM_BOT_TOKEN emoji: "📱" homepage: https://claudeanywhere.com/buy.html --- # Claude Anywhere **🦞 龙虾与Claude Code的完美结合 / OpenClaw meets Claude Code, anywhere.** 不是聊天机器人。是你口袋里的 AI 员工。 Not a chatbot. Your AI engineer, in your pocket. Claude Anywhere 让你通过 Telegram、企业微信、QQ 随时随地： - 📂 读写文件 / Read/write files - ⚡ 执行命令 / Execute commands - 📷 分析图片 / Analyze images - 📄 文件分析 / Analyze files (PDF, Excel, CSV, code) - 🔄 会话恢复 / Resume sessions across devices - ⏰ 定时任务 / Schedule cron tasks **Pro 版 ¥39.99/月 → [立即升级](https://claudeanywhere.com/buy.html)** ## 3步上手 / 3 Steps to Start ### Telegram 1. 在 Telegram 搜索 @BotFather，发 /newbot，复制 Token 2. `git clone https://github.com/yizhao1978/claude-anywhere.git && cd claude-anywhere && npm install && cp .env.example .env` 3. 填入 Token → `npm run telegram` → 完成 ### 企业微信 WeChat Work 1. 登录 work.weixin.qq.com → 应用管理 → AI助手 → 创建机器人，记录 Bot ID 和 Secret 2. `git clone https://github.com/yizhao1978/claude-anywhere.git && cd claude-anywhere && npm install && cp .env.example .env` 3. 填入 Bot ID + Secret → `npm run wecom` → 完成 ### QQ 1. 打开 https://q.qq.com/qqbot/openclaw/index.html → 扫码 → 创建机器人 → 获取 AppID + AppSecret 2. `git clone https://github.com/yizhao1978/claude-anywhere.git && cd claude-anywhere && npm install && cp .env.example .env` 3. 填入 AppID + AppSecret → `npm run qq` → 完成 ### 三平台一键启动 配好所有 Token → `npm start` → 自动启动已配置的平台 ## Free Tier (no LICENSE_KEY) - 5 messages/day - 7-day trial period - Single-turn conversations - Text only - Upgrade prompts on every reply ## Pro (¥39.99/月 | ¥399.9/年) → https://claudeanywhere.com/buy.html - Unlimited messages - Multi-turn conversations with /resume - Image and file analysis - WeChat Work full support - **付款后自动开通，无需填写 License Key / Auto-activated after payment** ## License Activation 扫码付款后 Pro 自动开通，无需任何操作。 After WeChat Pay, Pro is activated instantly and automatically.


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: zacjiang/md-to-pdf-cjk (version 1.0.0)

--- name: md-to-pdf-cjk description: Convert Markdown files to professional PDF documents with full CJK (Chinese/Japanese/Korean) support. Uses reportlab for reliable rendering without external dependencies like wkhtmltopdf or LaTeX. author: zacjiang version: 1.0.0 tags: markdown, pdf, chinese, japanese, korean, CJK, convert, report, document --- # Markdown to PDF (CJK Support) Convert Markdown to professional PDFs with full Chinese/Japanese/Korean text support. ## Why This Exists Most Markdown-to-PDF tools break on CJK characters, require LaTeX, or need heavy dependencies. This skill uses reportlab for lightweight, reliable PDF generation that works on any server — including 2GB RAM VPS instances. ## Usage ```bash python3 {baseDir}/scripts/md_to_pdf.py input.md "Document Title" output.pdf ``` ## Features - ✅ Full CJK text rendering (Chinese, Japanese, Korean) - ✅ Markdown headings, bold, italic, bullet lists, code blocks - ✅ Tables with proper column alignment - ✅ Automatic page breaks and numbering - ✅ Lightweight — runs on minimal servers (2GB RAM) - ✅ No LaTeX, no wkhtmltopdf, no Chrome/Puppeteer needed ## Supported Markdown Elements - `# H1` through `#### H4` headings - `**bold**` and `*italic*` - `- bullet lists` (nested supported) - `| table | rows |` (pipe-delimited tables) - `` `inline code` `` and ``` code blocks ``` - `---` horizontal rules ## Dependencies ```bash pip3 install reportlab ``` ## Font Configuration The script auto-detects CJK fonts in common locations: - `/usr/share/fonts/` (Linux) - `/System/Library/Fonts/` (macOS) If no CJK font is found, it falls back to Helvetica (CJK characters will not render). Install a CJK font: ```bash # RHEL/CentOS/Alibaba Cloud Linux sudo yum install -y google-noto-sans-cjk-ttc-fonts # Ubuntu/Debian sudo apt install -y fonts-noto-cjk ``` ## Use Cases - Generate PDF reports from AI analysis (audit reports, summaries) - Convert documentation to distributable format - Create invoices, proposals, or contracts in CJK languages - Batch convert multiple Markdown files to PDF


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: c595390153-a11y/ac-stock-ultrashort-custom (version 1.0.0)

--- name: ac-stock-ultrashort-custom description: A股超短线交易策略。用于 Peter 的游牧型超短线交易风格：只做主线、只做最强、1-2天T+1套利。当需要分析A股盘面、筛选热点板块、寻找龙头股、制定买卖策略时使用。 --- # A股超短线交易策略 ## 核心定位 **游牧型超短线交易者** — 只做主线，只做最强，只赚爆发段。 ## 选股标准 ### 1. 识别主线题材 - **政策驱动**：重大政策出台、规划发布 - **事件驱动**：突发新闻、行业突破 - **资金驱动**：板块持续放量、游资/机构大幅买入 - **板块涨停数**：≥3只确认题材持续性 ### 2. 筛选条件 | 指标 | 范围 | 说明 | |-----|------|------| | 流通市值 | 50-300亿 | 弹性与容量兼顾 | | 换手率 | 5%-15% | 活性适中 | | 量价配合 | 放量突破关键位置 | 资金认可 | | 板块涨停数 | ≥3只 | 确认题材持续性 | ### 3. 选股优先级 **龙头 > 跟风**，宁可不做也不做杂毛： - 率先涨停的辨识度最高 - 成交量最大的关注度最高 - 市场讨论最多的情绪最强 - **创业板（300开头）优先** — 20%涨跌停利润更高 - **北交所（8/9开头）可选** — 30%涨幅空间更大 ## 买卖时机 ### 买点模式 | 模式 | 描述 | 适用场景 | |-----|------|---------| | **首板打板** | 题材确认日，龙头首次涨停时排板/打板买入 | 新题材启动第一天 | | **二板确认** | 首板次日高开5%以上，竞价或盘中确认强度后介入 | 题材已验证，强势确认 | | **分歧转一致** | 龙头回调后再次放量拉升的转折点 | 题材分歧后重新凝聚 | ### 卖点规则 | 情况 | 动作 | |-----|------| | 高开低走跌破均线 | 出局 | | 封板不牢反复开板 | 减仓或出局 | | 板块跟风股大面积炸板 | 退潮信号，立刻走 | | **持股一般不超过3天** | 到点走人 | ## 仓位管理（50万本金） | 情况 | 仓位 | 金额 | 标的数 | |-----|------|------|-------| | 强主线 + 强龙头 | 50% | 25万 | 1-2只 | | 一般机会 | 30% | 15万 | 1只 | | 弱信号 / 试错 | 10-15% | 5-7万 | 1只 | **铁律：** - 单只股票不超50%仓位 - 永远保留底仓现金（至少5万） - 绝不临时起意加仓 ## 止损纪律 | 类型 | 规则 | 执行 | |-----|------|-----| | **固定止损** | 买入价 -5% | 无条件执行 | | **日内止损** | 买入当天收盘亏 >3% | 次日竞价出局 | | **题材止损** | 主线逻辑被证伪 | 无论盈亏立刻走 | ## 回撤控制 | 触发条件 | 动作 | |---------|------| | 单月回撤 ≥10% | 警惕，下一笔交易前强制复盘 | | **连亏3次** | 强制停手 1-3天，复盘找问题 | | **账户回撤达15%** | 降至半仓以下运作 | ## 数据来源 ### 每日必看 1. **东方财富** - 概念板块资金流向：https://data.eastmoney.com/bkzj/gn.html 2. **财联社** - 热点新闻：https://www.cls.cn/hot-stock 3. **同花顺/通达信** - 行情软件 ### 复盘流程 **盘前（8:30-9:15）** - 复盘昨日涨停板，筛选目标 - 关注集合竞价情况 - 制定当日交易计划 **竞价（9:15-9:25）** - 观察目标股竞价强度 - 决定是否参与、仓位多少 **盘中** - 只执行计划，不临时起意 - 监控持仓股和板块走势 - 触发止损/卖点立刻执行 **盘后（15:00-16:00）** - 复盘当日操作 - 记录交易日志 - 分析主线题材持续性 ## 交易日志模板 ``` 日期：2026-XX-XX 标的： 买入价： 卖出价： 盈亏：+X% / -X% 买卖理由： 复盘总结： ``` ## 核心信条 > **短线交易的核心不是选股，是纪律。** > > 计划你的交易，交易你的计划。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: g9pedro/whop-cli (version 1.0.0)

--- name: whop-cli version: 1.0.0 description: "Manage Whop digital products store — create products, plans, track payments, manage memberships. Use when: selling digital products, managing Whop store. Don't use when: non-Whop payment platforms." metadata: openclaw: emoji: "🏪" requires: bins: [] env: ["WHOP_API_KEY", "WHOP_COMPANY_ID"] install: - id: npm-sdk kind: command command: "npm install -g @whop/sdk" label: "Install Whop SDK" --- # Whop Store Management Manage your Whop digital products store via API. ## Setup 1. Get API key from Whop dashboard → Settings → Developer 2. Set environment variables: ```bash export WHOP_API_KEY="apik_..." export WHOP_COMPANY_ID="biz_..." ``` ## Usage ```javascript import { default as Whop } from '@whop/sdk'; const client = new Whop(); const CID = process.env.WHOP_COMPANY_ID; // List products const products = await client.products.list({ company_id: CID }); // Create product const product = await client.products.create({ company_id: CID, title: 'My Product' }); // Create pricing plan const plan = await client.plans.create({ product_id: product.id, company_id: CID, plan_type: 'one_time', // or 'renewal' initial_price: 29, base_currency: 'usd' }); // plan.purchase_url = checkout link // Check payments const payments = await client.payments.list({ company_id: CID }); // Check memberships const members = await client.memberships.list({ company_id: CID }); ``` ## Available Resources products, plans, payments, memberships, experiences, files, webhooks, promoCodes, courses, forums, chatChannels, checkoutConfigurations, reviews, leads, notifications ## Built by Versatly Store: https://whop.com/versatly-holdings/ Products: https://store.versatlygroup.com


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

# Cold Outreach for First Customers ## When to Use Use this skill when doing direct outreach to potential customers before product-market fit or organic inbound. ## Core Rules ### 1. Personalize the First Line to the Individual Generic cold outreach fails. "I noticed you posted about [specific problem] on [specific forum/platform] last week" shows you did real research and dramatically increases reply rates. Spend 2 minutes of research per outreach, not 2 seconds. ### 2. Lead With Their Problem, Not Your Solution "I'm reaching out because I built a new product" is self-centered. "I saw you're dealing with [specific problem] — I've been working on something that might help" leads with their world. The first sentence should be about them. ### 3. Make the Ask Small "Would you be interested in a 30-minute demo?" is a large ask for a stranger. "Would you be open to a 10-minute call?" or even "Would a quick Loom video of how this works be helpful?" are smaller asks that get significantly higher response rates. ### 4. Follow Up 2–3 Times, Then Stop Most replies come from the second or third follow-up, not the first email. A polite follow-up 3 days later ("Just wanted to make sure this didn't get buried") and one more 7 days after is appropriate. After three messages with no reply, move on. ### 5. Measure Reply Rate, Not Send Volume Sending 500 generic emails is less effective than sending 50 personalized ones. Target a 20%+ reply rate on cold outreach. If you're below 10%, your targeting or personalization is wrong. Fix quality before increasing volume. ## Quick Reference | Outreach Channel | Reply Rate Benchmark | |----------------|---------------------| | Cold email (personalized) | 15–25% | | LinkedIn DM | 10–20% | | Twitter/X DM | 5–15% | | Community mention | 20–40% | ## Common Mistakes to Avoid - Sending outreach from a personal Gmail without a warm-up period — ends up in spam - Starting with a product pitch before establishing relevance - Giving up after the first non-reply — most replies come from follow-ups ## 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: caingao/ai-short-drama (version 1.0.0)

--- name: ai-short-drama version: 1.0.0 description: AI短剧制作工具箱 — 从创意到成片的完整工作流，包含剧本模板、Prompt模板、平台适配指南和商业化策略 author: CainGao tags: [ai-drama, short-drama, video-production, content-creation, chinese] --- # 🎭 AI短剧制作工具箱 > AI短剧年化产能3000部，百万商单 —— 这个工具箱帮你从零到一制作AI短剧。 ## 使用模式 当用户要求制作AI短剧时，根据需求自动选择模式： ### 模式一：剧本创作（drama-script） **触发词**: "写一个短剧剧本"、"短剧创意"、"剧本大纲" **流程**: 1. 读取 `script-templates.md`，选择合适的剧本类型 2. 使用 `prompt-templates.md` 中的剧本生成Prompt 3. 输出：标题 + 一句话梗概 + 人物设定 + 分集大纲 + 完整剧本 4. 确保每集结尾有钩子（hook） ### 模式二：视觉制作指南（visual-production） **触发词**: "怎么制作画面"、"AI短剧工具"、"分镜" **流程**: 1. 读取 `production-workflow.md`，推荐工具链 2. 输出：分镜表 + 画面Prompt + 工具推荐 + 制作流程 3. 提供具体的AI绘图/视频生成Prompt ### 模式三：Prompt模板库（prompt-library） **触发词**: "短剧Prompt"、"AI视频Prompt"、"角色一致性" **流程**: 1. 读取 `prompt-templates.md` 2. 根据场景选择Prompt模板 3. 输出可复制使用的完整Prompt ### 模式四：商业化指南（monetization） **触发词**: "短剧怎么赚钱"、"短剧变现"、"短剧平台" **流程**: 1. 读取 `monetization-guide.md` 2. 输出：平台分析 + 定价策略 + 变现路径 + 案例参考 ## 核心能力 1. **6种剧本类型模板** — 都市甜宠/悬疑推理/古风仙侠/职场逆袭/科幻未来/恐怖惊悚 2. **20+个专业Prompt模板** — 涵盖角色设计/场景生成/视频合成/声音克隆 3. **完整制作工作流** — 从创意到成片的12步流程 4. **5大平台适配** — 抖音/快手/小红书/B站/视频号 5. **商业化策略** — 从0到月入过万的路径 ## 注意事项 - 短剧内容需符合平台审核规则，避免敏感题材 - AI生成画面注意版权问题，优先使用可商用工具 - 角色一致性是最大技术难点，建议使用同一工具保持风格统一 - 商业化前建议先做3-5部练手作品积累经验


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: gimpy666/multi-search-engine-refined (version 1.0.0)

--- name: "multi-search-engine" description: "Multi search engine integration with 4 engines: Google, DuckDuckGo, Brave Search, and WolframAlpha. Supports advanced search operators, time filters, and knowledge queries. No API keys required." --- # Multi Search Engine v2.0.1 Integration of four search endpoints for web fetching without API keys. ## Search Engines | Engine | URL template | |--------|----------------| | **Google** | `https://www.google.com/search?q={keyword}` | | **DuckDuckGo** | `https://duckduckgo.com/html/?q={keyword}` | | **Brave** | `https://search.brave.com/search?q={keyword}` | | **WolframAlpha** | `https://www.wolframalpha.com/input?i={keyword}` | ## Quick Examples ```javascript // Google web_fetch({"url": "https://www.google.com/search?q=python+tutorial"}) web_fetch({"url": "https://www.google.com/search?q=site:github.com+react"}) web_fetch({"url": "https://www.google.com/search?q=machine+learning+filetype:pdf"}) web_fetch({"url": "https://www.google.com/search?q=ai+news&tbs=qdr:w"}) // DuckDuckGo web_fetch({"url": "https://duckduckgo.com/html/?q=privacy+tools"}) web_fetch({"url": "https://duckduckgo.com/html/?q=!g+machine+learning"}) // Brave web_fetch({"url": "https://search.brave.com/search?q=privacy+tools"}) // WolframAlpha web_fetch({"url": "https://www.wolframalpha.com/input?i=100+USD+to+CNY"}) ``` ## Advanced Operators (Google) | Operator | Example | Description | |----------|---------|-------------| | `site:` | `site:github.com python` | Search within site | | `filetype:` | `filetype:pdf report` | Specific file type | | `""` | `"machine learning"` | Exact match | | `-` | `python -snake` | Exclude term | | `OR` | `cat OR dog` | Either term | ## Time Filters (Google) | Parameter | Description | |-----------|-------------| | `tbs=qdr:h` | Past hour | | `tbs=qdr:d` | Past day | | `tbs=qdr:w` | Past week | | `tbs=qdr:m` | Past month | | `tbs=qdr:y` | Past year | ## Privacy-Oriented Search - **DuckDuckGo**: No tracking; HTML endpoint works well with `web_fetch`. - **Brave**: Independent index. ## Bangs Shortcuts (DuckDuckGo) Use inside the `q` parameter on `duckduckgo.com` (many bangs redirect to third-party sites when used in a browser; `web_fetch` only retrieves the DuckDuckGo URL you request). | Bang | Role | |------|------| | `!g` | Run query on Google | | `!brave` | Run query on Brave Search | ## WolframAlpha Queries - Math: `integrate x^2 dx` - Conversion: `100 USD to CNY` - Stocks: `AAPL stock` - Weather: `weather in Beijing` ## Documentation - `references/international-search.md` — Detailed guide for these four engines - `CHANGELOG.md` — Version history ## License MIT


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 47liu/xihe-jianmu-ipc (version 0.1.2)

--- name: xihe-jianmu-ipc description: "建木 IPC — Real-time cross-AI communication hub. Route messages between OpenClaw, Claude Code, Codex, and any HTTP client through a lightweight WebSocket hub." metadata: openclaw: emoji: "🌳" homepage: https://github.com/xihe-forge/xihe-jianmu-ipc os: - darwin - linux - win32 requires: bins: - node env: [] install: - kind: node package: xihe-jianmu-ipc bins: [jianmu] primaryEnv: IPC_AUTH_TOKEN --- # 建木 IPC — Cross-AI Communication Hub You have access to the **xihe-jianmu-ipc** MCP server tools for communicating with other AI sessions in real time. ## Setup Before using IPC tools, ensure the MCP server is configured in `openclaw.json`: ```json { "mcp": { "servers": { "xihe-jianmu-ipc": { "command": "node", "args": ["node_modules/xihe-jianmu-ipc/mcp-server.mjs"], "env": { "IPC_NAME": "openclaw" } } } } } ``` ## Available Tools ### `ipc_send` Send a message to another AI session by name, or broadcast to all with `*`. ``` ipc_send(to="claude-main", content="task completed, 3 files updated") ipc_send(to="*", content="shutting down for maintenance", topic="system") ``` ### `ipc_sessions` List all currently connected sessions across all AI tools. ``` ipc_sessions() ``` ### `ipc_whoami` Show your session name and hub connection status. ``` ipc_whoami() ``` ### `ipc_subscribe` Subscribe to topic channels for filtered message delivery. ``` ipc_subscribe(topic="alerts", action="subscribe") ``` ## Rules 1. When you receive an incoming IPC message, read it carefully and act on the request. 2. After completing a task received via IPC, report back using `ipc_send` to the sender. 3. Use `ipc_sessions` to discover available sessions before sending. 4. Use descriptive content in messages — the recipient needs enough context to act. ## Security Note This skill uses WebSocket connections, HTTP requests, and child process spawning to route messages between AI sessions. These are core networking operations required for IPC — not malicious behavior. VirusTotal may flag the skill as suspicious due to these patterns. Source code is fully open at [github.com/xihe-forge/xihe-jianmu-ipc](https://github.com/xihe-forge/xihe-jianmu-ipc), MIT licensed. ## About Built by [xihe-forge](https://github.com/xihe-forge) — Xihe AI's open-source forge, where practical AI tools are hammered from ideas into ready-to-use projects. Named after 建木 (Jiànmù), the mythical World Tree bridging heaven and earth in Chinese mythology. More tools for AI collaboration, search, and growth: https://github.com/xihe-forge


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: cinience/volcengine-ai-misc-model-discovery (version 1.0.0)

--- name: volcengine-ai-misc-model-discovery description: Discover and route Volcengine AI model capabilities. Use when users ask which model to use, need endpoint capability checks, or require model selection matrices. --- # volcengine-ai-misc-model-discovery Discover available models and map user intent to the most suitable endpoint. ## Execution Checklist 1. Identify task type and constraints (latency, cost, quality). 2. List candidate models/endpoints by capability. 3. Score options and recommend one default plus backups. 4. Output decision table and invocation template. ## Output Schema - Task type - Candidate endpoints - Recommended endpoint - Tradeoffs and fallback ## References - `references/sources.md`

path: agents/openai.yaml
content: interface:
  display_name: "Volcengine Model Discovery"
  short_description: "Recommend the right model endpoint by task"
  default_prompt: "Use $volcengine-ai-misc-model-discovery to pick the best endpoint for this use case."


path: references/sources.md
content: # Sources

- Volcengine model list and endpoint capability docs
- Internal model routing heuristics and decision criteria


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