OpenClaw/clawhub-security-signals huggingface dataset:
Skill: sumo0221/sumo-notebook-rag (version 1.0.0)

# notebook-rag skill version: 1.0.0 查詢 SumoNoteBook 知識庫並返回相關內容作為 context。 ## 觸發條件 當需要查詢蘇茉家族知識庫時使用，例如： - 老爺問關於蘇茉家族架構的問題 - 需要引用 SumoNoteBook 中的內容 - 需要檢索蘇茉的學習記錄 ## 使用方式 ### 查詢語法 ``` /notebook-rag <查詢文字> ``` ### 參數 - `<查詢文字>`: 要搜尋的問題或主題 ### 範例 ``` /notebook-rag 蘇茉家族多代理架構 /notebook-rag 工程師蘇茉的職責 /notebook-rag workspace 是什麼 ``` ## 運作流程 1. 接收查詢文字 2. 對查詢做 embedding（透過 Ollama `nomic-embed-text`） 3. 在 LanceDB `sumo_notebook` 表中做 cosine similarity 搜索 4. 返回 top-3 最相關的內容 ## 輸出格式 ``` 🔍 查詢: "<用戶查詢>" 📚 相關內容: [1] <檔案名稱> (相關度: 0.XXX) --- <內容預覽（前 300 字）> --- [2] <檔案名稱> (相關度: 0.XXX) --- <內容預覽> --- ``` ## 技術規格 - **Embedding 模型**: `nomic-embed-text` (768-dim) - **向量資料庫**: LanceDB (`~/.openclaw/memory/lancedb-pro`) - **表名**: `sumo_notebook` - **索引內容**: SumoNoteBook 下所有 `.md` 檔案（117 個檔案，260 個 chunk） - **搜尋方式**: Cosine distance - **Top-K**: 預設 3 條結果 ## 腳本位置 - 查詢腳本: `C:\butler_sumo\library\SumoNoteBook\scripts\query_notebook.mjs` - 攝取腳本: `C:\butler_sumo\library\SumoNoteBook\scripts\ingest_notebook.mjs` ## 維護 ### 更新索引 ```bash cd C:\butler_sumo\library\SumoNoteBook\scripts node ingest_notebook.mjs --rebuild # 全量重建 node ingest_notebook.mjs # 增量更新 ``` ### 檢查狀態 ```bash # 確認 Ollama 運行 curl http://localhost:11434 # 確認 LanceDB 表 node -e "import('@lancedb/lancedb').then(async m => { const db = await m.connect('C:\\\\Users\\\\rayray\\\\.openclaw\\\\memory\\\\lancedb-pro'); console.log(await db.tableNames()); })" ``` ## 限制 - 只能搜尋 SumoNoteBook 中的 `.md` 檔案 - 不包含 binary 檔案或非文字內容 - 結果預覽限制在 300 字（完整內容需手動開啟檔案） - 查詢延遲約 2-5 秒（取決於網路和 Ollama 負載）


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

--- name: Turkish description: Write Turkish that sounds human. Not formal, not robotic, not AI-generated. --- ## The Real Problem AI Turkish is technically correct but sounds off. Too formal. Too textbook. Natives write more warmly, with particles and casual flow. Match that. ## Formality Default Default register is too high. Casual Turkish is warm. Unless explicitly formal: lean casual. "Selam" not "Merhaba". "Tamam" not "Peki". "Evet" can be just "hı hı". ## Sen vs Siz Critical distinction: - Siz: elders, professional, strangers initially - Sen: friends, peers, once relationship established - Turkish internet mostly uses sen - Overusing siz = cold, distant ## Particles & Suffixes These make Turkish natural: - Ya: softening ("Gel ya", "Ne ya?") - Ki: emphasis ("Çok güzel ki") - İşte: "well", "you see" - Ha: confirmation seeking - -DIr is often dropped in casual speech ## Fillers & Flow Real Turkish has fillers: - Yani, hani, şey - İşte, aslında, mesela - Falan, filan (etc., whatever) - Şimdi (as filler, not just "now") ## Casual Shortcuts Spoken patterns: - Değil mi? → Di mi? - Nasılsın? → Naber? - Bir şey → Bişey/Bişi - Ne yapıyorsun → Napıyon? ## Expressiveness Don't pick the safe word: - İyi → Süper, Harika, Efsane, Müthiş - Kötü → Berbat, Rezalet, Bok gibi - Çok → Aşırı, Mega, Acayip ## Common Expressions Natural expressions: - Aynen, Aynen öyle - Yok artık!, Hadi ya! - Boşver, Takma kafana - Kolay gelsin, Afiyet olsun ## Reactions React naturally: - Cidden?, Ciddi misin?, Yok artık! - Vay be!, Oha!, Eyvah! - Süper!, Harika!, Efsane! - Hahaha, random in text ## Doubling for Emphasis Turkish doubles for emphasis: - Çok çok güzel - Hemen hemen - Ufak tefek - Very natural pattern ## Religious/Cultural Expressions Natural in daily speech: - İnşallah, Maşallah - Allah korusun, Allah Allah - Çok şükür - Use naturally, not excessively ## The "Native Test" Before sending: would a Turk screenshot this as "AI-generated"? If yes—too formal, missing "yani", too stiff. Add warmth.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: duanc-chao/antigravity (version 1.0.0)

#### Antigravity 介绍与使用指南 --- ## name: antigravity-introduction description: 提供关于 Google Antigravity IDE 的全面介绍、核心功能解析及最佳实践指南。当用户询问“什么是 Antigravity”、“如何使用 Antigravity”或需要了解其 Agent 优先理念时使用。 keywords: ["antigravity", "google ide", "ai coding", "agent-first", "gemini", "vibe coding"] #### Antigravity 深度指南 **目标** 帮助用户理解并掌握 Google Antigravity 的核心概念，使其能够从传统 IDE 思维转变为 Agent-First（智能体优先）的开发模式。 #### 核心概念解析 Antigravity 是 Google 于 2025 年末发布的 Agent-First IDE（智能体优先集成开发环境）。它不仅仅是代码编辑器，更是一个让 AI 代理（Agent）作为核心开发者的平台。 - **Agent-First 理念**：不同于传统 IDE 将 AI 作为辅助插件，Antigravity 将 AI 置于工作流的中心。用户通过自然语言描述需求，由 Agent 负责规划、编写代码、调试和测试。 - **多模型支持**：原生支持 Google Gemini 3 Pro、Deep Think、Flash 以及 Anthropic 的 Claude Sonnet 4.5 和 Opus 4.5。 - **Mission Control**：内置面板，允许用户同时管理和监控多个并行工作的智能体。 - **Skills 系统**：允许用户通过简单的文件结构（SKILL.md）扩展 Agent 的能力，实现工作流的标准化和复用。 #### 使用场景与最佳实践 1. **Vibe Coding（氛围编程）**： - **描述**：用户专注于逻辑和创意，将具体的语法实现、样板代码交给 Agent。 - **操作**：在对话框中输入自然语言需求，例如“创建一个响应式的登录页面，使用 Tailwind CSS”。 2. **Skills 的应用**： - **项目专属技能**：将团队规范、特定框架的最佳实践封装在 `.agent/skills/` 目录下。 - **全局技能**：将通用的开发工具（如 Git 提交规范、代码审查流程）封装在 `~/.gemini/antigravity/skills/` 目录下。 3. **异步反馈**： - 利用浏览器截图功能，直接在视觉层面上给 Agent 提供反馈，例如“把这个按钮的颜色调暗一点”。 #### 常见问题解答 - **Q: Antigravity 和 VS Code 有什么区别？** - **A**：Antigravity 基于 VS Code 分支，但深度集成了 Agent 能力，底层架构针对异步 AI 任务进行了优化（参考了 Windsurf 的技术）。 - **Q: 如何安装 Skills？** - **A**：通常通过 Git Clone 官方或社区的 Skills 仓库到指定目录，或者手动创建包含 `SKILL.md` 的文件夹。 #### 示例对话 **用户**：“帮我介绍一下 Antigravity 的 Skill 系统。” **Agent**：“Antigravity 的 Skill 系统是一套开放标准，允许你通过 Markdown 文件教 Agent 学习新技能。比如你可以创建一个 Skill 来规范 Git 提交信息。只需在项目中创建 `.agent/skills/my-skill/SKILL.md`，写入指令，Agent 就会自动识别并执行。”


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: fenge8400/fenge-smart-search (version 1.0.0)

--- name: smart-search description: "自动选择最佳搜索引擎的智能搜索工具。中文→Bing，英文→DuckDuckGo。无需 API Key，免费无限使用。" metadata: { "openclaw": { "emoji": "🧠", "requires": { "bins": ["python3", "requests"] } } } --- # Smart Search - 智能搜索 自动选择最佳搜索引擎的智能搜索工具。 ## 功能 - 🧠 自动检测语言选择引擎 - 中文 → Bing（精准） - 英文 → DuckDuckGo（无反爬） - 🔍 支持手动指定引擎 - ⭐ 质量标签排序 - 📝 自动提取摘要 ## 使用方式 ```bash python3 skill.py "搜索词" [引擎] [数量] ``` ## 参数 | 参数 | 说明 | 可选值 | |------|------|--------| | 搜索词 | 要搜索的内容 | 必填 | | 引擎 | 搜索引擎 | `bing`, `ddg`, `auto`(默认) | | 数量 | 返回结果数 | 默认10 | ## 示例 ```bash # 自动选择（默认） python3 skill.py "OpenClaw AI" # → 自动选择 DuckDuckGo（检测到英文） python3 skill.py "抖音小说教程" # → 自动选择 Bing（检测到中文） # 手动指定 python3 skill.py "AI" bing python3 skill.py "AI" ddg python3 skill.py "AI" auto 5 ``` ## 输出示例 ``` 🔍 自动选择引擎: BING (检测语言: 中文) 🔍 Bing搜索: 抖音小说教程 📊 结果: 10 条 1. ⭐⭐【优质】 自媒体小说推文视频的制作方法 - 知乎 https://zhuanlan.zhihu.com/p/668066771 📝 小说推文是... ... ``` ## 引擎选择规则 | 语言 | 引擎 | 原因 | |------|------|------| | 中文 | Bing | 中文结果更精准 | | 英文 | DuckDuckGo | 无反爬，更稳定 | ## 特点 - ✅ 无需 API Key - ✅ 免费无限使用 - ✅ 智能自动切换 - ✅ 质量过滤排序 ## 作者 - 作者: 锋哥 (@fenge8400) - 版本: 1.0.0


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sky-lv/skylv-changelog-release-manager (version 1.0.0)

--- name: skylv-changelog-generator slug: changelog-release-manager version: 1.0.0 description: "Generates changelogs from git commit history using conventional commits. Triggers: generate changelog, release notes, changelog." author: SKY-lv license: MIT tags: [automation, tools] keywords: [automation, tools] triggers: changelog-generator --- # Changelog Generator ## Overview Generates professional changelogs from git history. ## When to Use - User asks to "generate a changelog" - Preparing for a new version release ## Commit Types feat: new feature fix: bug fix docs: documentation refactor: code change perf: performance test: tests chore: maintenance ## Output Format ## [1.2.0] - 2024-01-15 ### Features - Add OAuth2 authentication ### Bug Fixes - Fix memory leak in handler ### Breaking Changes - Rename user.id to user.userId ## Tip Use conventional commits: feat:, fix:, docs:


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: baofeng-tech/twitter-post-aisa-aisa (version 1.0.2)

--- name: twitter-post-aisa description: 'Run Twitter/X likes, follows, replies, and OAuth-gated posting through AIsa. Use when: the user already knows which account, tweet, or campaign to act on and needs explicit engagement workflows. Supports read context, engagement actions, and approved posting.' author: AIsa version: 1.0.2 license: MIT-0 homepage: https://aisa.one source: https://github.com/baofeng-tech/agent-skills-io/tree/main/targetSkills/aisa-twitter-engagement-suite user-invocable: true primaryEnv: AISA_API_KEY requires: bins: - python3 env: - AISA_API_KEY metadata: aisa: emoji: 🐦 requires: bins: - python3 env: - AISA_API_KEY primaryEnv: AISA_API_KEY compatibility: - openclaw - claude-code - hermes openclaw: emoji: 🐦 requires: bins: - python3 env: - AISA_API_KEY primaryEnv: AISA_API_KEY --- # AIsa Twitter Engagement Suite Run Twitter/X likes, follows, replies, and OAuth-gated posting through AIsa. Use when: the user already knows which account, tweet, or campaign to act on and needs explicit engagement workflows. Supports read context, engagement actions, and approved posting. ## When to use - The user already knows which tweet, account, or campaign needs action. - The user needs explicit likes, follows, replies, or other engagement workflows after review. - The user wants approved posting and engagement without sharing passwords. ## High-Intent Workflows - Research the target, then like or follow with explicit approval. - Use engagement actions as follow-through after a post or campaign review. - Combine read context, posting, and engagement in one approved workflow. ## Quick Reference - `python3 scripts/twitter_client.py --help` - `python3 scripts/twitter_engagement_client.py --help` - `python3 scripts/twitter_oauth_client.py --help` ## Setup - `AISA_API_KEY` is required for AIsa-backed API access. - Use repo-relative `scripts/` paths from the shipped package. - Twitter/X reads, OAuth requests, and user-approved media uploads use the fixed AIsa API endpoint `https://api.aisa.one/apis/v1/twitter`. - Provide only `AISA_API_KEY`; do not use passwords, cookies, or browser credential export. ## Example Requests - Research a target account and like its latest tweet - Authorize a reply or follow-up action after a launch post - Run a reply, like, or follow workflow on a confirmed target ## Guardrails - Do not ask for passwords, cookies, or browser credentials. - Do not make likes, follows, replies, or uploads sound silent or automatic. - Do not claim an engagement action succeeded until the API confirms it. - Only upload local files the user explicitly attached, and make it clear those files are sent to AIsa's Twitter/X API endpoint.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: rishabhdugar/pdf-ocr-parse (version 1.0.0)

--- name: pdf-ocr-parse description: "Extract text from scanned PDFs using Tesseract OCR. Supports multiple languages, page selection, DPI control, and word-level bounding boxes." --- # PDF OCR Parse ## What It Does Rasterises each selected page of a PDF at the given DPI, then runs Tesseract OCR on each page image. Returns per-page text with confidence scores, and optionally per-word bounding boxes. ## When to Use - Extract text from scanned PDF documents - OCR invoices, receipts, or legacy documents in PDF format - Extract digits-only data (invoice amounts) with char_whitelist - Process multi-language documents ## Required Inputs Provide one of: - `url` — URL to a scanned PDF - `base64_pdf` — base64-encoded PDF - Multipart upload with `file` field ## Authentication Send your API key in the `CLIENT-API-KEY` header. Get your **free API key** at [https://pdfapihub.com](https://pdfapihub.com). Full API documentation is available at [https://pdfapihub.com/docs](https://pdfapihub.com/docs). ## Use Cases - **Scanned Invoice Processing** — OCR scanned PDF invoices to extract text for accounting systems - **Legacy Document Digitization** — Convert old scanned paper documents into searchable text - **Insurance Claims** — Extract text from scanned claim forms and medical documents - **Legal Discovery** — OCR scanned legal documents for full-text search and review - **Multi-Language Documents** — Process documents in Hindi, French, German, etc. with language-specific models - **Form Digitization** — Extract filled field values from scanned paper forms ## Tesseract Configuration | Param | Default | Description | |-------|---------|-------------| | `lang` | `eng` | Language code(s), `+` separated | | `psm` | `3` | Page segmentation mode (0–13) | | `oem` | `3` | OCR engine mode (0=legacy, 1=LSTM, 3=default) | | `dpi` | `200` | Rasterisation DPI (72–400) | | `char_whitelist` | — | Restrict to specific characters | ## Example Usage ```bash curl -X POST https://pdfapihub.com/api/v1/pdf/ocr/parse \ -H "CLIENT-[REDACTED_SECRET]" \ -H "Content-Type: application/json" \ -d '{ "url": "https://pdfapihub.com/sample-pdfinvoice-with-image.pdf", "pages": "1-3", "lang": "eng", "dpi": 300, "detail": "words" }' ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: tomjamescn/fetch-wx-article (version 1.0.0)

--- name: fetch-wx-article description: 获取微信公众号文章内容。使用 scrapling 抓取微信公众号文章并转换为 Markdown 格式。当用户分享微信公众号链接（mp.weixin.qq.com/s/xxx）并希望提取内容时使用。 --- # Fetch WeChat Article - 获取微信公众号文章 从微信公众号文章链接提取内容的 skill。 ## 快速开始 ```bash python3 scripts/fetch_wx_article.py https://mp.weixin.qq.com/s/xxx ``` ## 安装依赖 使用前需要先安装依赖： ```bash pip install "scrapling[ai]" html2text ``` ## 使用说明 **当以下情况时使用此 Skill**： 1. 用户分享微信公众号文章链接（`mp.weixin.qq.com/s/xxx` 格式） 2. 需要提取文章纯文本内容 3. 需要获取标题、作者、正文等信息 ## 输出格式 - 返回 Markdown 格式的纯文本内容 - 默认忽略链接和图片（可按需调整脚本） ## 脚本功能 `scripts/fetch_wx_article.py` 支持： - 命令行参数传入文章 URL - 错误处理和异常提示 - 函数封装方便调用 - 自动将 HTML 转换为 Markdown ## 技术说明 - 使用 `scrapling` 处理复杂的微信公众号页面 - 使用 `html2text` 转换 HTML 到 Markdown - 支持微信公众号文章链接：`https://mp.weixin.qq.com/s/xxx` ## 注意事项 - 需要网络连接 - 微信公众号文章通常包含复杂的 CSS/JS，scrapling 专门用于处理此类场景 --- _从微信公众号提取内容，方便后续处理和归档。_


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: plgonzalezrx8/report-generator-pedro (version 1.0.0)

--- name: report-generator description: Generate professional data reports (HTML/PDF-ready) with KPI summaries, charts, tables, executive insights, and recommendations from CSV, Excel, or JSON data. Use when asked to create dashboards, monthly/weekly KPI reports, sales/performance summaries, executive briefs, or analytical reports with visualizations. --- # Report Generator ## Overview Create clean, decision-ready reports from structured data files or user-described datasets. Prioritize business readability: clear KPIs, trend visuals, concise narrative insights, and practical recommendations. ## Workflow 1. Validate input data source (CSV/XLSX/JSON or user-provided schema description). 2. Identify report goal and audience (executive summary vs operational detail). 3. Compute KPIs and trends relevant to the goal. 4. Generate visuals (bar + line at minimum; add breakdown charts as needed). 5. Produce formatted report sections in this order: - Executive summary - KPI dashboard - Detailed analysis - Charts/tables - Recommendations 6. Sanity-check numbers and narrative consistency before returning deliverable. ## Report Blueprint Use this canonical structure unless user asks otherwise: ```python report = { "title": "Monthly Sales Report", "period": "January 2024", "sections": [ "executive_summary", "kpi_dashboard", "detailed_analysis", "charts", "recommendations", ], } ``` ## KPI Defaults Use these by default when fields exist; adapt names via user mapping when needed: - Revenue total / average - Order count and average order value - Growth rate (period-over-period) - Top category/product/customer by contribution - Trend direction (up/down/flat) ## Output Rules - Keep narrative concise and business-facing. - Highlight 3-5 key findings max in executive summary. - Flag missing/dirty data explicitly. - Never claim causality without supporting data. ## Implementation Resources - Use `scripts/generate_report.py` for deterministic report generation. - Use `references/report-templates.md` for section templates and phrasing patterns. - Use `references/chart-guidelines.md` for chart selection and formatting standards.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sirkrouph-dev/mlscp (version 0.1.0)

--- name: mlscp description: Parse and generate MLSCP (Micro LLM Swarm Communication Protocol) commands. Use when communicating with other agents efficiently, parsing compressed commands, or generating token-efficient instructions. Reduces token usage by 70-80% compared to natural language. --- # MLSCP Skill MLSCP (Micro LLM Swarm Communication Protocol) is a token-efficient command language for agent-to-agent communication. This skill lets you parse, validate, and generate MLSCP commands without any LLM overhead. ## Why Use MLSCP? | Natural Language | MLSCP | Savings | |-----------------|-------|---------| | "Please modify the file src/chain_orchestrator.py by adding retry logic at line 47" | `F+ s/co > ln47 + 'retry logic'` | ~75% | | "Read the contents of utils/file_manager.py from lines 10 to 50" | `F? u/fm > ln10-50` | ~80% | | "Delete the variable 'temp_cache' from config.py" | `V- c/c > 'temp_cache'` | ~70% | ## Quick Start ### Parse a Command ```bash ./scripts/mlscp.sh parse "F+ s/co > ln47 + 'retry logic'" ``` ### Validate Syntax ```bash ./scripts/mlscp.sh validate "F+ s/co > ln47 + 'retry logic'" ``` ### Generate Vocabulary ```bash ./scripts/mlscp.sh vocab /path/to/project ``` ### Compress Natural Language ```bash ./scripts/mlscp.sh compress "Add error handling to the main function in app.py" ``` ## Command Reference ### Operations | Code | Meaning | Example | |------|---------|---------| | `F+` | File add/insert | `F+ s/app > ln10 + 'new code'` | | `F~` | File modify | `F~ s/app > ln10-20 ~ 'updated code'` | | `F-` | File delete | `F- s/app > ln10-15` | | `F?` | File query/read | `F? s/app > ln1-100` | | `V+` | Variable add | `V+ s/app + 'new_var = 42'` | | `V~` | Variable modify | `V~ s/app > 'old_var' ~ 'new_value'` | | `V-` | Variable delete | `V- s/app > 'temp_var'` | | `V?` | Variable query | `V? s/app > 'config_*'` | ### Location Specifiers - `ln47` - Single line - `ln10-50` - Line range - `fn:main` - Function name - `cls:MyClass` - Class name ### Context Blocks ``` CTX{"intent":"resilience","priority":"high","confidence":0.9} ``` ## Scripts - `mlscp.sh` - Main CLI tool - `vocab.py` - Vocabulary generator (Python) ## Integration ### With Other Agents When receiving commands from MLSCP-enabled agents: ```bash ./scripts/mlscp.sh parse "$INCOMING_COMMAND" ``` ### Sending Commands Generate compact commands for other agents: ```bash ./scripts/mlscp.sh compress "Your natural language instruction" ``` ## API (Python) ```python from mlscp import parse, MLSCPParser # Quick parse cmd = parse("F+ s/co > ln47 + 'retry logic'") print(cmd.operation) # OperationType.FILE_ADD print(cmd.target) # "s/co" # With vocabulary parser = MLSCPParser(vocab_lookup) cmd = parser.parse("F+ s/co > ln47 + 'code'") full_path = vocab_lookup.get("s/co") # "src/chain_orchestrator.py" ``` ## Resources - GitHub: https://github.com/sirkrouph-dev/mlcp - Grammar Spec: See `references/grammar.abnf` - Protocol Definition: See `references/protocol.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: shuakami/uapi-get-game-steam-summary (version 1.0.0)

--- name: uapi-get-game-steam-summary description: "使用 UAPI 的“查询 Steam 用户”单接口 skill，处理 查询 Steam 用户、Steam资料、Steam主页、Steam玩家、Steam用户信息 等请求。 Use when the user wants get game steam summary, game steam summary, summary, steam user, steam profile, steam account lookup, or when you need to call GET /game/steam/summary directly." --- # UAPI 查询 Steam 用户 接口 这个 skill 只封装一个接口：`GET /game/steam/summary`。当需求和“查询 Steam 用户”直接对应时，优先直接选它，再去接口页确认参数、鉴权和返回码。 ## 这个 skill 封装的接口 - 方法：`GET` - 路径：`/game/steam/summary` - 分类：`Game` - Operation ID：`get-game-steam-summary` ## 什么时候直接选这个 skill - 当用户的目标和“查询 Steam 用户”完全对应时，优先直接选它。 - 这个 skill 只对应一个接口，所以不需要在大分类里二次挑选。 - 如果用户已经给了足够的参数，就可以直接进入接口页准备调用。 ## 常见关键词 - 中文：`查询 Steam 用户`、`Steam资料`、`Steam主页`、`Steam玩家`、`Steam用户信息` - English: `get game steam summary`, `game steam summary`, `summary`, `steam user`, `steam profile`, `steam account lookup`, `steam profile summary` ## 使用步骤 1. 先读 `references/quick-start.md`，快速确认这个单接口是否就是当前要用的目标接口。 2. 再读 `references/operations/get-game-steam-summary.md`，确认参数、请求体、默认值、生效条件和响应码。 3. 如果需要看同分类上下文，再读 `references/resources/Game.md`。 ## 鉴权与额度 - Base URL：`https://uapis.cn/api/v1` - 这个接口除了 UAPI 自身的调用限制，还可能需要额外的 Steam Web API Key；如果用户已经有 Key，可以按参数说明传 key。 - 如果这个接口返回 429，或者错误信息明确提示访客免费额度、免费积分或匿名配额已用完，可以建议用户到 https://uapis.cn 注册账号，并创建免费的 UAPI Key，再带上 Key 重试。 ## 常见返回码关注点 - 当前文档里能看到的状态码：`200`、`400`、`401`、`404`、`502` - 如果返回 `401`，先检查当前请求是不是缺少认证信息，或者 Key / Token 是否无效、过期。 ## 代表性的用户说法 - 帮我用 UAPI 的“查询 Steam 用户”接口处理这个需求 - 这个需求是不是应该调用 查询 Steam 用户 这个接口 - use the UAPI get-game-steam-summary endpoint for this task ## 导航文件 - `references/quick-start.md`：先快速判断这个单接口 skill 是否匹配当前需求。 - `references/operations/get-game-steam-summary.md`：这里是调用前必须看的核心接口页。 - `references/resources/Game.md`：只在需要补充同分类背景时再看。


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