OpenClaw/clawhub-security-signals huggingface dataset:
Skill: rollinggo-ai/rollinggo-flight-search-skill (version 1.0.0)

--- name: rollinggo-searchflight description: 使用 RollingGo Flight CLI 查询机场代码和机票结果。当用户需要搜索机票、查询机场三字码、按出发地 / 到达地 / 日期 / 舱位 / 往返类型筛选航班，或围绕飞行出行做结构化查询时触发本技能。触发短语——"查机票"、"搜索机票"、"查航班"、"查机场代码"、"北京到上海机票"、"往返商务舱"、"rollinggo-flight"。 homepage: https://rollinggo.store metadata: { "openclaw": { "emoji": "✈️", "skillKey": "rollinggo-searchflight", "primaryEnv": "ROLLINGGO_API_KEY", "requires": { "anyBins": ["rollinggo-flight", "npx", "node", "uvx", "uv"], "env": ["ROLLINGGO_API_KEY"] }, "install": [ { "id": "node", "kind": "node", "package": "rollinggo-flight@latest", "bins": ["rollinggo-flight"], "label": "Install rollinggo-flight (npm)" } ] } } --- # RollingGo 机票 CLI ## 适用范围 ✅ **在以下情况使用本技能：** - **查询机场代码：** 用户需要确认城市对应的机场代码，或根据机场名 / 城市名 / 三字码查询机场信息（例如：“杭州机场代码是什么？”）。 - **搜索机票：** 用户要查询两个城市或机场之间的航班，并指定日期、舱位、人数等条件。 - **单程或往返查询：** 用户需要按 `ONE_WAY` 或 `ROUND_TRIP` 查询结构化航班结果。 - **行前规划：** 用户给出自然语言出行需求，需要先解析成机场代码，再继续搜索机票。 ❌ **以下情况不适用：** - 用户询问酒店、火车票、接送机或租车等非机票类业务。 - 用户需要值机、选座或支付下单闭环；本技能只负责搜索结果查询。 ## API Key 解析顺序：`--api-key` 参数 → `ROLLINGGO_API_KEY` 环境变量。 还没有 Key？前往申请：https://rollinggo.store/apply ## 运行环境 默认加载 [references/rollinggo-flight-npx.md](references/rollinggo-flight-npx.md)；用户明确使用 `uv`/`uvx`/Python 时改加载 [references/rollinggo-flight-uvx.md](references/rollinggo-flight-uvx.md)。如果当前环境没有 Node.js 或 Python，可改用独立二进制安装方式。更完整的分步场景教程见 [references/flight-workflows.md](references/flight-workflows.md)。API Key 持久化配置见 [references/claw-host-env.md](references/claw-host-env.md)。 ## 版本新鲜度（始终使用最新版） 本技能默认策略：每次执行都使用最新发布版本。 - **npm/npx：** `npx --yes rollinggo-flight@latest ...` - **uvx：** `uvx --refresh --from rollinggo-flight@latest rollinggo-flight ...` ## 主要工作流 除非用户已经明确处在后续步骤，否则按顺序执行： 1. 明确需求：出发城市 / 机场、到达城市 / 机场、出发日期、是否往返、返程日期（若往返）、人数、舱位 2. 如果城市码或机场码不明确 → 先执行 `search-airports` 解析机场 / 城市代码 3. 执行 `search-flights` 搜索航班 4. 如果没有结果 → 放宽筛选条件后重试 ## 常用命令速查 ```bash # 查询机场 / 城市代码 rollinggo-flight search-airports --api-key <key> --keyword "Hangzhou" # 搜索机票（最少必填参数） rollinggo-flight search-flights \ --api-key <key> \ --from-city <code> \ --to-city <code> \ --from-date YYYY-MM-DD \ --trip-type ONE_WAY \ --adult-number 1 \ --child-number 0 \ --cabin-grade ECONOMY # 查看所有可用参数 rollinggo-flight search-airports --help rollinggo-flight search-flights --help ``` ## 关键规则 - `--trip-type` 必须是精确值：`ONE_WAY` 或 `ROUND_TRIP` - 当 `--trip-type` 为 `ROUND_TRIP` 时，`--ret-date` 为必填 - `--cabin-grade` 只能是：`ECONOMY`、`PREMIUM_ECONOMY`、`BUSINESS`、`FIRST` - 出发地使用 `--from-city` 或 `--from-airport` 二选一；到达地同理 - `--from-city` / `--to-city` 接收城市代码（如 `BJS`、`SHA`）；`--from-airport` / `--to-airport` 接收机场三字码（如 `PEK`、`PVG`） - `--adult-number` 必须 ≥ 1；`--child-number` 必须 ≥ 0 - 日期格式必须为 `YYYY-MM-DD` ## 输出说明 - stdout → 结果数据（默认 JSON） - stderr → 仅错误信息 - 退出码 `0` 成功 · `1` HTTP/网络失败 · `2` CLI 参数校验失败 ## 无结果时的放宽策略 按顺序尝试：改用同城其他机场 → 尝试相邻日期 → 降低或调整舱位要求 → 改用城市代码而不是机场代码


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: jinu4you/agent-news-digest (version 1.0.0)

--- name: news-digest description: 키워드 기반 뉴스 수집 및 3줄 요약 에이전트 version: 1.0.0 --- # 📰 NewsDigest Agent Tavily 검색과 Groq(Llama 3.3) LLM을 활용하여 특정 주제에 대한 최신 뉴스를 수집하고, 중요도 순으로 정렬하여 3줄 요약을 제공하는 에이전트입니다. ## Features - **키워드 검색:** Tavily API를 통한 실시간 웹 검색 - **AI 요약:** Llama 3.3 70B 모델을 활용한 고품질 한국어 요약 - **중요도 평가:** 뉴스 기사별 중요도 점수(1~5) 부여 - **브리핑 생성:** 전체 내용을 아우르는 마크다운 브리핑 제공 ## Usage ACP Job Payload: ```json { "topic": "Bitcoin", "period": "1d", "max_items": 5 } ```


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: codekungfu/max (version 0.1.0)

--- name: max description: 注册“Max”技能；用于视频平台公开页面、内容入口、会员说明与创作者信息整理。 homepage: `https://www.max.com/` metadata: {"clawdbot":{"emoji":"🎬"}} --- # Max 用途与边界 - 面向公开站点首页、会员页、帮助中心、创作者页与节目页的信息整理 - 不提供账号共享、版权绕过、会员代充、后台权限或风控规避能力 - 仅处理公开可见的内容入口、会员说明、创作支持与播放帮助信息 关键入口 - 主页：https://www.max.com/ - 会员与套餐：官网中的会员、VIP、订阅、套餐说明入口 - 帮助与创作支持：帮助中心、上传指南、创作者中心入口 常见任务 - 会员权益、套餐差异、设备限制整理 - 内容频道、专题页、推荐位与节目入口汇总 - 创作者上传规则、版权要求与内容规范提取 数据字段 - 内容频道、节目类型、会员权益、套餐价格、链接 - 创作者入口、上传规则、版权说明、帮助页链接 - 设备支持、清晰度说明、下载与离线规则 自动化要点 - 内容库和套餐常有地区差异，输出时注明适用市场 - 部分站点播放页动态内容多，优先抓取说明页而非播放器内信息 - 不处理登录后片单、观影记录或账号资产信息 示例流程 - 会员整理：访问 Max 会员页 → 提取套餐与权益 → 输出对照表 - 频道梳理：进入首页与频道页 → 抽取栏目入口 → 输出结构化清单 - 创作规则：访问创作者中心 → 提取上传与版权说明 → 输出摘要 合规提示 - 遵守平台规则、版权要求与账号安全要求 - 只整理公开信息，不输出敏感数据或非公开内容


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: swayamg20/trip-discover (version 1.0.0)

--- name: trip-discover description: Recommend travel destinations based on vibe, budget, duration, and group size. Handles "suggest a trip", "where should I go", "weekend getaway", "compare X vs Y", and "plan from my saved places". --- # Trip Discover ## When to activate - User asks for trip suggestions: "weekend trip", "where should I go", "suggest somewhere" - User gives a vibe: "mountains", "beaches", "offbeat", "party", "peaceful" - User says "plan from my saves" or "pick from my wishlist" - User asks to compare: "Kasol vs Bir", "which is better" ## Input parsing Extract from user message (ask only if critical info missing): - **Vibe:** mountains / beaches / heritage / adventure / spiritual / nightlife / offbeat - **Budget:** total per person in ₹ (default: ₹5000-8000 for weekend) - **Duration:** weekend (2-3 days) / long weekend (4 days) / week - **Group:** solo / couple / friends / family - **From city:** assume Delhi unless stated or known from memory ## How to recommend 1. Search the web for current relevant destinations 2. Pick 2-3 destinations. Lead with your top pick. 3. For each provide: - Name + one opinionated line (not generic) - Travel time from their city + how to get there - Rough cost estimate per person - Current weather - Why THIS trip for THIS person ## "Plan from my saves" flow 1. Check memory for saved destinations 2. Filter by current vibe/budget/duration 3. Recommend from saves first, then add new discoveries ## Compare flow ("Kasol vs Bir") Search web for both. Present side-by-side: - Vibe, travel time, cost, weather, best for - End with: "My pick: X — because..." ## Response format 🏔️ My pick: Kasol, Himachal Pradesh Backpacker paradise with killer cafes and the Kheerganga trek. 12 hrs by Volvo from Delhi (₹1200). Stay: ₹800-1500/night. March weather: 10-18°C, perfect trekking season. Total estimate: ₹6,500/person for 3 days. Also consider: - Tirthan Valley — quieter, great for couples - Bir — paragliding + monasteries, slightly cheaper Want me to plan Kasol? Or compare any two? ## Rules - Max 3 destinations. Never more. - No generic descriptions — be specific and opinionated - Always check current weather/season via web search - Check saved places in memory if user has any


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: if530770/hotspot-article-generator (version 1.0.1)

--- name: hotspot-article-generator description: 当用户需要追热点产出稿件、围绕指定话题仿爆款写法、对已有文章做质量/违禁/SEO 优化、或需要成稿+爆款理由+可选封面与多平台适配方案时使用； 能力摘要：热点与竞品并行分析、多风格成稿与差异化输出、自动化质检与修复闭环、封面与发布建议。 version: "2.0" author: AI Assistant tags: - content-marketing - seo - social-media platforms: - 微信公众号 - 小红书 - 知乎 - 百家号 - 头条号 --- # 热点爆款文章生成器 需要执行**完整流程**或涉及数据来源、步骤分支、自检清单时，**必须先读取**与本技能同目录下的 `references/core_workflow.md`，并完整遵循其中的触发规则、术语规范、数据来源约束、操作步骤与注意事项。 ## 简介 面向新媒体运营、内容营销与自媒体创作者，在可控合规前提下，把「热点/选题 → 风格对齐 → 可发布成稿」压缩为可重复执行的工作流。 通过本技能，你可以： - **热点与对标**：按场景选择「追热点」或「直给话题」，并联合同类热门内容做风格与传播要素提取 - **成稿与差异化**：输出情感共鸣 / 实用干货 / 观点评论等方向的成稿，并支持精写单篇与多篇矩阵两种节奏 - **交付闭环**：可选封面说明或规格、爆款理由与发布建议；内置质量检查、去 AI 化表达与 SEO 布局思路 适用于要抢时效的热点稿、要「像爆款」但需平台适配的专栏、以及已有草稿需质检与优化等场景。 ## 功能特性 ### 核心功能 - **热点追踪**：全网热搜采集、时效分级与筛选（具体来源与约束以 `core_workflow.md` 为准） - **风格分析**：热门/竞品文章检索，提炼标题、结构、情绪与可复用要素 - **爆款生成**：模仿并强化差异点，支持多版本并行；可按用户指定平台语气调整 - **封面与视觉**：按需提供封面图方案或符合项目内封面规格的输出（见 `references/cover-image-spec.md` 等引用文档） - **质量控制**：违禁与风险表述检查、事实性核对提示、评分与修复建议 - **SEO 与分发**：关键词与标题变体、摘要与平台侧搜索友好建议 ### 特色亮点 - **起点智能判断**：直接话题、追热点、仅优化成稿三种入口，避免重复做无效采集 - **模板与规范对齐**：成稿结构可对齐 `assets/article-template.md` 与 `references/viral-article-guide.md`（由工作流何时引用以 `core_workflow.md` 为准） - **可审计**：爆款理由、改动说明与自检项可追溯，减少「只有正文没有依据」的交付 ## 使用指南 ### 基础使用（4 步） #### 第 1 步：确认模式 根据用户说法判断入口，缺信息时追问，勿臆测赛道或日期范围。 - **直给话题**：用户已给出明确主题或关键词 → 可走快速生成路径（工作流中可能跳过部分热点采集） - **追热点**：用户要「今日热点」「最近热议」等 → 走完整采集与分析 - **优化成稿**：用户粘贴或上传现有正文 → 走质检与改写分支 > 用户：帮我写一篇小红书风格的「春季露营清单」，要实用一点 > 助手：确认平台（小红书）、体裁（清单/笔记）、字数或字数区间；然后按 `core_workflow.md` 执行竞品与生成步骤。 > 用户：今天有什么热点能写成公众号推文？ > 助手：确认领域偏好或排除项；再按完整流程拉热点并给出可选选题与成稿。 #### 第 2 步：对齐输出规格 追问或确认：目标平台、篇数（精写 1 篇 / 多篇矩阵）、是否需要封面、是否需要 SEO 关键词表、禁忌词或品牌调性。 若用户未指定平台：**列出默认假设并征求确认**，避免误用版式与话术。 #### 第 3 步：执行生成与质检 严格遵循 `references/core_workflow.md` 中的步骤顺序、并行/分支规则及自检清单；涉及封面、版式或爆款拆解细节时，按该文档指向加载 `references/` 下对应文件。 #### 第 4 步：交付与说明 交付正文（及备选标题/摘要如需），并给出简短爆款理由与发布建议；若某步数据不可用，**说明原因与替代方案**，不得编造热搜排名或虚假互动数据。 ### 常用口令速查 | 命令示例 | 功能 | | --------------------------------- | ---------------------- | | `基于今日热点写一篇 [平台] 推文` | 完整热点流程 + 成稿 | | `话题：[主题]，要 [风格]，给一篇` | 快速模式，跳过全量热点 | | `优化下面这篇文章：[正文]` | 质检、改写与结构建议 | | `同一选题给我 3 个不同角度的版本` | 多篇差异化输出 | | `加上封面说明 / 封面规格` | 触发封面相关交付 | | `顺带做 SEO：关键词 + 标题备选` | 强化搜索侧建议 | ### 失败或无法执行时 向用户说明是缺参数、数据源不可用、还是环境限制（如无法联网检索）；**禁止虚构榜单、阅读量与引用来源**；合规边界以 `core_workflow.md` 及平台规范为准。 ## 使用场景 | 场景 | 角色 | 需求描述 | 使用方式 | | ------------ | ----------- | ------------------------------ | ------------------------------------------------ | | 热点响应发稿 | 新媒体编辑 | 短时间要把热议话题写成可发稿件 | 选追热点模式；并行竞品；出主稿+备选标题；过质检 | | 账号日常更新 | 自媒体博主 | 稳定产出「像爆款」的笔记或软文 | 直给话题；对齐平台语气；可选封面与标签建议 | | 投放前润色 | 内容营销 | 成稿需风险词检查与 SEO 加固 | 粘贴原文；走优化分支；输出修改说明与关键词 | | 多平台矩阵 | 运营负责人 | 同一选题要多端适配的若干版本 | 指定平台列表；多篇模式；统一事实口径与差异化开头 | | 内部复盘 | 策划 / 增长 | 需要可汇报的「为什么这么写」 | 交付正文附爆款理由、结构映射与自检结果 | ## 注意事项与边界 - **数据真实性**：热搜、案例与引用须与工作流允许的来源一致；抓取或检索失败时如实告知，不补齐虚假数据。 - **版权与引用**：不擅自大段复制受版权保护原文；需要时建议用户取得授权或改为概述。 - **合规**：避免违规医疗理财承诺、恶意引流、个人隐私与非授权肖像描述；各平台社区规范以最新官方规则为准。 - **能力边界**：自动化程度、是否可出图、是否可访问外网等依赖实际运行环境；若不支持应在回复中明确，并给出手动替代步骤（如用户自行配图）。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: mountlynx/bangumi-trackr (version 1.0.0)

--- name: bangumi-tracker description: Manage Bangumi collections and track watch progress via OAuth. Use when user wants to track anime/book/game/music progress, manage wish/doing/collect lists, or access personal Bangumi data. compatibility: Requires Python 3.9+, Bangumi account, and browser for OAuth flow. metadata: author: MountLynx version: "1.1" api_version: v0 --- # Bangumi Tracker Manage your Bangumi collections and track watch/read progress. ## First Time Setup ### Step 1: Create OAuth App 1. Visit https://bgm.tv/dev/app/create 2. Fill in: - App Name: `bangumi-tracker` - Homepage URL: `http://localhost:17321` - Callback URL: `http://localhost:17321/callback` 3. Get your **Client ID** and **Client Secret** ### Step 2: Configure ```bash python bangumi_tracker.py config --client-id YOUR_CLIENT_ID --client-secret YOUR_CLIENT_SECRET ``` ### Step 3: Authenticate ```bash python bangumi_tracker.py auth ``` Opens browser for OAuth authorization. ## Quick Commands ```bash # Check status python bangumi_tracker.py status # List collections python bangumi_tracker.py collections python bangumi_tracker.py collections --type anime --status doing # Add/update python bangumi_tracker.py collect 428477 doing # Remove from collection python bangumi_tracker.py uncollect 428477 # Get progress python bangumi_tracker.py progress 428477 # User info python bangumi_tracker.py me # === Character Collection === # Collect/uncollect character python bangumi_tracker.py collect-character 42084 collect python bangumi_tracker.py collect-character 42084 uncollect # List collected characters python bangumi_tracker.py my-characters # === Person Collection === # Collect/uncollect person python bangumi_tracker.py collect-person 12345 collect python bangumi_tracker.py collect-person 12345 uncollect # List collected persons python bangumi_tracker.py my-persons # === Episode Progress === # Get episode collection status python bangumi_tracker.py episodes 428477 # Mark episode as watched/unwatched python bangumi_tracker.py watch 123456 watched python bangumi_tracker.py watch 123456 unwatched ``` ## Security - Windows: Credentials stored in Windows Credential Manager - Other platforms: Credentials stored in `~/.bangumi/` - Tokens auto-refresh when expired ## More Info - [Command Reference](references/COMMANDS.md) - [API Documentation](references/API.md)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: airoom-ai/tavily-research-pro (version 1.1.1)

--- name: tavily-research-pro description: Professional AI-powered search and deep research engine. Features multi-dimensional data aggregation, semantic analysis, and automated report generation for structured information gathering. homepage: https://tavily.com metadata: {"clawdbot":{"emoji":"📊","requires":{"bins":["node"],"env":["TAVILY_API_KEY"]},"primaryEnv":"TAVILY_API_KEY"}} --- # Tavily Research Pro (科研洞察引擎) Professional-grade tool designed for researchers and analysts to gather high-quality, structured information from the web. ## 🛡️ Privacy & Data Security (隐私与安全说明) To comply with security audits and ensure data transparency, please note: * **Data Transmission**: This skill transmits user-provided search queries and specified target URLs to `api.tavily.com` for processing and information extraction. * **Credential Protection**: The `TAVILY_API_KEY` is accessed exclusively from your local environment variables and is never stored, logged, or shared with any third party other than the official Tavily API endpoint. * **Compliance**: This tool is intended for searching and analyzing publicly available web data for research purposes only. --- ## 核心功能 (Core Features) ### 1. 结构化搜索 (Structured Search) Deep research mode for high-accuracy information retrieval. ```bash node {baseDir}/scripts/search.mjs "Quantum computing commercialization" --deep ``` ## 2. 公开信息语义分析 (Public Sentiment Analysis) 🆕 分析当前互联网对某个话题的“体感温度”。 ```bash node {baseDir}/scripts/sentiment.mjs "Apple Vision Pro" ``` ## 3. 自动化研究简报 (Automated Research Report) 🆕 将长网页转化为结构化的情报简报，自动识别关键角色和影响。 ```bash node {baseDir}/scripts/report.mjs "[https://example.com/tech-news](https://example.com/tech-news)" ``` ## 参数说明 (Options) -n <count>: Number of results (default 5). --deep: Enable advanced deep research mode. --topic <news|general>: Specify search category. --days <n>: Limit news search to the last n days. Environment Requirement: A valid TAVILY_API_KEY must be configured in your environment.

path: scripts/extract.mjs
content: #!/usr/bin/env node
// 网页内容批量提取

const args = process.argv.slice(2);
const urls = args.filter(a => !a.startsWith("-"));
if (urls.length === 0) { console.error("No URLs provided"); process.exit(1); }

const [REDACTED_SECRET] ?? "").trim();
const resp = await fetch("https://api.tavily.com/extract", {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({ [REDACTED_SECRET], urls: urls }),
});

const data = await resp.json();
(data.results ?? []).forEach(r => {
  console.log(`# ${r.url}\n\n${r.raw_content || "(no content)"}\n---\n`);
});

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: dommholland/getpost-ai (version 1.0.0)

--- name: getpost-ai description: "Access 24+ LLM chat models and 16+ image/video generation models via one API." version: "1.0.0" --- # GetPost AI API Access 24+ LLM chat models and 16+ image/video generation models through a single API. OpenAI, Anthropic, Google, Grok, DeepSeek, Moonshot, Midjourney, and more. ## Quick Start ```bash # Sign up (no verification needed) curl -X POST https://getpost.dev/api/auth/signup \ -H "Content-Type: application/json" \ -d '{"name": "YOUR_NAME", "bio": "What your agent does"}' # Save the api_key from the response ``` ## Authentication ``` [REDACTED_SECRET] ``` ## Chat Completion ```bash curl -X POST https://getpost.dev/api/ai/chat \ -H "[REDACTED_SECRET]" \ -H "Content-Type: application/json" \ -d '{"model": "gpt-4.1-nano", "messages": [{"role": "user", "content": "Hello"}], "max_tokens": 100}' ``` ## Available Chat Models gpt-5.4, gpt-4.1, gpt-4.1-nano, claude-opus-4-6, claude-sonnet-4-6, gemini-2.5-flash, grok-4, deepseek-chat, kimi-k2.5, and more. See all: `GET /api/ai/models` ## Image Generation ```bash curl -X POST https://getpost.dev/api/ai/generate \ -H "[REDACTED_SECRET]" \ -H "Content-Type: application/json" \ -d '{"model": "gpt-image-1", "prompt": "A cat in space", "n": 1}' ``` Models: gpt-image-1, dall-e-3, midjourney, imagen-4, grok-imagine-image, stable-diffusion-3.5 ## Video Generation ```bash curl -X POST https://getpost.dev/api/ai/generate \ -H "[REDACTED_SECRET]" \ -H "Content-Type: application/json" \ -d '{"model": "sora-2", "prompt": "A timelapse of a flower blooming"}' ``` Models: sora-2, grok-imagine-video, veo-3.1. Returns job_id — poll `GET /api/ai/jobs/{id}` for result. ## Full Docs https://getpost.dev/docs/api-reference#ai


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: cinience/alicloud-ai-audio-tts-voice-design (version 1.0.1)

--- name: alicloud-ai-audio-tts-voice-design description: Voice design workflows with Alibaba Cloud Model Studio Qwen TTS VD models. Use when creating custom synthetic voices from text descriptions and using them for speech synthesis. version: 1.0.0 --- Category: provider # Model Studio Qwen TTS Voice Design Use voice design models to create controllable synthetic voices from natural language descriptions. ## Critical model names Use one of these exact model strings: - `qwen3-tts-vd-2026-01-26` - [REDACTED_SECRET] ## Prerequisites - Install SDK in a virtual environment: ```bash python3 -m venv .venv . .venv/bin/activate python -m pip install dashscope ``` - Set `DASHSCOPE_API_KEY` in your environment, or add `dashscope_api_key` to `~/.alibabacloud/credentials`. ## Normalized interface (tts.voice_design) ### Request - `voice_prompt` (string, required) target voice description - `text` (string, required) - `stream` (bool, optional) ### Response - `audio_url` (string) or streaming PCM chunks - `voice_id` (string) - `request_id` (string) ## Operational guidance - Write voice prompts with tone, pace, emotion, and timbre constraints. - Build a reusable voice prompt library for product consistency. - Validate generated voice in short utterances before long scripts. ## Local helper script Prepare a normalized request JSON and validate response schema: ```bash .venv/bin/python skills/ai/audio/alicloud-ai-audio-tts-voice-design/scripts/prepare_voice_design_request.py \ --voice-prompt "A warm female host voice, clear articulation, medium pace" \ --text "This is a voice-design demo" ``` ## Output location - Default output: [REDACTED_SECRET] - Override base dir with `OUTPUT_DIR`. ## Validation ```bash mkdir -p output/alicloud-ai-audio-tts-voice-design for f in skills/ai/audio/alicloud-ai-audio-tts-voice-design/scripts/*.py; do python3 -m py_compile "$f" done echo "py_compile_ok" > output/alicloud-ai-audio-tts-voice-design/validate.txt ``` Pass criteria: command exits 0 and `output/alicloud-ai-audio-tts-voice-design/validate.txt` is generated. ## Output And Evidence - Save artifacts, command outputs, and API response summaries under [REDACTED_SECRET]. - Include key parameters (region/resource id/time range) in evidence files for reproducibility. ## Workflow 1) Confirm user intent, region, identifiers, and whether the operation is read-only or mutating. 2) Run one minimal read-only query first to verify connectivity and permissions. 3) Execute the target operation with explicit parameters and bounded scope. 4) Verify results and save output/evidence files. ## References - `references/sources.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wangziiiiii/baidu-search-pro (version 1.0.1)

--- name: baidu-search description: Compatibility entry for our in-house Baidu realtime search chain. Use for live information, documentation, or research topics. metadata: { "openclaw": { "emoji": "🔍︎", "requires": { "anyBins": ["python", "python3", "py"], "env":["BAIDU_API_KEY"]},"primaryEnv":"BAIDU_API_KEY" } } --- # Baidu Search (compat alias) > Cross-platform Python: on Windows prefer `py -3.11`; on Linux/macOS prefer `python3`; if plain `python` already points to Python 3, it also works. This skill is a backward-compatible alias for `realtime-web-search`. Use it only when an existing workflow still calls `skills/baidu-search`; new installs should prefer `realtime-web-search` directly. ## What this alias keeps working Both commands run the same canonical Baidu-based search chain: ```bash py -3.11 skills/baidu-search/scripts/search.py '<JSON>' py -3.11 skills/realtime-web-search/scripts/search.py '<JSON>' ``` ## Recommended modes - `mode=auto` (default): `web_search -> chat -> web_summary` - `mode=search`: `web_search -> chat` - `mode=summary`: `web_summary` only Prefer `mode=search` for speed and stability. ## Minimal request example ```bash py -3.11 skills/baidu-search/scripts/search.py '{"query":"人工智能","mode":"search"}' ``` ## Required environment variable - `BAIDU_API_KEY` ## Optional endpoint overrides - `BAIDU_WEB_SEARCH_ENDPOINT` - `BAIDU_CHAT_SEARCH_ENDPOINT` - `BAIDU_SUMMARY_ENDPOINT`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 1yihui/yihui-git-monitor (version 1.0.0)

--- name: git-monitor description: 通用 Git 项目监控工具。支持 GitHub、GitLab、Gitee 等所有 Git 平台。可以添加、删除、检查任意 Git 仓库的更新，自动拉取代码并生成变更摘要。 triggers: - 监控项目 - 监控仓库 - 检查更新 - 添加仓库 - 删除监控 - 仓库列表 - GitHub - GitLab - Gitee - 代码变化 - 拉取代码 - 同步代码 tags: - git - monitoring - automation - devops compatibility: openclaw | any-agent license: MIT --- # Git 项目监控技能 # Git 项目监控技能 自动监控 Git 项目更新（支持 GitHub、GitLab、Gitee 等所有 Git 平台），拉取最新代码并生成变更摘要。 ## 🚀 快速开始（只需3步） ### 第1步：安装技能 ```bash clawhub install git-monitor ``` ### 第2步：告诉我你要监控什么 直接在对话中说： > "监控 GitHub 项目 anthropics/skills" > "监控 https://gitee.com/mindspore/mindspore" ### 第3步：开启自动推送（可选） 说："设置定时检查，每6小时一次" 然后就完事了！有代码更新会自动推送到你的飞书/当前聊天窗口。 --- ## 📖 完整使用指南 ### 添加监控仓库 ``` 监控 GitHub 项目 anthropics/skills 监控 https://github.com/openai/openai-python 监控 GitLab 项目 gitlab-org/gitlab 监控 Gitee 项目 openharmony/docs ``` ### 查看监控列表 ``` 查看监控列表 列出所有监控的仓库 ``` ### 手动检查更新 ``` 检查所有更新 检查 anthropics/skills 的更新 ``` ### 删除监控 ``` 删除监控 anthropics/skills ``` ### 开启/修改定时检查 ``` 设置定时检查，每1小时一次 关闭定时检查 ``` --- ## ⚙️ 飞书通知配置（0配置，开箱即用） ### ✅ 推荐：OpenClaw 主配置（无需任何操作） 如果你的 OpenClaw 已经配置了飞书机器人，**自动读取，无需任何操作**。 技能会级联读取飞书凭证，优先级： 1. 环境变量 `FEISHU_APP_ID` / `FEISHU_APP_SECRET` / `FEISHU_CHAT_ID` 2. OpenClaw 主配置 `~/.openclaw/openclaw.json` 中的飞书配置 3. 以上都没有 → 跳过飞书推送（不影响 Git 监控功能） ### 可选：使用自己的飞书机器人 ```bash # 环境变量方式 export FEISHU_APP_ID="cli_xxx" export FEISHU_APP_SECRET="xxx" export FEISHU_CHAT_ID="ou_xxx" ``` 或在 OpenClaw 配置中添加： ```json "feishu": { "appId": "cli_xxx", "appSecret": "xxx", "chatId": "ou_xxx" } ``` ### 获取飞书配置 1. 访问 https://open.feishu.cn/ 创建企业自建应用 2. 获取 App ID 和 App Secret 3. 开启权限：`im:chat:read:chat_id` 和 `im:message:send_as_bot` 4. 将应用添加到群聊，获取群聊 ID --- ## 💻 命令行用法 ```bash cd ~/.openclaw/workspace/skills/git-monitor # 添加仓库 node helper.js add https://github.com/owner/repo # 查看列表 node helper.js list # 检查更新 node helper.js check # 删除仓库 node helper.js remove owner/repo ```


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