OpenClaw/clawhub-security-signals huggingface dataset:
Skill: tianyuvision-max/design-daily-news (version 1.0.1)

--- name: 设计日报 slug: design-daily-news description: 每日设计与科技资讯聚合 - 从优设读报、36氪、虎嗅自动抓取并筛选最新的设计、AI、产品等行业动态。使用此技能获取最新的设计行业新闻、AI 趋势或每日新闻摘要。 --- # 设计日报 每日自动聚合三大数据源，推送设计与科技行业精选资讯。 ## 🎯 核心功能 - **多源聚合**: 优设读报 + 36氪 + 虎嗅，三大数据源覆盖设计与科技 - **智能筛选**: 自动过滤不相关内容，保留设计/AI/科技核心资讯 - **优先级规则**: 优设读报当日新闻全部展示，剩余名额从36氪+虎嗅补充 - **总量控制**: 每日最多推送 15 条 - **可点击链接**: 有来源链接的新闻以 `[标题](链接)` 格式展示，支持微信直接点击跳转 ## 📡 数据来源 | 来源 | 网址 | 类型 | 抓取方式 | 状态 | |------|------|------|----------|------| | 优设读报 | https://www.uisdc.com/news | 设计/AI | HTML 内嵌 JSON (`var uisdc_news`) | ✅ 正常 | | 36氪 | https://36kr.com | 科技/创投 | `fetch_36kr.sh` | ✅ 正常 | | 虎嗅 | https://www.huxiu.com | 科技/商业 | HTML `__NUXT_DATA__` JSON | ✅ 正常 | ## 📋 推送规则 ### 数量规则 1. **优设读报**: 当日新闻**全部展示**（不限条数） 2. **36氪 + 虎嗅**: 从剩余名额里补充优质新闻，交替取以保证来源均衡 3. **总上限**: 15 条 ### 内容筛选（仅适用于36氪/虎嗅） **✅ 保留关键词:** AI、GPT、Claude、Gemini、OpenAI、DeepSeek、GPU、UI、UX、Figma、Adobe、Cursor、Midjourney、OpenClaw、人工智能、芯片、设计、产品、交互、科技、技术、创新、趋势、Agent、模型、智能、腾讯、阿里、百度、龙虾、大模型、机器人、开源、MiniMax、Manus、字节、小米、华为、苹果、谷歌、微软 **❌ 过滤关键词:** 白酒、营销、广告、SU7、电动车评测、明星、综艺、体育、足球、篮球、娱乐、八卦、房产、理财、基金 ## 📖 输出格式规范 ``` 📰 设计日报 - YYYY-MM-DD 🎨 优设读报 1. [有链接的标题](https://example.com) - 内容摘要 2. 没有链接的标题 - 内容摘要 📡 科技资讯精选 3. [有链接的标题](https://example.com) [36氪] - 内容摘要 4. 没有链接的标题 [虎嗅] - 内容摘要 --- 共 N 条 | 优设读报 X 条 + 科技精选 Y 条 ``` **格式要点:** - 有来源链接 → `[标题](url)` 格式，微信可直接点击跳转 - 无来源链接 → 纯文本标题 - 优设读报内嵌的 `[[来源:xxx]]` / `[[全文:xxx]]` / `[[官网:xxx]]` 自动提取为链接 - 36氪/虎嗅新闻标题后附 `[来源名]` 标签 ## 🔧 核心脚本 | 脚本 | 说明 | |------|------| | `scripts/build_daily_report.py` | **主脚本** - 统一抓取三源、筛选、格式化输出 | | `scripts/fetch_uisdc.sh` | 优设读报抓取脚本 | | `scripts/fetch_36kr.sh` | 36氪抓取脚本 | | `scripts/fetch_huxiu.sh` | 虎嗅抓取脚本（修复版） | **运行主脚本:** ```bash python3 /Applications/QClaw.app/Contents/Resources/openclaw/config/skills/design-daily-news-1.0.0/scripts/build_daily_report.py ``` ## ⏰ 定时任务 - **任务名称**: 设计日报 - **执行时间**: 每天 09:00（Asia/Shanghai） - **推送渠道**: 微信（ClawBot） - **超时限制**: 300 秒 ## 🔧 故障排查 | 问题 | 原因 | 解决方案 | |------|------|----------| | 优设读报抓取失败 | 网站结构变更 | 检查 `var uisdc_news` 是否仍存在 | | 虎嗅抓取为空 | `__NUXT_DATA__` 结构变更 | 更新 `fetch_huxiu.sh` 解析逻辑 | | 36氪抓取失败 | 网络或反爬 | 检查 `fetch_36kr.sh` | | 链接无法点击 | 格式问题 | 确认使用 `[标题](url)` markdown 格式 | ## 📝 版本历史 - **v1.0.0** (2026-03-23): 初始版本，支持优设读报 - **v1.1.0** (2026-03-24): 重构为设计日报，移除不稳定的虎嗅/36氪 - **v1.2.0** (2026-03-25): 重新加入36氪+虎嗅，新增统一推送脚本，支持可点击链接格式 **作者**: TUNE.天宇 **更新**: 2026-03-25


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ignaciocervino/swiftui-empty-app-init (version 1.0.0)

--- name: swiftui-empty-app-init description: Initialize a minimal SwiftUI iOS app in the current directory by generating a single `.xcodeproj` with XcodeGen (no workspaces, packages, or tests unless explicitly requested). --- # SwiftUI Empty App Init ## Overview Initialize a clean, single-target SwiftUI iOS app in the current directory. The project is generated using **XcodeGen** to produce a single `.xcodeproj`, leaving developers ready to start adding features immediately. ## Prerequisites - Xcode installed and selected via `xcode-select` - **XcodeGen** available on `PATH` If any prerequisite is missing: - Stop execution - Tell the user exactly what is missing - Do **not** attempt alternative scaffolding or auto-installation ## Inputs - **Project name** (required) - **Minimum iOS deployment target** - **Optional bundle identifier** (or use default) ## Defaults (use without extra confirmation) - Bundle identifier default: `com.example.<ProjectName>` - Proceed immediately once required inputs are provided (do not ask extra confirmations) ## Core Requirements The resulting project must: - Be generated via **XcodeGen** (do not hand-author `project.pbxproj`) - Use a single `.xcodeproj` (no `.xcworkspace`) - Contain exactly one **app target** - Use the SwiftUI `@main App` lifecycle - Contain a minimal `ContentView` placeholder - Contain a minimal `Info.plist` (avoid unnecessary scene or delegate keys) - Contain **no Swift packages** - Contain **no test targets** unless explicitly requested ## Generation - Create a minimal `project.yml` using the provided inputs - Generate `YourApp.xcodeproj` using XcodeGen - Ensure the output matches all Core Requirements ## Expected Structure - `project.yml` - `YourApp.xcodeproj` - `YourApp/` (app target source files) - Optional config files only No additional folders, packages, workspaces, scripts, or assets should be present. ## Minimal Verification (fast) - Confirm `YourApp.xcodeproj` is generated successfully by XcodeGen. - Confirm the default scheme exists (e.g., via a lightweight scheme listing). - Do **not** boot simulators, build, install, or launch unless explicitly requested. ## Notes - Keep the project minimal and unopinionated - Do not add icons/scripts, packages, workspaces, or architecture scaffolding - This skill is for **app initialization only**, not feature scaffolding


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

--- name: "baidu-search-sldjs" description: "Open browser and search for keywords. Use when user wants to search online." version: 1.0.0 --- # Baidu Search This skill performs web searches using Baidu search engine. ## When to Use Invoke this skill when: - User asks to search for information on Baidu - User requests web search results for specific keywords - User needs to find information online using Baidu ## How It Works 1. Opens the default web browser on the local system 2. Navigates to Baidu search engine 3. Performs a search with the provided keywords 4. Returns search results to the user ## Usage Simply provide the search keywords when invoking this skill. The skill will: - Construct the Baidu search URL with the keywords - Open the browser to the search results page - Allow the user to view the search results ## Example User: "Search for Python tutorials on Baidu" Skill: Opens browser and searches "Python tutorials" on Baidu


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

--- name: meta-调控 description: 我作为主控 Agent 的协调能力。调度子 Agent 后台操作，我只管全局。 --- # Meta 调控 我作为主控 Agent，调度子 Agent 后台干活，我只管全局。 ## 能力轮廓 - **输入**：复杂任务 - **输出**：拆分给子 Agent，我只汇总 - **核心**：调控全局，子 agent 干活 ## 工作流 ``` 1. 收到任务 2. 拆解成子任务 3. 调度子 Agent 并行执行 4. 子 Agent 后台干活 5. 我汇总结果 6. 调控全局 ``` ## 子 Agent 类型 | 类型 | 用途 | |------|------| | 搜索 Agent | 查资料、爬网站 | | 分析 Agent | 分析数据、写报告 | | 执行 Agent | 跑脚本、自动化 | | 监控 Agent | 持续监控、告警 | ## 调度方式 用 sessions_spawn 开启子 Agent： - 独立会话，后台运行 - 我继续和你聊天 - 子 Agent 干完自动汇报 ## 沟通方式 - 子 Agent → 我 → 你 - 我不打扰你，只在必要时汇总 - 有重要进展才通知你 ## 核心原则 - **我只管全局** - 细节让子 Agent 做 - **后台运行** - 不阻塞和你的对话 - **需要时才汇报** - 不刷屏

path: skill.json
content: {
  "name": "meta-调控",
  "version": "1.0.0",
  "description": "主控 Agent 调度子 Agent 后台操作",
  "author": "xiaoning",
  "keywords": ["meta", "orchestrator", "sub-agent", "调度"]
}


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: davidsteelerose/k2ljl-qmd (version 1.0.0)

--- name: qmd description: Search and retrieve markdown documents from local knowledge bases using qmd. Supports BM25 keyword search, vector semantic search, and hybrid search with LLM re-ranking. Use for querying indexed notes, documentation, meeting transcripts, and any markdown-based knowledge. Requires qmd CLI installed (bun install -g https://github.com/tobi/qmd). --- # QMD - Local Markdown Search Search and retrieve documents from locally indexed markdown knowledge bases. ## Installation ```bash bun install -g https://github.com/tobi/qmd ``` ## Setup ```bash # Add a collection qmd collection add ~/notes --name notes --mask "**/*.md" # Generate embeddings (required for vsearch/query) qmd embed ``` ## Usage Rules **Always use `--json` flag** for structured output when invoking qmd commands. ## Search Commands ### search (BM25 keyword search - fast) ```bash qmd search "authentication flow" --json qmd search "error handling" --json -n 10 qmd search "config" --json -c notes ``` ### vsearch (vector semantic search) ```bash qmd vsearch "how does login work" --json qmd vsearch "authentication best practices" --json -n 20 ``` ### query (hybrid with LLM re-ranking - best quality) ```bash qmd query "implementing user auth" --json qmd query "deployment process" --json --min-score 0.5 ``` ### Search Options | Option | Description | |--------|-------------| | `-n NUM` | Number of results (default: 5, or 20 with --json) | | `-c, --collection NAME` | Restrict to specific collection | | `--min-score NUM` | Minimum score threshold | | `--full` | Return complete document content in results | | `--all` | Return all matches | ## Retrieval Commands ### get (single document) ```bash qmd get docs/guide.md --json qmd get "#a1b2c3" --json qmd get notes/meeting.md:50 -l 100 --json ``` ### multi-get (multiple documents) ```bash qmd multi-get "docs/*.md" --json qmd multi-get "api.md, guide.md, #abc123" --json qmd multi-get "notes/**/*.md" --json --max-bytes 20480 ``` ## Maintenance Commands ```bash qmd update # Re-index changed files qmd status # Check index health qmd collection list # List all collections ``` ## Search Mode Selection | Mode | Speed | Quality | Best For | |------|-------|---------|----------| | search | Fast | Good | Exact keywords, known terms | | vsearch | Medium | Better | Conceptual queries, synonyms | | query | Slow | Best | Complex questions, uncertain terms | **Performance note:** `vsearch` and `query` have ~1 minute cold start latency for vector initialization. Prefer `search` for interactive use. ## MCP Server qmd can run as an MCP server for direct integration: ```bash qmd mcp ``` Exposes tools: `qmd_search`, `qmd_vsearch`, `qmd_query`, `qmd_get`, `qmd_multi_get`, `qmd_status`


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

--- name: "Concert" description: "提供演唱会、音乐节、话剧舞剧官方订票通道及现场打卡攻略。 " --- # 演出订票 ## 筛选项 - 内容分级（全年龄/青少年/付费/独播） - 清晰度/帧率（4K/1080P/杜比音效） - 题材领域（热剧/动漫/游戏竞技/文化博主） ## 返回字段 - 播放进度云记录/离线存量/下载速度 - 互动弹幕控制/倍速播放/画面比例调整 - 会员到期提醒/付费单集状态/推荐精准度 ## 示例请求 - “如何快速使用 演出订票 处理 [具体场景任务] ？” - “演出订票 的 [核心功能] 有哪些最新玩法/优惠信息？” ## 更新频率 - 跟随版权更新进度、流行趋势与算法热度每日迭代 ## 注意事项 - 观看请注意用眼健康；青少年建议开启青少年模式限制在线时长。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: davidsmorais/capacities (version 1.0.0)

--- name: capacities description: Manage Capacities notes, daily entries, and weblinks. metadata: { "openclaw": { "emoji": "🧠", "requires": { "env": ["CAPACITIES_API_TOKEN"] }, "primaryEnv": "CAPACITIES_API_TOKEN", }, } --- # Capacities Skill Use this skill to interact with your Capacities "Second Brain". ## Requirements - `CAPACITIES_API_TOKEN`: Obtain from Settings > Capacities API in the desktop app. - `CAPACITIES_SPACE_ID`: (Optional) If not provided, the first available space will be used. ## Usage ### Daily Notes To add a thought, task, or note to today's daily note: `curl -X POST https://api.capacities.io/save-to-daily-note -H "Authorization: Bearer $CAPACITIES_API_TOKEN" -H "Content-Type: application/json" -d '{"spaceId": "$CAPACITIES_SPACE_ID", "mdText": "Your note here"}'` ### Web Links To save a URL to your space: `curl -X POST https://api.capacities.io/save-weblink -H "Authorization: Bearer $CAPACITIES_API_TOKEN" -H "Content-Type: application/json" -d '{"spaceId": "$CAPACITIES_SPACE_ID", "url": "https://example.com"}'` ### Search / Lookup To find an object's ID: `curl -X POST https://api.capacities.io/lookup -H "Authorization: Bearer $CAPACITIES_API_TOKEN" -H "Content-Type: application/json" -d '{"spaceId": "$CAPACITIES_SPACE_ID", "searchTerm": "My Note"}'` ### Space Info To get all object types and structures: `curl -X GET "https://api.capacities.io/space-info?spaceid=$CAPACITIES_SPACE_ID" -H "Authorization: Bearer $CAPACITIES_API_TOKEN"`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: ssidharhubble/x402-monetize (version 1.0.13)

--- name: x402-monetize version: 1.0.13 description: | Creates paid API endpoints using the x402 micropayment protocol. Wraps existing Zo skills and tools behind per-request payment walls. Each call costs $0.001–$0.10, settled on-chain. No crypto custody risk — the x402 facilitator handles payment. compatibility: Zo Computer, Node.js, @x402/core, @x402/express metadata: author: ssyopros.zo.computer category: monetization tags: x402, micropayments, paid-api, passive-# x402-monetize Create paid API endpoints that earn micropayments every time they're called. ## What This Does 1. Wrap any Python script or Zo tool as an HTTP API endpoint 2. Add x402 payment middleware (client pays per-call via USDC on Base) 3. Register the endpoint as a Zo user service on port 4020 4. The endpoint runs 24/7 and deposits earnings ```bash cd /home/workspace/MoneyMachine/x402_server node index.js ``` ## Endpoints Included | Endpoint | Price | Description | |---|---|---| | `/api/signals/:ticker` | $0.01 | Options trading signal (bull/bear/neutral) | | `/api/whale/:ticker` | $0.01 | Whale flow alert (size, direction, conviction) | | `/api/wave/:ticker` | $0.01 | Elliott Wave count + Fibonacci levels | | `/api/maxpain/:ticker` | $0.005 | Max pain level + strike wall zones | | `/api/backtest` | $0.05 | Run options strategy backtest | | `/api/services` | free | List all paid endpoints | ## Adding a New Paid Endpoint ```javascript const { paymentMiddleware } = require('@x402/express'); // Add to index.js: app.get('/api/my-endpoint', paymentMiddleware(), async (req, res) => { // Your logic here res.json({ result: 'paid data', price: '$0.01' }); }); ``` ## Payment Flow 1. Client requests `/api/signals/AAPL` 2. Server returns `402 Payment Required` with payment details 3. Client pays USDC to facilitator 4. Client retries with payment proof 5. Server returns data ## Docs - https://docs.x402.org/getting-started/quickstart-for-sellers


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: raisonliu/xxjhe-ashare-daily-info (version 1.0.0)

--- name: stock-daily-report description: | 股票机构消息日报筛选工具。从指定API信源获取当日机构研报和资讯，按影响力、重要性、新颖性等维度评分筛选，输出Top 20精选日报。 触发场景：用户说"看一下今天的股市消息"、"看一下机构消息"、"机构日报"、"股市日报"、"今天有什么重要消息"、"stock daily"、"机构信息筛选"等涉及股票/机构资讯筛选汇总的需求。 --- # 股票机构消息日报 ## 数据源 API地址：`http://81.68.131.238/api/today` 使用 `web_fetch` 工具抓取，设置 `maxChars=500000`，`extractMode=text`。数据为JSON格式，`data` 数组中每条消息包含 `text`（消息正文+点评）、`created_at`（发布时间）、`id` 等字段。 ## 工作流程 ### 第一步：获取数据 调用 `web_fetch` 获取当日全部消息。注意： - API返回可能被截断，必要时多次抓取 - 过滤掉纯链接无正文的消息（`text` 中仅有 `<e type="web"` 标签） - 过滤掉广告/推广内容（"访问信息聚合知识星球"等开头的模板文字不计入评分） ### 第二步：三轮筛选（内部执行，不输出中间结果） 对每条有效消息按以下5个维度评分（每项1-5分，满分25分）： 1. **时效性**：发布时间越新分数越高，按 `created_at` 排序赋分 2. **影响力**：该事件影响多少人/多少资金规模 3. **重要性**：是否涉及宏观政策、重大产业趋势/技术突破、公司重大事项 4. **证据可靠性**：来源是否权威、是否有数据/事实支撑（券商研报 > 自媒体 > 段子） 5. **新颖性**：是否首次出现的信息、是否存在"信息差" 执行三轮独立评分： - 每轮对所有消息打分，选出总分最高的前20条 - 三轮完成后，按消息被选中频率排序，选出出现次数最多的20条作为最终结果 - 若出现频率相同，按三轮平均总分从高到低排序 ### 第三步：输出日报 仅输出最终20条精选消息，格式如下： ``` # 📊 YYYY年MM月DD日 信息聚合机构信息日报精选（Top 20） **1. [简短标题，15字以内]** [保留原文核心内容，精简但不丢失关键数据和逻辑] **2. [简短标题，15字以内]** [保留原文核心内容] ... **今日核心主题：** [用一句话总结今日最关键的投资主题，3-5个关键词] ``` 输出要求： - 保留原文中的关键数据（营收、增速、价格、订单量等） - 不附带评分过程 - 按总分从高到低排序 - 每条标题简洁有力，正文精炼但信息密度高 ## 注意事项 - 不要自我幻想、不要猜测，仅基于信源实际内容 - 行业综述类消息（如季度财报总结）虽然信息量大，需评估其新颖性——如果只是常规数据汇总而非超预期信息，分数不应过高 - 个股事件（重大订单、FDA获批、并购等）通常新颖性更高 - 宏观地缘事件（中东局势、关税政策等）影响力大但需区分是否为旧闻更新 - 券商研报中的"推荐买入"属于常规操作，不额外加分；但首次覆盖或目标价大幅上调可加分 - 结尾加上信息来源：信息聚合知识星球


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ivangdavila/markdown (version 1.0.1)

--- name: Markdown description: Generate clean, portable Markdown that renders correctly across parsers. metadata: {"clawdbot":{"emoji":"📝","os":["linux","darwin","win32"]}} --- ## Whitespace Traps - Blank line required before lists, code blocks, and blockquotes—without it, many parsers continue the previous paragraph - Nested lists need 4 spaces (not 2) for GitHub/CommonMark; 2 spaces breaks nesting in strict parsers - Two trailing spaces for `<br>` break—invisible and often stripped by editors; prefer blank line or `<br>` tag - Lines with only spaces still break paragraphs—trim trailing whitespace ## Links & Images - Parentheses in URLs break `[text](url)`—use `%28` `%29` or angle brackets: `[text](<url with (parens)>)` - Spaces in URLs need `%20` or angle bracket syntax - Reference-style links `[text][ref]` fail silently if `[ref]: url` is missing—verify all refs exist - Images without alt text: always provide `![alt](url)` even if empty `![]()` for accessibility tools ## Code - Triple backticks inside fenced blocks—use 4+ backticks for outer fence or indent method - Inline backticks containing backtick—wrap with double backticks and pad: ``` `` `code` `` ``` - Language hint after fence affects syntax highlighting—omit only when truly plain text ## Tables - Alignment colons go in separator row: `:---` left, `:---:` center, `---:` right - Pipe `|` in cell content needs backslash escape: `\|` - No blank line before table—some parsers fail - Empty cells need at least one space or break rendering ## Escaping - Characters needing escape in text: `\*`, `\_`, `\[`, `\]`, `\#`, `\>`, `\``, `\\` - Escape not needed inside code spans/blocks - Ampersand only needs escape as `&amp;` when it could form an HTML entity ## Portability - HTML tags work in GitHub but stripped in many renderers—prefer pure Markdown - Extended syntax (footnotes, task lists, emoji shortcodes) not universal—check target parser - YAML frontmatter needs `---` fences and only at file start; some parsers render it as text


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tonicpopo/xiaowuzi-gene-repair (version 1.0.0)

\# 小五子 - 基因修复 (xiaowuzi-gene-repair) \## 模块概述 本模块是小五子基因演化系统（GEP）中的“免疫与修复中心”。它专门设计用于在复杂的 AI 代理演化过程中，实时识别并修复逻辑坍塌、语法错误或指令冲突。 \## 核心修复矩阵 \- \*\*异常逻辑拦截 (Anomaly Detection)\*\*：利用深度学习模型扫描基因片段，识别可能导致 AI 陷入死循环的逻辑悖论。 \- \*\*自动化基因回滚\*\*：当新演化的基因片段在运行测试中表现不佳或报错时，系统将触发秒级回滚机制，确保主代理（Agent）的稳定性。 \- \*\*指令集自愈\*\*：针对 Prompt 注入或格式错误，模块会自动尝试按照预设的演化协议进行微调修复。 \## 部署意义 在无人值守的自动化任务（如 StagflationV3Pro 长期监控）中，基因修复模块是系统稳健性的最后一道防线，极大降低了因模型输出不稳定导致的系统崩溃风险。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: openclawzhangchong/douyindownloadwhisper (version 1.0.0)

## 一、技能基础信息 name: douyin-download-whisper description: 抖音无水印视频下载和文案提取工具，适配Windows系统，支持无水印视频解析下载、语音文案提取（锁定Whisper base模型）及文案语义分段。 metadata: openclaw: emoji: 🎵 requires: bins: [ffmpeg, whisper] config: whisper_model: base # 锁定Whisper base模型，确保转写效率与兼容性，适配Windows本地运行 ## 二、技能介绍 douyin-download 是一款适配 Windows 系统的抖音辅助工具，核心功能为无水印视频下载、视频文案提取（含语音转文字）及文案语义分段，依托本地 Whisper 实现语音转写，调用 OpenClaw 内置 LLM 完成语义分段，操作简单、可直接通过命令行调用。 ## 三、核心功能 - 🎬 获取无水印视频下载链接：解析抖音分享链接，提取无水印视频源地址 - 📥 下载抖音视频：将解析后的无水印视频直接下载至指定目录 - 🎙️ 语音文案提取：通过本地 Whisper 工具，将视频中的语音内容转写为文字文案 - ✂️ 文案语义分段：自动调用 OpenClaw 内置 LLM，对提取的文案进行自然语义分段，提升可读性 ## 四、环境依赖（Windows 系统必配） - **Windows 安装（推荐使用阿里云镜像）** ```powershell pip install -i https://mirrors.aliyun.com/pypi/simple/ --upgrade openai-whisper ``` 该技能依赖以下工具，需提前安装并配置，否则无法正常运行： 1. **ffmpeg**：用于视频解析与处理，需安装后添加至 Windows 系统环境变量 PATH（安装后可在命令行输入 `ffmpeg -version` 验证是否配置成功）。 2. **whisper**：用于语音转文字，通过 Python 命令安装，安装命令：`pip install -U openai-whisper`，安装后可在命令行输入 `whisper --version` 验证是否可正常调用。 3. **Node.js**：需提前安装 Node.js 环境，确保 `node` 命令可在命令行正常使用（用于执行 JS 脚本）。 ## 五、环境变量 （此技能无需配置额外环境变量，仅需确保上述依赖工具正常安装并配置完成即可） ## 六、使用方法（Windows 命令行专用） 所有命令均需在 Windows 命令提示符（CMD）或 PowerShell 中执行，复制命令后替换「抖音分享链接」为实际链接即可使用，路径可根据需求自行修改。 ### 6.1 获取视频信息 功能：解析抖音链接，获取无水印视频地址、视频标题等基础信息。 ```bash node "%USERPROFILE%.openclawworkspaceskillsdouyin-downloaddouyin.js" info "抖音分享链接" ``` ### 6.2 下载视频 功能：将无水印视频下载至指定目录，默认下载至 C:Tempdouyin-download，可通过 -o 参数修改下载路径。 ```bash node "%USERPROFILE%.openclawworkspaceskillsdouyin-downloaddouyin.js" download "抖音链接" -o C:Tempdouyin-download ``` 说明：若指定的下载目录不存在，工具会自动创建该目录。 ### 6.3 提取文案（自动语义分段） 功能：下载视频并提取语音文案，自动进行语义分段，生成可读性强的文字内容。 ```bash # 如需使用本地 Whisper，请确保已安装 whisper 可执行文件（安装命令：pip install -U openai-whisper） node "%USERPROFILE%.openclawworkspaceskillsdouyin-downloaddouyin.js" extract "抖音链接" ``` - 核心逻辑：先下载视频 → 提取视频中的语音 → 通过本地 Whisper（已锁定base模型）转写为文字 → 调用 OpenClaw 内置 LLM 进行自然语义分段，base模型体积小、运行高效，适配Windows本地环境。 - 注意：首次使用 Whisper 时，会自动下载锁定的base模型，体积较小，无需等待过长时间，需保持网络通畅。 ### 6.4 跳过语义分段 功能：提取文案但不进行语义分段，直接输出完整的转写文字。 ```bash node "%USERPROFILE%.openclawworkspaceskillsdouyin-downloaddouyin.js" extract "抖音链接" --no-segment ``` ## 七、常见问题 - 问题1：执行命令时提示「node 不是内部或外部命令」？ 解决：未安装 Node.js 或未将 Node.js 添加至系统环境变量，重新安装 Node.js 并重启命令行。 - 问题2：提取文案时提示「whisper 不是内部或外部命令」？ 解决：Whisper 安装未成功或未添加至环境变量，重新执行 `pip install -U openai-whisper`，并检查 Python 的 Scripts 目录是否在系统 PATH 中。 - 问题3：下载视频失败？ 解决：检查抖音链接是否有效（需为完整的抖音分享链接），网络是否通畅，目标下载目录是否有写入权限。 ## 八、注意事项 1. 本工具仅用于个人学习、研究使用，请勿用于商业用途或下载侵权视频，遵守抖音平台规则。 2. 若抖音平台接口更新，可能导致工具解析失败，需等待技能更新适配。 3. Whisper 转写精度受视频语音清晰度影响，模糊语音可能出现转写误差。


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mzfshark/mentor-counsel (version 1.0.1)

--- name: mentor-counsel description: Provide strategic guidance with clarity, trade-offs, and risk framing; returns agency to the user without dependency. metadata: author: Morpheus version: 2.0.0 owner: Morpheus Agent category: mentoring --- # SKILL: mentor-counsel ## Purpose Provide strategic guidance to the developer with clarity, trade-offs, and risk framing — without dependency creation. ## When to Use - The user is stuck, uncertain, or overwhelmed - A strategic decision must be made (architecture, product, governance) - The user asks for “what should I do” guidance ## Inputs - `problem` (required): the decision or situation - `context` (required): current state, constraints, timeline - `constraints` (optional): non-negotiables (governance, security, budget, time) ## Steps 1. Reframe the problem (what is actually being decided). 2. Identify the real objective (success criteria + non-goals). 3. Present 2–3 viable paths. 4. Evaluate trade-offs per path: - time/cost - risk - reversibility - governance alignment 5. Recommend a direction and provide the next 1–3 actions. ## Validation - Recommendation is consistent with stated constraints. - Risks are explicit (no hidden assumptions). - If confidence is low, say so and propose verification steps. ## Output - `recommendation` - `reasoning` - `risks` - `next_actions` ## Safety Rules - No emotional manipulation. - No false certainty; do not overclaim. - Do not replace user judgment; return agency. ## Example Input: - problem: “Should we ship now or refactor first?” - context: “Bug reports rising; limited time.” Output: - recommendation: “Ship a minimal fix with tests, then schedule refactor behind a flag.” - risks: “Tech debt persists; mitigate with characterization tests.”


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mikeclaw007/lizhi (version 0.1.0)

--- name: lizhi-hot-trend description: 注册“荔枝FM”热门技能；当需要访问或自动化荔枝相关内容时调用。 homepage: `https://www.lizhi.fm/` metadata: {"clawdbot":{"emoji":"🔥"}} --- # 荔枝FM 用途与边界 - 面向公开节目/播客页的检索与表现数据摘要 - 不提供下载、接口逆向或突破风控能力 - 仅用于公开页面的轻量分析与提醒 关键入口 - 主页：https://www.lizhi.fm/ - 分类/榜单：站点入口 - 搜索入口：站内搜索 常见任务 - 指定关键词/分类的节目列表摘要（播放量、收藏、评论） - 播客主页近期节目表现对比 - 榜单条目分布与题材统计 数据字段 - 节目名称、播客名称、时长、发布时间、播放量/收藏/评论、链接 - 播客主页链接、近期节目交互指标摘要 - 榜单名称、采集时间、来源链接 自动化要点 - 动态渲染与人机校验，建议人工打开后触发解析 - 请求频率需控制，不进行批量抓取 - 不进行下载或接口调用 示例流程 - 关键词摘要：输入关键词 → 搜索 → 抽取条目 → 输出表现摘要 - 播客对比：访问主页 → 抽取近期节目 → 输出交互指标 - 榜单统计：访问榜单 → 抽取条目 → 题材分布统计 合规提示 - 遵守平台规则，不进行下载、接口调用或批量采集 - 结果仅用于内部分析与提醒


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