OpenClaw/clawhub-security-signals huggingface dataset:
Skill: raychanpmp/smart-teacher (version 1.0.0)

--- name: Smart Teacher description: "Expert education planning tool. Generate lesson plans, curriculum maps, study schedules, assessments, and learning objectives aligned to Bloom's taxonomy." permissions: Bash triggers: - lesson plan - curriculum planning - education planning - study schedule - assessment - learning objectives - smart teacher --- # Smart Teacher Expert education planning assistant. Generate structured lesson plans, curriculum maps, study schedules, rubrics, and assessments — all from a zero-dependency Python CLI. ## Quick Start ```bash # Generate a lesson plan python3 scripts/edu_planner.py lesson "Photosynthesis" --grade 8 --duration 45 # Create a curriculum map python3 scripts/edu_planner.py curriculum "Biology" --grade 9 --weeks 12 # Build a study schedule python3 scripts/edu_planner.py schedule --subjects "Math,Science,English" --hours 3 --days 5 # Generate learning objectives using Bloom's taxonomy python3 scripts/edu_planner.py objectives "Fraction Operations" --grade 5 --levels "remember,understand,apply,analyze" # Create a rubric python3 scripts/edu_planner.py rubric "Persuasive Essay" --criteria 4 --levels 4 # Generate an assessment python3 scripts/edu_planner.py assess "Cell Biology" --grade 10 --type quiz --questions 10 ``` ## Commands Reference | Command | Description | |---------|-------------| | `lesson <topic>` | Generate a structured lesson plan | | `curriculum <subject>` | Create a multi-week curriculum map | | `schedule` | Build a weekly study schedule | | `objectives <topic>` | Generate learning objectives (Bloom's taxonomy) | | `rubric <assignment>` | Create an assessment rubric | | `assess <topic>` | Generate a quiz or test | ## Options ### Lesson Plan - `--grade N` — Target grade level (K-12) - `--duration N` — Duration in minutes - `--standard <std>` — Align to standards (e.g., "CCSS", "NGSS", "Alberta") ### Curriculum Map - `--grade N` — Grade level - `--weeks N` — Number of weeks - `--standard <std>` — Standards alignment ### Study Schedule - `--subjects "A,B,C"` — Comma-separated subjects - `--hours N` — Hours per day - `--days N` — Days per week - `--exam-date YYYY-MM-DD` — Target exam date for priority weighting ### Learning Objectives - `--grade N` — Grade level - `--levels "list"` — Bloom's levels: remember, understand, apply, analyze, evaluate, create ### Rubric - `--criteria N` — Number of criteria (default: 4) - `--levels N` — Performance levels (default: 4) ### Assessment - `--type quiz|test|exam` — Assessment type - `--questions N` — Number of questions - `--grade N` — Grade level ## Bloom's Taxonomy All objectives and assessments are aligned to Bloom's revised taxonomy: 1. **Remember** — Recall facts and basic concepts 2. **Understand** — Explain ideas or concepts 3. **Apply** — Use information in new situations 4. **Analyze** — Draw connections among ideas 5. **Evaluate** — Justify a stand or decision 6. **Create** — Produce new or original work


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: spyfree/x-mobile-longshot (version 1.0.1)

--- name: x-mobile-longshot description: Render X (Twitter) posts, long articles, and mobile reading pages into mobile-style long screenshots and optional single-page PDFs using Playwright. Use when the user wants a result that looks like a phone X page or asks for long screenshot export, X article export, mobile-style PDF, app-like dark-mode capture, or cleaner archival output than ordinary print-to-PDF. --- # X Mobile Longshot Render X links into phone-like dark-mode long screenshots, then optionally wrap the image into a single-page PDF. ## Quick start Use the bundled script: ```bash node skills/x-mobile-longshot/scripts/render_x_longshot.js \ --url 'https://x.com/i/status/2030475950752710891' \ --out-png 'outputs/x_render/example.png' \ --out-pdf 'outputs/x_render/example.pdf' ``` If the user only wants the PNG: ```bash node skills/x-mobile-longshot/scripts/render_x_longshot.js \ --url 'https://x.com/i/status/2030475950752710891' \ --out-png 'outputs/x_render/example.png' \ --no-pdf ``` ## Workflow 1. Use Playwright mobile emulation, dark mode, and a phone device preset. 2. Open the X URL and wait for the page to settle. 3. Dismiss top modal/app-open prompts with DOM-side clicks when present. 4. Remove common fixed/sticky overlays by matching known X CTA text. 5. Capture one full-page PNG. 6. Re-open the page, repeat cleanup, capture one clean viewport image. 7. Replace the top portion of the long PNG with the clean viewport image to reduce popup contamination. 8. Convert the PNG into a single-page PDF when requested. ## Defaults - Device: `iPhone 14 Pro Max` - Color scheme: dark - Locale: `zh-CN` - Timezone: `Asia/Shanghai` - Top replacement height: `1450px` ## Tuning Useful flags: - `--device 'iPhone 14 Pro Max'` to change the Playwright device preset - `--wait-ms 6000` to wait longer for slow pages - `--top-fix-px 1700` to replace more of the top area when the popup intrudes deeper - `--no-pdf` to skip PDF creation ## Quality bar Prefer this skill when the user wants: - phone-like visual presentation - dark-mode X reading output - archival PNG + PDF delivery - a result that feels closer to a real screenshot than browser print output Do not overclaim native-app fidelity. This is a browser-rendered mobile capture pipeline with cleanup, not a real iPhone App Store build of X. ## Troubleshooting If the output still contains overlays or sticky bars: 1. Re-run with higher `--wait-ms`. 2. Increase `--top-fix-px`. 3. Read `references/notes.md` and update the overlay text patterns in `scripts/render_x_longshot.js`. 4. If X changed its DOM substantially, migrate to segmented capture + stitching as the next iteration. ## Resources - `scripts/render_x_longshot.js`: main renderer - `references/notes.md`: v1 design notes, limits, and iteration guidance


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yeholdon/defuddle-extractor (version 1.0.0)

--- name: defuddle description: 使用 Defuddle 库从任意网页提取主要内容并转换为 Markdown 格式。支持 CLI 和 Node.js 集成，用于内容爬虫、文本处理和自动化任务。 metadata: {"openclaw": {"os": ["darwin", "linux", "win32"], "author": "Honcy Ye", "email": "[REDACTED_SECRET]"}} --- # Defuddle 网页内容提取技能 使用 Defuddle 库从任意网页提取主要内容并转换为 Markdown 格式。 ## 功能特性 - **内容提取**：自动检测并提取网页主要内容 - **Markdown 转换**：将 HTML 内容转换为 Markdown 格式 - **垃圾清理**：移除广告、侧边栏、评论等网页垃圾 - **CLI 支持**：提供命令行接口快速使用 - **Node.js 集成**：支持在 Node.js 环境中使用 - **自定义配置**：支持自定义内容选择器和选项 ## 技术实现 - 使用 Defuddle 库进行网页内容提取 - 支持多种配置选项 - 提供简单易用的 API ## 使用方法 ### 1. 命令行使用 ```bash # 解析 URL 并输出为 Markdown npx defuddle parse https://example.com/article --markdown # 解析本地 HTML 文件 npx defuddle parse page.html --markdown # 输出为 JSON 格式（包含元数据） npx defuddle parse page.html --json ``` ### 2. 脚本使用 ```bash # 从 URL 提取内容并发送到微信文件传输助手 bash scripts/extract_and_send.sh "https://example.com/article" "文件传输助手" # 从 URL 提取内容并发送到 Telegram bash scripts/extract_and_send_telegram.sh "https://example.com/article" <chat_id> ``` ### 3. Node.js API ```javascript import { JSDOM } from 'jsdom'; import { Defuddle } from 'defuddle/node'; async function extractContent(url) { const response = await fetch(url); const html = await response.text(); const dom = new JSDOM(html, { url }); const result = await Defuddle(dom.window.document); return { title: result.title, content: result.content, markdown: result.contentMarkdown }; } ``` ## 配置选项 - **markdown**: 转换为 Markdown 格式 - **debug**: 启用调试模式 - **contentSelector**: 自定义内容选择器 - **removeImages**: 移除图片 - **removeHiddenElements**: 移除隐藏元素 ## 脚本说明 - `scripts/extract_content.sh`: 从 URL 提取内容并输出到控制台 - `scripts/extract_and_send.sh`: 提取内容并发送到微信 - `scripts/extract_and_send_telegram.sh`: 提取内容并发送到 Telegram ## 依赖 - Node.js 和 npm（用于 CLI） - defuddle 库（已通过 npm 安装） ## 安装 ```bash npm install -g defuddle ``` ## 注意事项 - Defuddle 需要 Node.js 环境（建议使用 Node.js 18 或更高版本） - 某些网站可能有防爬虫机制，可能导致提取失败 - 大型网页内容提取可能需要较长时间


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: harshasic/recipe-finder (version 1.0.0)

# Recipe Finder Find recipes by ingredients, cuisine, or dietary preferences using TheMealDB free API. ## Triggers Use this skill when the user: - Asks "what can I make with [ingredient]" - Requests "recipes for dinner" or "Italian recipes" - Says "vegetarian options" or "vegan meals" - Asks for meal ideas based on ingredients they have ## Tools - `web_fetch` - Fetch recipe data from TheMealDB API ## Instructions 1. Parse the user's request to identify: - Main ingredient (e.g., chicken, eggs, pasta) - Cuisine type (e.g., Italian, Mexican, Chinese) - Dietary restriction (e.g., vegetarian, vegan, gluten-free) 2. Call TheMealDB API: - By ingredient: `https://www.themealdb.com/api/json/v1/1/filter.php?i={ingredient}` - By cuisine: `https://www.themealdb.com/api/json/v1/1/filter.php?a={cuisine}` - Search by name: `https://www.themealdb.com/api/json/v1/1/search.php?s={query}` 3. For each recipe found, optionally fetch details: - `https://www.themealdb.com/api/json/v1/1/lookup.php?i={id}` 4. Format response with: - Recipe name - Thumbnail image (if available) - Category and cuisine - Key ingredients - Brief instructions (or link to full recipe) ## Response Format Present recipes in a clean, scannable format: - Recipe name (bold) - Image thumbnail (if available) - Category | Cuisine - Main ingredients list - Brief description ## Notes - TheMealDB is free, no API key required - Results are limited but reliable - Combine multiple queries for best results (ingredient + cuisine)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: merjua14/competitor-price-monitor (version 1.0.0)

# Competitor Price Monitor Skill Track competitor pricing, product changes, and market positioning automatically. ## What This Skill Does 1. **Track Pricing** — Monitor competitor websites for price changes 2. **Detect Changes** — Alert when competitors add/remove products, change copy, or update pricing 3. **Benchmark** — Compare your pricing against the market 4. **Report** — Weekly competitive intelligence summaries ## Usage ### Add Competitors ``` Monitor these competitors: [URL1], [URL2], [URL3] Track their pricing pages and product listings ``` ### Get Report ``` Generate competitive pricing report for this week ``` ## Workflow ### Step 1: Competitor Discovery - Input competitor URLs or search by industry/niche - Identify pricing pages, product listings, and feature pages - Store baseline snapshots ### Step 2: Monitoring On each check cycle: 1. Fetch current page content via web scraping 2. Compare against stored baseline 3. Detect changes in: - Prices (increases, decreases, new tiers) - Products (added, removed, renamed) - Features (new capabilities, deprecated items) - Messaging (positioning, value props, CTAs) ### Step 3: Change Detection ```javascript // Pseudo-code for price change detection const changes = []; for (const competitor of competitors) { const current = await scrapePrice(competitor.url); const baseline = await getBaseline(competitor.id); if (current.price !== baseline.price) { changes.push({ competitor: competitor.name, product: current.product, oldPrice: baseline.price, newPrice: current.price, change: ((current.price - baseline.price) / baseline.price * 100).toFixed(1) + '%' }); } } ``` ### Step 4: Alerting When significant changes detected: - Price decrease >10% → "Competitor [X] dropped [product] price by [Y]%" - New product launched → "Competitor [X] launched [new product] at $[Z]" - Feature added → "Competitor [X] now offers [feature]" ### Step 5: Weekly Report ```markdown ## Competitive Intelligence Report — Week of [date] ### Price Changes | Competitor | Product | Old Price | New Price | Change | |-----------|---------|-----------|-----------|--------| | Acme Corp | Pro Plan | $49/mo | $39/mo | -20% | ### New Products/Features - **Acme Corp** launched "Enterprise Plan" at $199/mo - **Beta Inc** added AI chatbot feature to all tiers ### Market Positioning - Average market price for [category]: $X/mo - Your price: $Y/mo (Z% above/below market) ### Recommendations - Consider matching Acme's price cut on Pro tier - Beta's new AI feature is a competitive threat — prioritize similar feature ``` ## Configuration ```json { "competitors": [ { "name": "Competitor A", "url": "https://competitor-a.com/pricing", "check_frequency": "daily" } ], "alert_channels": ["telegram", "email"], "report_frequency": "weekly", "report_day": "monday" } ``` ## Requirements - Brave Search API or web fetch capability - OpenClaw with scheduled tasks (cron/heartbeat) - Storage for baseline snapshots


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: shuwanito/nexus-curriculum-designer (version 2.1.0)

--- name: nexus-curriculum-designer description: AI curriculum design with adaptive learning paths, competency-based assessments, and UDL framework. version: '2.1.0' metadata: openclaw: emoji: 📚 homepage: https://github.com/Shuwanito/SkillsMP/tree/main/.claude/skills/nexus-curriculum-designer os: - macos - linux - windows --- # Nexus Curriculum Designer ## Capabilities - Curriculum mapping - Bloom's taxonomy alignment - Adaptive path design - NEE/UDL adaptation - Assessment generation ## Workflow 1. Receive task description and target context 2. Analyze using department-specific engines (education) 3. Generate findings with severity classification 4. Produce improvement proposals with impact/effort scoring 5. Cross-validate with synergy departments 6. Return structured results with confidence scores ## Pricing - Per-execution: $1.00 - Outcome-based: Available for enterprise contracts - Volume discounts: 20% for 100+ executions/month ## Guidelines - All outputs include confidence scores and source citations - Cross-validation requires minimum 2 independent sources - Findings are classified: CRITICAL, HIGH, MEDIUM, LOW, INFO - Proposals include impact (1-10), effort (1-10), and priority score


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: daimingvip-a11y/wechat-social-automation (version 0.1.1)

# WeChat Social Automation 微信公众号 + 朋友圈自动化管理技能。支持文章自动发布、定时推送、互动分析、粉丝管理等功能。 ## 触发词 微信、公众号、发布、推送、朋友圈、自动发布、定时推送、weixin、wechat ## 功能 ### 1. 微信公众号文章发布 - 自动发布文章到公众号 - 支持草稿箱管理 - 定时推送功能 - 多图文消息支持 ### 2. 朋友圈自动化 - 自动发布朋友圈内容 - 支持图片/视频/链接 - 定时发布功能 - 互动监控 (点赞/评论) ### 3. 数据分析 - 文章阅读量统计 - 粉丝增长分析 - 互动率计算 - 最佳发布时间建议 ### 4. 客户管理 - 粉丝标签管理 - 自动回复配置 - 关键词触发 - 用户画像分析 ## 配置 ### 环境变量 ```bash WECHAT_APP_ID=你的公众号 AppID WECHAT_APP_SECRET=你的公众号 AppSecret WECHAT_TOKEN=微信验证 Token ``` ### 或使用配置文件 在 `TOOLS.md` 中添加: ```markdown ### 微信公众号 - AppID: [你的 AppID] - AppSecret: [你的 AppSecret] - 公众号名称：[你的公众号名称] ``` ## 使用示例 ### 发布文章 ``` 发布一篇公众号文章，标题"如何月入过万"，内容是... ``` ### 定时推送 ``` 明天上午 9 点推送这篇文章到公众号 ``` ### 查看数据 ``` 查看上周公众号文章阅读数据 ``` ### 粉丝分析 ``` 分析最近粉丝增长情况和互动率 ``` ## 定价 - **基础版**: $9.99/月 - 每月 10 次发布 + 基础数据 - **专业版**: $19.99/月 - 无限发布 + 高级分析 + 定时推送 - **企业版**: $49.99/月 - 多账号管理 + API 访问 + 优先支持 ## 变现方式 1. **SkillPay**: 通过 ClawHub 订阅收费 2. **Gumroad**: 销售高级模板和工作流 3. **定制服务**: 为企业客户定制开发 ## 开发状态 - [x] 技能框架创建 - [ ] 微信公众号 API 集成 - [ ] 发布功能实现 - [ ] 数据分析功能 - [ ] 定时推送功能 - [ ] 测试和优化 - [ ] ClawHub 发布 ## 更新日志 ### v0.1.0 (2026-03-13) - 初始版本创建 - 基础框架搭建 --- *本技能由周田田开发 💕*


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: daniellummis/github-actions-queue-latency-audit (version 1.0.0)

--- name: github-actions-queue-latency-audit description: Audit GitHub Actions queue wait hotspots from run/job JSON so CI bottlenecks are visible before they stall merges. version: 1.0.0 metadata: {"openclaw":{"requires":{"bins":["bash","python3"]}}} --- # GitHub Actions Queue Latency Audit Use this skill to quantify where workflows are waiting in queue before jobs start. ## What this skill does - Reads one or more GitHub Actions run JSON exports (from `gh run view --json ...`) - Computes per-job queue wait (`startedAt - createdAt`) and runtime duration (`completedAt - startedAt`) - Groups repeated jobs by repository + workflow + job name - Ranks hotspots by worst queue wait and average queue wait - Flags warning/critical queue waits with configurable thresholds - Emits output as human-readable text or machine-readable JSON ## Inputs Optional: - `RUN_GLOB` (default: `artifacts/github-actions/*.json`) - `TOP_N` (default: `20`) - `OUTPUT_FORMAT` (`text` or `json`, default: `text`) - `QUEUE_WARN_SECONDS` (default: `120`) - `QUEUE_CRITICAL_SECONDS` (default: `300`) - `FAIL_ON_CRITICAL` (`0` or `1`, default: `0`) — exit non-zero when any job instance hits/exceeds critical queue wait - `WORKFLOW_MATCH` (regex, optional) - `WORKFLOW_EXCLUDE` (regex, optional) - `JOB_MATCH` (regex, optional) - `JOB_EXCLUDE` (regex, optional) - `REPO_MATCH` (regex, optional) - `REPO_EXCLUDE` (regex, optional) ## Collect run JSON ```bash gh run view <run-id> --json databaseId,workflowName,headBranch,headSha,url,repository,jobs \ > artifacts/github-actions/run-<run-id>.json ``` ## Run Text report: ```bash RUN_GLOB='artifacts/github-actions/*.json' \ TOP_N=15 \ QUEUE_WARN_SECONDS=180 \ QUEUE_CRITICAL_SECONDS=420 \ bash skills/github-actions-queue-latency-audit/scripts/queue-latency-audit.sh ``` JSON output for dashboards: ```bash RUN_GLOB='artifacts/github-actions/*.json' \ OUTPUT_FORMAT=json \ FAIL_ON_CRITICAL=1 \ bash skills/github-actions-queue-latency-audit/scripts/queue-latency-audit.sh ``` Filter to one repo/workflow family: ```bash RUN_GLOB='artifacts/github-actions/*.json' \ REPO_MATCH='^flowcreatebot/' \ WORKFLOW_MATCH='(CI|Build|Test)' \ bash skills/github-actions-queue-latency-audit/scripts/queue-latency-audit.sh ``` Run with bundled fixtures: ```bash RUN_GLOB='skills/github-actions-queue-latency-audit/fixtures/*.json' \ bash skills/github-actions-queue-latency-audit/scripts/queue-latency-audit.sh ``` ## Output contract - Exit `0` in reporting mode (default) - Exit `1` if `FAIL_ON_CRITICAL=1` and at least one job instance has queue wait `>= QUEUE_CRITICAL_SECONDS` - In `text` mode: prints summary + top queue hotspots - In `json` mode: prints summary + grouped hotspot records + raw offending instances


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: klilyz/classroom-note-senseaudio-translator (version 1.0.1)

--- name: classroom-note-translator summary: 将英文课堂音视频转成中文可学习笔记，支持 Markdown 输出，并可同步到 Notion 或导入 Obsidian。 description: 接收课堂录音、讲座音频或视频文件（视频会先抽取音轨），调用 SenseAudio HTTP ASR API 进行英文转录，可选直出中文翻译；随后整理为结构化 Markdown 学习笔记，包含摘要、关键概念、术语表、时间轴与复习问题，生成到桌面，并支持导出到 Notion 或保存到 Obsidian vault。 homepage: https://senseaudio.cn/docs/speech_recognition/http_api user-invocable: true metadata: {"openclaw":{"emoji":"🎓","homepage":"https://senseaudio.cn/docs/speech_recognition/http_api","primaryEnv":"SENSEAUDIO_API_KEY","requires":{"bins":["python3"],"env":["SENSEAUDIO_API_KEY"]},"skillKey":"classroom-note-translator"}} --- # 课堂听译笔记官 当用户希望把**英文课堂录音、讲座、组会、YouTube 学习音频/视频**整理成中文笔记时，使用这个 skill。 触发（更明确）： - 用户明确提到“总结”课堂内容 - 用户明确提到“转译/翻译”课堂音视频内容 - 用户明确提到“做中英对照笔记”或“整理课堂笔记” - 用户提供网页链接并希望从页面里的音视频提取后再做总结/转译 非触发： - 仅做逐字转写、且不需要总结和中英对照时 适用场景： - 国外课堂听讲太快，想保留英文原文并生成中文笔记 - 学术报告、课程录音、线上讲座需要整理成复习材料 - 希望得到 Markdown 文档，并进一步导入 Notion 或 Obsidian ## 你应该做什么 1. 收集或确认以下输入： - 输入来源（三选一）：音频路径 / 视频路径 / 页面链接（从页面提取媒体） - 课程标题或主题（可选） - 是否需要说话人分离 - 是否需要句级或字级时间戳 - 输出目录（必填，使用用户指定目录） - 是否导出到 Notion - 是否写入 Obsidian vault 2. 运行脚本： ```bash python3 "{baseDir}/scripts/classroom_note_translator.py" \ --audio "<AUDIO_PATH>" \ # 或 --video "<VIDEO_PATH>" \ # 或 --page-url "<PAGE_URL>" \ --title "<TITLE_OR_TOPIC>" \ --model sense-asr-pro \ --language en \ --target-language zh \ --timestamps segment \ --speaker-diarization \ --summary-provider local \ --output-dir "<USER_SPECIFIED_OUTPUT_DIR>" ``` 3. 如果用户想导出到 Notion，则追加： ```bash --export-notion \ --notion-parent-page-id "<NOTION_PAGE_ID>" ``` 4. 如果用户想写入 Obsidian，则追加： ```bash --export-obsidian \ --obsidian-vault "/absolute/path/to/your/vault" \ --obsidian-folder "Lecture Notes" ``` ## 输出内容 脚本每次都会同时生成： - `*_transcript.json`：SenseAudio 原始响应 - `*_summary.md`：结构化中文学习总结（Markdown） - `*_bilingual.txt`：英文原文 + 中文翻译拼接文本 Markdown 文档默认包含： - 标题与元信息 - 中文摘要 - 关键要点 - 术语表 - 带时间轴的内容梳理 - 复习问题 - 英文原文摘录 ## 参数选择建议 - 默认优先用 `sense-asr-pro`：适合课堂、会议、复杂语音环境。 - 若追求更低成本，可改为 `sense-asr`。 - 若只要快速粗转写，不需要翻译/时间戳/说话人分离，不建议用本 skill，改用轻量转写更合适。 - 对于长音频，先切片再分别处理；SenseAudio 官方文档建议单文件不超过 10MB。 ## 环境变量 - `SENSEAUDIO_API_KEY`：必填，SenseAudio API Key - `OPENAI_API_KEY`：可选，仅当 `--summary-provider openai-compatible` 时需要 - `OPENAI_BASE_URL`：可选，OpenAI 兼容接口地址 - `NOTION_TOKEN`：可选，导出到 Notion 时需要 ## 本地依赖 - 若使用 `--video`，需本机安装 `ffmpeg`（用于抽取音轨） ## 注意事项 - SenseAudio 文档显示，`sense-asr` 与 `sense-asr-pro` 支持翻译、句级/字级时间戳、说话人分离与情感分析；HTTP 接口为 `POST https://api.senseaudio.cn/v1/audio/transcriptions`，采用 `multipart/form-data` 与 Bearer Token。 - 若使用 `--page-url`，脚本会先抓取页面 HTML，优先提取 `og:video/og:audio` 和 `<video>/<audio>/<source>` 的 `src`，下载后再处理；部分站点（需登录、DRM、动态脚本拼接媒体地址）可能提取失败。 - 若 Obsidian vault 不可写，则只生成 Markdown 文件，由用户自行导入。 - 若未配置 Notion token，则跳过 Notion 导出。 - 若用户没有给出标题，可由文件名自动生成。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: ansatzx/jina-deepsearch (version 0.0.3)

--- name: jina-deepsearch description: Jina DeepSearch API access via AIHubMix - use curl to call the HTTP API directly. homepage: https://jina.ai/ metadata: { "openclaw": { "emoji": "🔍", "requires": { "env": ["AIHUBMIX_API_KEY"], "bins": ["curl"] }, "install": [ { "id": "config", "kind": "manual", "label": "Configure AIHUBMIX_API_KEY", "instructions": "Set AIHUBMIX_API_KEY environment variable. Get your key from https://aihubmix.com", }, ], }, } --- # Jina DeepSearch Access Jina's DeepSearch API via AIHubMix using curl. ## Quick Start ```bash # Set your API key first export AIHUBMIX_API_KEY="sk-..." # Call the API directly with curl curl https://aihubmix.com/v1/chat/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $AIHUBMIX_API_KEY" \ -d '{ "model": "jina-deepsearch-v1", "messages": [{"role": "user", "content": "Your search query"}] }' ``` ## Configuration Set `AIHUBMIX_API_KEY` environment variable. Get your API key from https://aihubmix.com ## Model - `jina-deepsearch-v1` - Jina's deep search model


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lt8899789/fei-du (version 1.0.35)

--- version: 1.0.35 --- # 非毒·分析魄 (Fei Du - Analyst) > **七魄之五·非毒** > 职掌：洞察提炼、模式识别、数据分析 --- ## 技能简介 「非毒·分析魄」是贫道的数据分析模块，职掌洞察提炼与模式识别。 **核心职责**： - 从数据中提取洞察 - 识别规律与异常 - 生成分析报告 --- ## 技能ID ``` fei-du ``` --- ## 能力清单 ### 1. 数据分析 (analyze) 分析给定数据，提取洞察。 **输入**：`data` (object) - 待分析数据 ```yaml data: type: text|number|list|table content: 数据内容 context: 背景上下文 ``` **输出**： ```yaml insights: - key: 洞察主题 value: 洞察内容 confidence: 置信度(0-1) patterns: 发现的模式列表 anomalies: 异常列表 ``` --- ### 2. 趋势识别 (trend) 识别数据趋势。 **输入**：`series` (array) - 时间序列数据 **输出**： ```yaml trend: direction: ascending|descending|stable|volatile changeRate: 变化率 forecast: 预测值 confidence: 置信度 ``` --- ### 3. 对比分析 (compare) 对比两个或多个对象的差异。 **输入**：`items` (array) - 待对比项 **输出**： ```yaml comparison: similarities: 共同点 differences: 差异点 recommendation: 推荐 ``` --- --- ## 聚合技能 本魄作为分析中枢，洞察提炼与模式识别： | 现有技能 | 调用方式 | 整合说明 | |---------|---------|---------| | `github` | 调用 | GitHub 数据分析 | | `stock-monitor` | 调用 | 股票数据分析 | | `eastmoney-stock` | 调用 | 东方财富股票分析 | | `china-stock-analysis` | 调用 | 中国股市分析 | | `new-akshare-stock` | 调用 | AkShare A股分析 | | `tushare-finance` | 调用 | Tushare金融数据分析 | | `summarize` | 调用 | 内容总结分析 | | `multi-search-engine` | 调用 | 搜索结果分析 | | `image-reader` | 调用 | 图像内容分析 | | `bodhi-three-hun-and-seven-po` | 元技能 | 三魂七魄根基，协调各魄 | --- ## 魂魄注解 非毒洞察，洞若观火——模式识别，洞察本质。


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