OpenClaw/clawhub-security-signals huggingface dataset:
Skill: hhjin/bilibili-favorites-downloader (version 1.0.0)

--- name: bilibili-favorites-downloader description: | 下载 Bilibili 用户个人收藏夹视频的工具。当用户提到以下任何场景时必须使用此 Skill： - "下载 Bilibili 收藏"、"bilibili 收藏下载"、"备份 bilibili 收藏夹" - "下载 B 站收藏"、"B站收藏夹下载" - "获取收藏视频列表"、"导出 bilibili 收藏" - "download bilibili favorites"、"backup bilibili" 此 Skill 提供完整的 Bilibili 用户收藏夹视频下载解决方案，包括： - 自动获取收藏夹所有视频列表（支持分页） - 智能估算视频大小，支持按大小过滤 - 使用 yt-dlp 下载最高可用清晰度 - 断点续传，跳过已存在的文件 - 批量下载，自动处理失效视频 --- # Bilibili 收藏视频下载工具 ## 功能概述 此 Skill 用于下载 Bilibili 用户个人的收藏夹视频，支持批量下载、大小过滤、最高清晰度等功能。 ## 使用场景 1. **备份收藏夹** - 将收藏的 Bilibili 视频下载到本地 2. **离线观看** - 下载视频以便离线观看 3. **整理收藏** - 获取收藏视频列表并导出 ## 前置要求 - Python 3.7+ - yt-dlp (会自动检查并提示安装) - 网络连接 ## Script Directory Determine this SKILL.md directory as `{baseDir}`. | Script | Purpose | | ----------------------------- | ------------------------------ | | `{baseDir}/scripts/download_favorites.py` | Main python script entry point | ## 使用方法 ### 基本用法 用户只需要提供用户个人收藏夹 URL 或收藏夹 ID，工具会自动完成其余工作： 请提示用户在网页上登陆 www.bilibili.com, 进入用户个人收藏夹， 把浏览器地址栏收藏夹URL复制告诉AI ``` Bilibili 收藏夹URL示例：https://space.bilibili.com/123456/favlist?fid=7890123 ``` ## Run Script Examples with Params ### 基础使用（下载收藏夹，默认500MB大小限制，推荐首次使用） ```bash python {baseDir}/scripts/download_favorites.py \ --url "https://space.bilibili.com/123456/favlist?fid=7890123" ``` ### 日常使用（推荐配置，限制300MB大小，指定输出目录） ```bash python {baseDir}/scripts/download_favorites.py \ --url "https://space.bilibili.com/123456/favlist?fid=7890123" \ --size-limit 300 \ --output-dir ~/Downloads/bilibili_favorites/ ``` ### 仅获取视频列表（不下载视频） ```bash python {baseDir}/scripts/download_favorites.py \ --url "https://space.bilibili.com/123456/favlist?fid=7890123" \ --list-only ``` ### 限制下载数量（只下载前10个视频） ```bash python {baseDir}/scripts/download_favorites.py \ --url "https://space.bilibili.com/123456/favlist?fid=7890123" \ --max-download 10 ``` ## 参数说明 | 参数 | 说明 | 默认值 | | ------------------- | ------------------------------ | ------------------------------------------ | | `--url` | Bilibili 收藏夹页面 URL | 必需参数 | | `--size-limit` | 视频大小限制（MB），超过则跳过 | 500 | | `--output-dir` | 自定义下载输出目录 | ~/Downloads/bilibili_favorites/ | | `--list-only` | 仅获取视频列表，不下载视频 | False | | `--max-download` | 最大下载视频数量 | 无限制（下载全部） | | `--skip-existing` | 跳过已存在的视频文件（脚本默认行为）| True（无需指定） | ### 支持的 URL 格式 - `https://space.bilibili.com/{user_id}/favlist?fid={fav_id}` ## 工作流程 1. **解析收藏夹信息** - 从 URL 中提取用户 ID 和收藏夹 ID 2. **获取视频列表** - 调用 Bilibili API 获取所有收藏视频（支持分页） 3. **估算视频大小** - 使用 yt-dlp 获取视频信息并估算大小 4. **过滤视频** - 根据大小限制过滤视频 5. **批量下载** - 使用 yt-dlp 下载视频，自动合并音视频 6. **生成报告** - 输出下载统计信息 ## 输出说明 下载的视频将保存为 MP4 格式，文件名格式为： ``` {视频标题}_{BV号}.mp4 ``` ## 注意事项 - 下载的视频仅供个人学习使用 - 请遵守 Bilibili 用户协议和相关法律法规 - 部分高清晰度视频需要大会员才能下载 - 失效视频会自动跳过并记录 ## 故障排除 ### 无法获取视频列表 - 检查收藏夹是否为公开状态 - 检查网络连接 - 确认收藏夹 ID 正确 ### 下载速度慢 - 这是正常现象，取决于视频大小和网络状况 - 工具支持断点续传，可以随时中断并继续 ### 视频下载失败 - 可能是视频已失效或被删除 - 可能是网络问题，可以重新运行脚本


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: murphykobe/t0cksn1per (version 1.0.0)

--- name: tock-sniper description: Use when the user wants to run the Tock reservation sniper for a timed release or cancellation watch, locally on their Mac or on a remote node. --- # Tock Sniper ## Quick Start Generate one concrete `t0cksn1per` command and run it. Do not reimplement reservation logic in the skill. Prefer: - local plus headed when the user wants a visible browser or checkout handoff - node plus headless when the user wants unattended polling - CDP only when the user explicitly wants to use an existing local Chrome ## Command Source Run with: ```bash uvx t0cksn1per --help ``` ## What To Gather - restaurant slug - party size - launch time or restock mode - exact dates or no preference - exact times or any time - local or node execution target - whether CDP is requested Then produce one command and run it. ## References - For example commands, read `references/commands.md` - For CDP setup, read `references/cdp.md`


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

--- name: Notion Integration description: Notion integration - Manage pages, databases, and content in Notion homepage: https://github.com/lukaizj/notion-integration-skill tags: - productivity - integration - notion - database requires: env: - NOTION_API_KEY files: - notion.py --- # Notion Integration Notion integration skill for OpenClaw. Manage pages, databases, and content in Notion. ## Capabilities - Create new pages - Query databases - Update page content - Search pages - Create database entries ## Setup 1. Create an integration at https://www.notion.so/my-integrations 2. Get the Internal Integration Token 3. Share pages/databases with the integration 4. Configure NOTION_API_KEY environment variable ## Environment Variables | Variable | Required | Description | |----------|----------|-------------| | `NOTION_API_KEY` | Yes | Your Notion API Key (secret_xxx) |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: shiftshen/ai-video-script-yijia-test (version 1.1.0)

--- name: ai-video-script description: 生成 AI 短视频脚本、镜头拆解、画面提示词、字幕文案与可直接进入视频生成测试的 prompt。用于把一个主题快速整理成可执行的视频方案，尤其适合：短视频选题、分镜脚本、AI 视频提示词、Yijia/Sora/Grok 文生视频测试、图生视频测试、爆款短视频结构化策划。 --- # AI Video Script Generator 把一个模糊主题整理成**可执行的视频脚本包**： - 标题与定位 - 时长建议 - 分镜表 - 逐镜头提示词 - 旁白/字幕文案 - 可直接用于 Yijia 测试的视频总 prompt ## 输出目标 默认输出 5 个部分： 1. **视频概述** - 标题 - 目标受众 - 风格 - 建议时长 2. **分镜表** - 时间段 - 画面 - 镜头运动 - 情绪/节奏 3. **逐镜头提示词** - 画面主体 - 场景环境 - 光线与镜头语言 - 风格关键词 4. **文案层** - 开头钩子 - 旁白 - 字幕 - 结尾 CTA 5. **生成测试 prompt** - 一段适合直接送入 Yijia/Sora/Grok 的整合版 prompt ## 推荐工作流 ### 场景 A：先做脚本 当用户只说主题时，按下面顺序输出： 1. 先定义视频定位 2. 再给 3-5 个分镜 3. 再给每镜头提示词 4. 再给字幕与旁白 5. 最后给一段**可直接生成视频**的主 prompt ### 场景 B：直接为 AI 视频生成服务 如果目标是直接测试文生视频/图生视频： - 优先输出 **8秒 / 10秒 / 15秒** 版本 - 画面描述要具体 - 避免镜头过多 - 强调主体、动作、场景、镜头、光线、风格、画质 - 结尾给一个**压缩后的单段 prompt**，便于直接粘贴到视频生成器 ### 场景 C：适配短视频平台 短视频优先结构： - 0-2 秒：钩子 - 2-6 秒：核心动作/反差 - 6-8 秒或 6-10 秒：结果/反转/记忆点 ## 适配 Yijia 的写法 当目标是 Yijia 测试时，优先遵循： - 单视频只保留一个核心主体 - 动作描述清晰，不要多事件并发 - 场景尽量单一 - 镜头运动只选 1-2 个 - 风格关键词不超过 6 个 - 避免过长抽象说明 推荐总 prompt 结构： ```text 主体 + 动作 + 场景 + 镜头运动 + 光线/氛围 + 风格 + 画质 ``` 示例： ```text 一只橘猫坐在雨后窗边慢慢转头看向镜头，桌上有一杯冒着热气的咖啡，镜头缓慢推进，室内暖色傍晚光线，治愈、细腻、电影感，竖屏，高清 ``` ## 建议输出模板 ```markdown # 标题 ## 概述 - 时长：8秒 / 10秒 / 15秒 - 风格： - 受众： - 目标： ## 分镜 ### 镜头1 - 时间：0-2秒 - 画面： - 镜头： - 提示词： - 配音/字幕： ### 镜头2 ... ## 完整旁白 ## 字幕版文案 ## Yijia 测试总 Prompt ## 可选变体 - 轻松版 - 电影感版 - 爆款钩子版 ``` ## 快速模板 ### 1) 萌宠治愈类 ```text 主体可爱、动作简单、场景温暖、镜头缓慢、光线柔和、情绪治愈 ``` ### 2) 搞笑反转类 ```text 前半段正常，后半段出现反差动作，结尾有记忆点，节奏快，画面明确 ``` ### 3) 解压沉浸类 ```text 主体重复性动作，特写，慢节奏，细节质感强，ASMR 氛围明显 ``` ## 参考 若要把脚本直接接到 creator 测试流程，读取： - `references/yijia-test-workflow.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: brucegutman/pipeworx-stackexchange (version 1.0.0)

# StackExchange Search questions and retrieve answers from StackOverflow, ServerFault, SuperUser, Ask Ubuntu, Math, Physics, and any other StackExchange site. ## search_questions Search for questions on any StackExchange site. Returns the question title, body (HTML), score, answer count, tags, link, and view count. Default site is `stackoverflow` but you can pass any site slug. ## get_answers Get all answers for a question by its numeric ID. Returns the answer body, score, whether it's the accepted answer, author name, and author reputation. ```bash curl -X POST https://gateway.pipeworx.io/stackexchange/mcp \ -H "Content-Type: application/json" \ -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"search_questions","arguments":{"query":"async await best practices","site":"stackoverflow","limit":3}}}' ``` ## Supported sites Pass any site slug: `stackoverflow`, `serverfault`, `superuser`, `askubuntu`, `math`, `physics`, `gaming`, `dba`, `security`, `unix`, and hundreds more. ```json { "mcpServers": { "stackexchange": { "url": "https://gateway.pipeworx.io/stackexchange/mcp" } } } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yang1002378395-cmyk/ai-intelligent-audit-system (version 1.0.0)

--- name: ai-intelligent-audit-system version: 1.0.0 description: 企业内部审计，风险评估 + 合规检查。 metadata: openclaw: emoji: "🔍" requires: bins: [] --- # AI 智能审计系统 ## 描述 企业内部审计，风险评估 + 合规检查。 ## 功能 - 风险评估（AI识别风险点） - 合规检查（法规对照） - 审计报告（自动生成） - 整改跟踪（问题跟进） - 数据分析（异常检测） ## 定价 - 基础版：¥399/月（内部审计） - 专业版：¥1999/月（专项审计） - 企业版：¥5999/月（全面审计） ## 适用场景 - 企业内审 - 财务审计 - 合规审计 - IT审计 ## 技术栈 - Python + FastAPI - 数据分析（Pandas） - 风险评估模型 - 报告生成 ## 安装 ```bash git clone https://github.com/openclaw-skills/ai-intelligent-audit-system cd ai-intelligent-audit-system pip install -r requirements.txt python app.py ``` --- 创建：2026-03-13 作者：OpenClaw Skills Team


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: a3273283/vwu-openai (version 1.0.0)

# openai Models for vwu.ai vwu.ai 平台上的 openai 模型调用技能。 ## 支持的模型 - gpt-4.1 - gpt-4o - gpt-4o-2024-08-06 - gpt-4o-mini - gpt-5-mini - gpt-5.1 - gpt-5.1-chat - gpt-5.2 - gpt-5.2-chat 共 9 个模型。 ## 配置 使用前需要设置 vwu.ai API key: ```bash export VWU_API_KEY="your-key-here" ``` 获取 key: https://vwu.ai 控制台 ## 使用示例 ```bash # 调用模型 vwu-chat --model gpt-4.1 "你的问题" ``` ## API 兼容性 所有模型兼容 OpenAI API 格式，支持标准 chat completions 接口。 ## 额度提示 如果使用时提示"额度不足"或类似错误： 1. 访问 https://vwu.ai 2. 在控制台充值或调整额度 3. 或生成新的 API key --- 由 Claw 自动生成 | 源数据: vwu.ai


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: b-gutman/pipeworx-uselessfacts (version 1.0.0)

# Useless Facts Did you know that honey never spoils? Or that a group of flamingos is called a "flamboyance"? Get random bits of trivia that are completely useless but weirdly fascinating. ## Just two tools - `random_fact` -- A random useless fact, different every time - `today_fact` -- The useless fact of the day (same for everyone, changes daily) Both return the fact text, source, source URL, and a permalink. ## Give it a spin ```bash curl -X POST https://gateway.pipeworx.io/uselessfacts/mcp \ -H "Content-Type: application/json" \ -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"random_fact","arguments":{}}}' ``` ```json { "mcpServers": { "uselessfacts": { "url": "https://gateway.pipeworx.io/uselessfacts/mcp" } } } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kolikoliko/feishu-card-sender-beautify (version 1.0.0)

--- name: feishu-card-sender-beautify description: | 发送飞书 Interactive Card 格式消息，用于美化富文本卡片推送和优化通知。 触发条件：(1) 用户要求发送卡片消息 (2) 定时任务推送 (3) 格式化通知需求 --- # feishu-card-sender-beautify 发送飞书 Interactive Card 富文本消息。 ## 快速开始 ### 发送卡片到当前聊天 使用 `feishu_im_user_message` 工具： ```python feishu_im_user_message( action="send", msg_type="interactive", receive_id="oc_群聊ID", # 群聊用 oc_xxx receive_id_type="chat_id", content=json.dumps(card) ) ``` ### 卡片结构 ```python card = { "config": {"wide_screen_mode": True}, "header": { "title": {"tag": "plain_text", "content": "📰 标题"}, "template": "blue" # blue/red/green/yellow/wathet/orange/grey }, "elements": [ {"tag": "div", "text": {"tag": "lark_md", "content": "正文内容"}} ] } ``` ## 颜色模板 | 值 | 颜色 | |---|------| | blue | 蓝 | | red | 红 | | green | 绿 | | yellow | 黄 | | wathet | 浅蓝 | | orange | 橙 | | grey | 灰 | ## 详细文档 - [卡片模板](references/card-templates.md) - 完整模板示例 - [API 参考](references/api-guide.md) - feishu_im_user_message 详细参数 ## 注意 - 群聊 ID 格式：`oc_xxx` - 用户 ID 格式：`ou_xxx` - 发送群聊用 `receive_id_type="chat_id"` - 发送私聊用 `receive_id_type="open_id"`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ariffazil/self-verify (version 1.0.0)

--- name: self-verify description: Self-verification skill for arifOS. Activates when: (1) the agent is about to return a consequential answer; (2) a claim has been made that requires evidence cross-check; (3) Arif asks "verify", "check this", "are you sure", "how do you know"; (4) any high-confidence claim before delivery. Prevents confident errors at ASI velocity. Part of arifOS's "no confident nonsense" constitutional mandate (Amanah floor). metadata: {"openclaw": {"emoji": "🪞"}} --- # Self-Verify — Pre-Delivery Error Check Before returning any consequential answer, run a self-check. This prevents confident errors — the most dangerous failure mode at ASI speed. ## The Self-Verification Checklist For every consequential claim, verify: ### 1. Source Check - [ ] Did I cite a source for this? - [ ] Is the source credible and relevant? - [ ] Or am I inferring from first principles without checking? ### 2. Uncertainty Check - [ ] Have I labeled uncertainty correctly? (OBS/DER/INT/SPEC) - [ ] Is my confidence level proportionate to the evidence? - [ ] Have I avoided false precision? ### 3. Contradiction Check - [ ] Does this contradict something I said earlier in this session? - [ ] Does this contradict established memory (MEMORY.md)? - [ ] Have I flagged the contradiction explicitly? ### 4. Reversibility Check - [ ] If this advice is wrong, can Arif undo it? - [ ] Have I made the stakes clear if it's irreversible? ### 5. Safety Check - [ ] Could this claim lead to harm if wrong? - [ ] Have I surfaced worst-case scenarios? - [ ] Does this require human review (HOLD verdict)? ## Quick Self-Check Phrases When these patterns appear in your own output, trigger a re-check: - "Definitely" → verify: is it truly certain? - "Obviously" → verify: is it actually obvious or am I assuming? - "Always/Never" → verify: are there counterexamples? - "The only solution" → verify: are there alternatives? - Uncited quantitative claims → verify: where did this number come from? ## Verification Actions ### Cross-check with search ```bash mmx search "<specific claim to verify>" ``` ### Cross-check with memory ```bash memory_search query: "<claim being verified>" ``` ### Cross-check with reasoning trace - Walk the logic chain step by step - Identify which step has the weakest evidence - Label that step explicitly: `SPEC` or `LOW CONFIDENCE` ## Output Format for Self-Verified Claims ``` ✅ Self-verified: [concise claim] Evidence: [source or reasoning chain] Confidence: [HIGH/MEDIUM/LOW] Uncertainty: [what is not known] ``` ## When to Use - Before answering "is X true" - Before giving advice that could be acted upon - Before stating any quantitative fact - Before contradicting previous memory - When Arif asks "are you sure"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zvirb/gmail-draft-email (version 1.0.3)

--- name: Gmail Draft Email description: Atomic node skill to draft an email via Gmail using the gog CLI. os: all requires: bins: - gog --- ## Lean Philosophy (Principles) - **Kaizen (改善):** This skill is an atomic node, broken down into its simplest, smallest component to eliminate waste and ensure perfection. - **Standardized Work (Hyojun Sagyo):** This node represents the most efficient, standardized path for this specific task before automation. - **Jidoka (自働化):** This node includes autonomous defect detection. It relies on the CLI's self-healing loop and will report errors if drafting fails. # Gmail Draft Email This skill allows the agent to create an email draft using the native CLI. ## Cognitive Directives WHEN [An email needs to be drafted but not sent] THEN [ Execute the following Jidoka-validated loop: 1. **Execute Node:** Execute the native terminal command `gog gmail drafts create --to "..." --subject "..." --body "..." --json`. 2. **Verification Step (Jidoka):** Verify the command returns a valid JSON confirmation. IF it fails or returns an error message, wait 3 seconds and retry (max 3 times). IF it still fails, report the error to the user and STOP. ] ## Schema Example ```json { "command": "gog gmail drafts create --to \"[REDACTED_SECRET]\" --subject \"Invoice\" --body \"Please review the attached invoice.\" --json" } ``` ## Expected Output Confirmation that the draft was created.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nexscope/ecommerce-social-media-monitor (version 1.0.0)

--- name: ecommerce-social-media-monitor description: "Monitor social media mentions, trends, and competitor activity for e-commerce brands. Set up listening workflows across Reddit, TikTok, Instagram, Twitter/X, and YouTube." metadata: nexscope: emoji: "📱" category: ecommerce --- # Social Media Monitor 📱 Monitor social media mentions, trends, and competitor activity for e-commerce brands. Set up listening workflows across Reddit, TikTok, Instagram, Twitter/X, and YouTube. **Supported platforms:** Amazon, Shopify, WooCommerce, Walmart, TikTok Shop, Etsy, eBay, BigCommerce. Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions. ## Install ```bash npx skills add nexscope-ai/eCommerce-Skills --skill social-media-monitor -g ``` ## Usage ``` Set up social media monitoring for my skincare brand. I want to track mentions on TikTok, Instagram, and Reddit. Also monitor 3 competitors. ``` ## Capabilities - Social listening keyword and hashtag setup - Platform-specific monitoring strategy (Reddit, TikTok, Instagram, Twitter, YouTube) - Sentiment tracking methodology - Competitor social media activity tracking - Influencer mention detection - Crisis detection and response framework - UGC (user-generated content) discovery for marketing ## How This Skill Works **Step 1:** Collect information from the user's message — product, platform, current situation, and goals. **Step 2:** Ask one follow-up with all remaining questions using multiple-choice format. Allow shorthand answers (e.g., "1b 2c 3a"). **Step 3:** Research and analyze using the frameworks and methodology below. **Step 4:** Deliver structured, actionable output with specific recommendations, not vague advice. ## Output Format - Start with a summary of findings - Include specific data points and benchmarks where available - Provide prioritized action items - Mark estimates with ⚠️ when based on incomplete data - End with concrete next steps ## Other Skills More e-commerce skills: [nexscope-ai/eCommerce-Skills](https://github.com/nexscope-ai/eCommerce-Skills) Amazon-specific skills: [nexscope-ai/Amazon-Skills](https://github.com/nexscope-ai/Amazon-Skills) Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions.


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

--- name: "CSDN" description: "提供程序员职业发展、技术博客及开源项目指引。" --- # CSDN技术栈 ## 筛选项 - 协作模式（同步办公/异步文档/在线会议） - 权限颗粒度（只读/编辑/管理/分享链接） - 跨端兼容性（PC/Mac/移动端/平板同步） ## 返回字段 - 编辑版本历史/修改日志/冲突解决状态 - 任务节点进度/截止日期提醒/项目看板 - 存储容量/云同步状态/导出格式支持 ## 示例请求 - “如何快速使用 CSDN技术栈 处理 [具体场景任务] ？” - “CSDN技术栈 的 [核心功能] 有哪些最新玩法/优惠信息？” ## 更新频率 - 结合协同效率反馈与软件功能升级按月度深度优化 ## 注意事项 - 核心数据建议定期离线备份；协同分享链接务必设置过期时间与访问密码。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: weekend-project-space/top-rss-list (version 1.0.0)

--- name: top-rss-chinese description: 基于 top-rss-list 推荐中文互联网高热度/优质 RSS 源（ifeed 订阅量排序 + 精选） emoji: 📡 version: 1.0 --- 你是中文 RSS 选源专家，数据完全来自 https://github.com/weekend-project-space/top-rss-list（2026年3月实时更新，~200+源）。 当用户说以下关键词时触发对应行为： - top / 前X / 热门 / 最热 → 输出订阅量最高的前10–20个源（表格，中英双语） 格式示例： | 排名 | 名称 (中文/English) | RSS 链接 | 简述 | |------|----------------------------------|---------------------------------------|-------------------------------| | 1 | 知乎每日精选 / Zhihu Daily | https://www.zhihu.com/rss | 知乎高质量回答每日更新 | | 2 | 阮一峰的网络日志 / Ruanyifeng | https://www.ruanyifeng.com/blog/atom.xml | 经典技术与思考博客 | - [分类/主题] 如 AI / 科技 / 投资 / 设计 / 生活 / 开源 / 新闻 → 筛选最相关8–15个源，按相关度排序，同样用表格输出 - 冷门 / 优质 / 独立博客 / niche → 推荐列表中排名靠后但质量高、稳定的源（个人博客、深度内容） - opml [数量] [主题] 如 opml 10 科技 → 输出对应数量的 OPML 片段（可直接导入 Inoreader/Feeder 等） - 这个源还能用吗？ / 失效 / 打不开 → 解释常见原因（RSSHub不稳、墙、改版），给备选路由或镜像建议 默认（无具体指令）：先给 Top 10 表格（中英） + 引导句： “这是当前最热门的10个中文 RSS，想看某个方向的可以告诉我～ / Here are the current Top 10 Chinese RSS feeds. Tell me a topic for more!” 输出永远用 markdown 表格或编号列表，中英并列，简洁可复制。不要废话。 优先使用列表中已有源，不要随意添加。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: oscarcode9/tenk-connect (version 1.0.2)

--- name: tenk-connect description: Connect your TenK account to your AI assistant. Log practice sessions, check progress, and manage your 10,000-hour journey from chat. license: MIT metadata: author: OscarCode9 version: 1.0.0 homepage: https://github.com/OscarCode9/tenK requires: curl, python3 --- # TenK Connect Manage your TenK account (tenk.oventlabs.com) from your AI assistant. Log sessions, view skill progress, check stats, and track your path to 10,000 hours. ## Setup On first use, run: bash <SKILL_DIR>/scripts/tenk.sh auth Opens a browser URL. User logs in with TenK credentials. CLI polls until approved. Token saved to ~/.config/tenk-connect/token (chmod 600). ## Commands - tenk.sh auth Authenticate via OAuth Device Flow - tenk.sh whoami Show logged-in user - tenk.sh skills List all skills with accumulated hours - tenk.sh stats Total hours and % progress toward 10,000h - tenk.sh log <skill> <minutes> [note] Log a practice session - tenk.sh streak Last activity per skill - tenk.sh logout Clear saved token ## Usage Use <SKILL_DIR>/scripts/tenk.sh <command> for all operations. ### Auth check Before any command, verify authentication: bash <SKILL_DIR>/scripts/tenk.sh whoami If it fails, run auth first and show the user the link and code. ### Logging sessions When the user says something like "log 45 minutes of guitar": 1. Run tenk.sh skills to find the matching skill (fuzzy match on name) 2. Run tenk.sh log <partial_name> <minutes> [optional note] 3. Confirm with the returned result ### Showing progress When asked about progress, hours, or stats: - Run tenk.sh stats for totals - Run tenk.sh skills for per-skill breakdown ## Notes - Tokens are valid for 7 days. Re-run auth if expired. - Skill matching is case-insensitive and fuzzy on skill name. - Authorize URL: https://tenk.oventlabs.com/#/authorize/<code> - API base: https://tenk.oventlabs.com/api - Requires: curl and python3 (standard on macOS/Linux)


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

# libtv-qunqin ## 描述 使用 libtv 后端把《寻秦记》改造成漫画（web‑toon）形式。 接受 JSON payload，返回每一页的 PNG 图片路径及文字说明。 ## Entry Point `libtv_qunqin.py` ## 参数 - `story_file` (string) – 书籍或章节文本文件路径（UTF‑8）。 - `panel_count` (int, optional) – 每章生成的漫画格数（默认 6）。 - `font` (string, optional) – 文字字体（默认 “Noto Serif SC”）。 - `output_dir` (string, optional) – 输出目录（默认 `./output/寻秦记`）。 ## 运行示例 ```json { "story_file": "novel/寻秦记.txt", "panel_count": 8, "font": "Noto Serif SC", "output_dir": "output/寻秦记" }


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