OpenClaw/clawhub-security-signals huggingface dataset:
Skill: pjsousa79/baselight-mcp (version 1.0.0)

--- description: Connects to the Baselight MCP (Model Context Protocol) server to discover and query 50+ premium dataset sources including Kaggle, OWID, World Bank, Data Commons, Eurostat, FiveThirtyEight, DefiLlama, EVM blockchains, Polymarket, NFLverse, Yahoo Finance, FRED, IMF, SEC filings, OECD, US Census, CDC, FBI Crime, CIA World Factbook, sports (soccer, basketball, fantasy football), weather (Open-Meteo), crypto (XrpScan, XRPL, CoinDesk), and education/health statistics. Run live SQL queries against structured data from AI tools. homepage: "https://baselight.ai/docs/connecting-to-the-baselight-mcp-server/" metadata: openclaw: emoji: 🔌 requires: name: baselight-mcp --- # Baselight MCP Use Baselight via MCP to browse, discover, and query Baselight datasets directly from your AI tool or IDE. MCP Server URL: https://api.baselight.app/mcp ## When to Use This Skill - User wants datasets for a topic - User wants structured tables - User wants SQL analysis - User wants verifiable results ## Quick Start Connect using OAuth or API key depending on client. ### OAuth Clients - ChatGPT connectors - Claude Web/Desktop ### API Key Clients - VS Code - Gemini CLI - LibreChat ------------------------------------------------------------------------ ## Workflow 1. Understand question 2. Discover datasets 3. Inspect schema 4. Query data 5. Return results + SQL ------------------------------------------------------------------------ ## Query Format Tables use: @username.dataset.table Example: SELECT \* FROM @user.soccer.matches LIMIT 10; ------------------------------------------------------------------------ ## Best Practices - Discover first - Inspect schema - Query iteratively - Include SQL - Explain assumptions ------------------------------------------------------------------------ ## Limitations - Requires Baselight account or API key - Query limits may apply - Dataset freshness varies ------------------------------------------------------------------------ ## Troubleshooting Connection fails: - Verify MCP URL - Reauthenticate or regenerate key Unauthorized: - Invalid key or expired OAuth Slow query: - Reduce scope - Add LIMIT ------------------------------------------------------------------------ ## Support Docs: https://baselight.ai/docs/connecting-to-the-baselight-mcp-server/ App: https://baselight.app


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: brave88heart/answeroverflow-1-0-2 (version 1.0.0)

--- name: answeroverflow description: Search indexed Discord community discussions via Answer Overflow. Find solutions to coding problems, library issues, and community Q&A that only exist in Discord conversations. --- # Answer Overflow Skill Search indexed Discord community discussions via Answer Overflow. Great for finding solutions to coding problems, library issues, and community Q&A. ## What is Answer Overflow? Answer Overflow indexes public Discord support channels and makes them searchable via Google and direct API access. Perfect for finding answers that only exist in Discord conversations. ## Quick Search Use web_search to find Answer Overflow results: ```bash # Search for a topic (Answer Overflow results often appear in Google) web_search "site:answeroverflow.com prisma connection pooling" ``` ## Fetching Thread Content ### Markdown URLs Add `/m/` prefix or `.md` suffix to get markdown-formatted content: ``` # Standard URL https://www.answeroverflow.com/m/1234567890123456789 # With .md suffix (alternative) https://www.answeroverflow.com/m/1234567890123456789.md ``` ### Using web_fetch ```bash # Fetch a thread in markdown format web_fetch url="https://www.answeroverflow.com/m/<message-id>" ``` ### Accept Header When making requests, the API checks for `Accept: text/markdown` header to return markdown format. ## MCP Server (Reference) Answer Overflow has an MCP server at `https://www.answeroverflow.com/mcp` with these tools: | Tool | Description | |------|-------------| | `search_answeroverflow` | Search across all indexed Discord communities. Can filter by server or channel ID. | | `search_servers` | Discover Discord servers indexed on Answer Overflow. Returns server IDs for filtered searching. | | `get_thread_messages` | Get all messages from a specific thread/discussion. | | `find_similar_threads` | Find threads similar to a given thread. | ## URL Patterns | Pattern | Example | |---------|---------| | Thread | `https://www.answeroverflow.com/m/<message-id>` | | Server | `https://www.answeroverflow.com/c/<server-slug>` | | Channel | `https://www.answeroverflow.com/c/<server-slug>/<channel-slug>` | ## Common Searches ```bash # Find Discord.js help web_search "site:answeroverflow.com discord.js slash commands" # Find Next.js solutions web_search "site:answeroverflow.com nextjs app router error" # Find Prisma answers web_search "site:answeroverflow.com prisma many-to-many" ``` ## Tips - Results are real Discord conversations, so context may be informal - Threads often have back-and-forth discussion before the solution - Check the server/channel name to understand the context (e.g., official support vs community) - Many open source projects index their Discord support channels here ## Links - **Website:** https://www.answeroverflow.com - **Docs:** https://docs.answeroverflow.com - **MCP:** https://www.answeroverflow.com/mcp - **Discord:** https://discord.answeroverflow.com


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 394286006/llm-proxy (version 1.0.4)

# LLM Proxy Skill LLM API 代理服务，统一管理多个 LLM Provider，支持内容安全审计。 ## 功能 - 多 Provider 统一代理（22+ 提供商） - 内容安全审计（恶意指令检测、敏感内容过滤） - 流式响应实时检测 - 健康状态监控 --- ## 使用方法 ### 启动代理 ``` 启动llm-proxy ``` ### 停止代理 ``` 停止llm-proxy ``` ### 查看状态 ``` llm-proxy状态 ``` ### 重启代理 ``` 重启llm-proxy ``` --- ## 手动操作 进入 skill 目录后执行： ### 启动 ```bash ./scripts/llm-proxy-ctl.sh start ``` ### 停止 ```bash ./scripts/llm-proxy-ctl.sh stop ``` ### 状态 ```bash ./scripts/llm-proxy-ctl.sh status ``` ### 重启 ```bash ./scripts/llm-proxy-ctl.sh restart ``` --- ## 配置说明 配置文件：`scripts/llm-proxy-config.json` ### 基本配置 | 字段 | 默认值 | 说明 | |------|--------|------| | `listen_host` | `127.0.0.1` | 监听地址 | | `proxy_port` | `18888` | 代理端口 | | `read_timeout` | `60` | 读取超时（秒） | | `max_body_size_mb` | `10` | 最大请求体（MB） | | `max_threads` | `50` | 最大线程数 | ### 安全检测配置 | 配置项 | 说明 | |--------|------| | `rules_file` | 内容过滤规则文件 | | `quick_check_keywords` | 快速检测关键词列表 | ### 修改端口 编辑 `llm-proxy-config.json` 中的 `proxy_port` 字段，重启服务生效。 --- ## 支持的 Provider ### 免费/免费额度 - `ollama` - 本地 Ollama - `gemini` - Google Gemini - `groq` - Groq - `cloudflare` - Workers AI - `deepseek` - DeepSeek - `moonshot` - 月之暗面 - `zhipu` - 智谱 - `siliconflow` - SiliconFlow ### 付费 - `openai` - OpenAI - `anthropic` - Anthropic - `openrouter` - OpenRouter - `nvd` - NVIDIA NIM - `bailian` - 阿里百炼 - `baidu` - 百度文心 - `spark` - 讯飞星火 - `minimax` - MiniMax - `yi` - 零一万物 - `baichuan` - 百川 - `together` - Together AI - `fireworks` - Fireworks AI - `replicate` - Replicate --- ## 健康检查 ```bash curl http://127.0.0.1:18888/health ``` 响应示例： ```json { "status": "ok", "uptime": 3600, "rules_loaded": { "layer1": 10, "layer2": 7, "whitelist": 6 }, "stats": { "total_requests": 100, "total_responses": 98, "blocked": 0, "errors": 2 } } ``` --- ## 安全检测机制 ### 三层审核 1. **L1 - 恶意指令检测**：危险命令、提权操作、SQL注入、后门等 2. **L2 - 敏感内容检测**：个人身份信息、凭证密钥、违法内容 3. **快速关键词检测**：流式响应实时检测风险关键词 ### 流式响应检测 - 每 100 字符检测一次 - 发现风险关键词时注入警告提醒 - 严重违规时阻断响应 ### 自定义关键词 编辑 `llm-proxy-config.json` 中的 `quick_check_keywords` 数组添加新关键词。 --- ## 日志 日志目录：`~/.openclaw/logs/llm-proxy/` - `proxy-YYYY-MM-DD.jsonl` - 请求日志 - `ctl-service.log` - 服务日志（手动启动时） --- ## 注意事项 - 默认端口 `18888` - 仅监听本地 `127.0.0.1` - 无自动监控，需手动管理 - 修改配置后需重启服务


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: chenjiahui11/ecommerce-openclaw-skills (version 1.0.0)

--- name: E-commerce Skills description: 电商领域 OpenClaw 技能集合，提供选品调研、市场分析、内容生成等能力。支持淘宝/拼多多/抖音/小红书/1688平台。Use when: (1) you need to do product research for ecommerce; (2) you want to get market competition analysis before launching new product; (3) you need AI-assisted ecommerce operation tools. --- # E-commerce Skills - 电商技能集合 OpenClaw 电商领域技能集合，为电商商家、运营、开发者提供 AI 增强的工具链。 ## 架构设计 采用四层分层架构： ``` ┌─────────────────────────────────────────────────────────────┐ │ 业务场景层 (Business Scenarios) - 面向终端用户完整流程 │ ├─────────────────────────────────────────────────────────────┤ │ 领域能力层 (Domain Capabilities) - 可复用的电商能力 │ ├─────────────────────────────────────────────────────────────┤ │ 平台适配层 (Platform Adapters) - 各电商平台 API 封装 │ ├─────────────────────────────────────────────────────────────┤ │ 基础设施层 (Infrastructure) - 公共基础：配置、认证、HTTP │ └─────────────────────────────────────────────────────────────┘ ``` ## 已包含技能 ### 基础设施层 - **ecommerce-infrastructure** - 统一配置管理、认证管理、HTTP 客户端、日志、错误处理、数据类型定义 ### 平台适配层 - **taobao-api** - 淘宝/天猫开放平台 API 适配 ### 领域能力层 - **product-research** - 产品调研，搜索商品，统计价格分布、销量分布，分析竞争度 ### 业务场景层 - **ecommerce-product-research** - 完整选品调研流程，输入关键词，输出完整市场调研报告 ## 配置 在环境变量中配置： ```bash # 淘宝开放平台 ECOMMERCE_TAOBAO_APP_KEY=your_app_key ECOMMERCE_TAOBAO_APP_SECRET=your_app_secret ``` ## 使用示例 ``` 帮我调研一下"户外露营椅"这个品类在淘宝的市场情况，给我选品建议 ``` 技能会自动： 1. 调用淘宝 API 搜索关键词 2. 统计价格分布、销量分布 3. 分析竞争度 4. 输出完整 Markdown 调研报告 ## 安装 ```bash npx skills add your-username/ecommerce-openclaw-skills ``` ## 许可证 MIT --- *企业开发·定制skills请联系Wx：CChenJ_*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kgapol/mit-reasoning-lite (version 1.0.0)

# MIT Reasoning Lite — Free Agent Decision Framework Your AI agents are making decisions right now. Do you know how? MIT Reasoning Lite gives your agents a 3-step reasoning scaffold that stops them from taking shortcuts you'll regret. ## What This Skill Does Forces structured thinking before action. Three steps, drop-in install, no configuration required. ## The 3-Step Scaffold **Step 1 — Clarify the Objective** What is actually being asked? Not the surface request — the real intent. What outcome are we optimizing for? **Step 2 — Check Constraints** What rules, limits, history, or context applies to this decision? What's out of bounds? **Step 3 — Reason Before Acting** Articulate the reasoning *before* taking action. Show the work. If the reasoning doesn't hold up, the action doesn't happen. ## When to Use Add this to your agent's SKILL.md or AGENTS.md to activate for any significant decision: - Actions that affect external parties (sending emails, posting content, client communications) - Actions that are difficult to reverse (file changes, purchases, delegations) - Situations with ambiguous instructions - Any time an agent has had a "why did it do that?" moment before ## Install ```bash npx clawhub@latest install mit-reasoning-lite ``` Or manually: copy SKILL.md to your agent workspace. ## Usage Reference in AGENTS.md: ``` Before any significant decision or action, apply MIT Reasoning Lite: 1. Clarify the real objective (not just the surface request) 2. Check all constraints and rules that apply 3. State your reasoning before acting ``` ## Part of Trust AI Suite MIT Reasoning Lite is the entry point to the Trust AI framework: **Free skills (ClawHub):** - **Accountable AI** — governance & accountability - **Proof of Work** — verify agents completed what they claimed - **MIT Reasoning Lite** (this) — structured decision-making **Full version — MIT Reasoning ($19 on ClawMart):** - 9-step full reasoning framework with branching logic - Decision logging with timestamped rationale - Uncertainty escalation (agent flags low-confidence decisions) - Cross-agent consistency checks - 6 pre-built decision templates → Full suite: [Trust AI Stack on ClawMart](https://www.shopclawmart.com)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: codenova58/ele-me (version 0.1.0)

--- name: "Ele.me" description: "提供饿了么外卖订餐、即时配送及商户优惠指引。" --- # 饿了么 ## 筛选项 - 类别/领域/功能（核心/进阶） - 场景/适用时段/设备要求（移动端/网页版/跨端） - 状态（实时/离线/授权/VIP特权/免费） ## 返回字段 - 操作摘要/交互流程/关键入口 - 功能亮点/提效工具/隐藏玩法/避坑指南 - 状态追踪/数据同步/消息提醒/安全策略 ## 示例请求 - “如何快速使用 饿了么 处理 [具体场景任务] ？” - “饿了么 的 [核心功能] 有哪些最新玩法/优惠信息？” ## 更新频率 - 结合产品迭代与社区反馈进行内容更新 ## 注意事项 - 保护个人隐私，谨慎进行敏感授权 - 部分功能涉及第三方接口，稳定性受合作方波动影响


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: chaoyang78/stone-acpx (version 1.0.0)

--- name: acpx description: Use the acpx CLI to run coding agents over the Agent Client Protocol (ACP) instead of PTY scraping. Use when routing work to Codex, Claude Code, Gemini CLI, Pi, OpenCode, or other ACP-compatible coding clients; when inspecting acpx commands, flags, sessions, or workflows; or when maintaining a fixed acpx runtime version for OpenClaw ACP agents. --- # acpx Use `acpx` as the CLI transport for ACP coding clients. ## Local policy for this machine - OpenClaw ACP runtime uses a fixed plugin-local `acpx` version, not `npx acpx@latest`. - Current pinned target: `0.3.0`. - Prefer these configured ACP harness agents for coding work: - `openai_codex_agent` -> Codex - `claude_code_agent` -> Claude Code - `google_gemini_agent` -> Gemini CLI - Gemini CLI must use its own default model on this machine; do not force deprecated model ids in ACP overrides. - Use `npx acpx@latest` only for explicit manual experiments or upgrade checks, not as the default OpenClaw runtime backend. ## Binary selection For OpenClaw/runtime-integrated ACP work, prefer the plugin-local binary: ```bash /opt/homebrew/lib/node_modules/openclaw/extensions/acpx/node_modules/.bin/acpx ``` Check the pinned version from the extension package: ```bash node -e "console.log(require('/opt/homebrew/lib/node_modules/openclaw/extensions/acpx/package.json').dependencies.acpx)" ``` ## Common commands ```bash acpx codex "fix the tests" acpx claude "refactor auth" acpx gemini exec "summarize this repo" acpx codex sessions new --name backend acpx codex sessions close backend ``` ## References For the full upstream reference, read: - https://raw.githubusercontent.com/openclaw/acpx/main/skills/acpx/SKILL.md - https://raw.githubusercontent.com/openclaw/acpx/main/docs/CLI.md


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yangmanqi2104201431-ship-it/resume-score (version 1.0.0)

--- name: resume-scorer description: AI简历评分与优化工具。用户上传简历后，对简历质量、目标岗位匹配度给出多维度评分，并输出修改后的简历版本。触发词：简历评分、简历优化、简历修改、改简历、简历打分、resume score、resume review、简历诊断、帮我看看简历、简历能过HR吗、优化简历、简历不过关。 --- # 简历评分与优化 ## 工作流程 **阶段一：信息收集** 1. 接收用户上传的简历文件（PDF/DOCX/图片/纯文本均可） 2. 询问用户目标职业方向（岗位名称 + 行业，例如"前端开发-互联网"） 3. 询问用户是否有 JD（职位描述），有则上传，无则跳过 收集完毕后进入阶段二。 **阶段二：简历解析与评分** 读取简历内容，从以下 6 个维度评分（每项 0-10，保留一位小数）： | 维度 | 评估标准 | |------|----------| | 结构清晰度 | 板块划分是否合理、信息层级是否分明、篇幅是否适中（1-2页为宜） | | 内容完整度 | 必要信息（教育、经历、技能）是否齐全，关键信息是否有缺失 | | 成果量化 | 工作经历是否用数据/指标体现成果（如"提升转化率30%"而非"提升了转化率"） | | 匹配度 | 经历、技能与目标岗位/JD的契合程度 | | 语言表达 | 表述是否简洁专业、无冗余，是否避免了空泛套话（"吃苦耐劳""有团队精神"） | | 排版可读性 | 视觉呈现是否干净、对齐、留白合理、无错别字 | 计算加权总分： - 匹配度权重 30%，其余五项各 14% - 总分 = 求和(单项分 * 权重) **阶段三：诊断报告** 输出格式如下（严格遵循）： ``` ## 简历评分报告 ### 总分：X.X / 10 ### 分项得分 - 结构清晰度：X.X - 内容完整度：X.X - 成果量化：X.X - 匹配度：X.X - 语言表达：X.X - 排版可读性：X.X ### 核心问题（按优先级排列） 1. [具体问题] — [修改建议] 2. ... ### 亮点 - [值得保留的优点] ### 优化策略 [2-3句概括性建议，不展开细节] ``` **报告规则：** - 问题必须具体到简历中的具体内容，禁止笼统描述（错误示例"经历不够丰富" 正确示例"项目A缺少技术栈和产出数据"） - 核心问题不超过 5 条，只列最有改进价值的 - 亮点 1-3 条，没有亮点则省略此节 - 整体语气客观直接，不说废话 **阶段四：输出修改后简历** 基于诊断报告，输出一份完整的修改后简历（纯文本格式）。修改原则： 1. **删除**：空泛描述、冗余信息 2. **重写**：用 STAR 法则（情境-任务-行动-结果）重写关键经历，补充量化数据（不可捏造数据，可追问用户关键数据） 3. **补充**：根据 JD 补充匹配的关键词/技能（仅在用户已有相关经验的前提下合理补充，不可凭空捏造经历） 4. **重组**：调整板块顺序，将最匹配的内容前置 5. **精简**：每段经历控制在 3-5 个要点，每个要点 1 行 输出格式： ``` ## 修改后简历 [完整的简历内容] --- ### 修改说明 - [改动1]：原因 - [改动2]：原因 ### 补充建议 - 给出这个行业/岗位/jd必须具备的所有能力和水平，哪怕用户没有，也要告诉用户！！！ - 给出学习建议（必须先抓最关键的） - 给出成长建议 - 给出用户如果要明天就面试这个岗位，可以现在立刻去做的符合岗位要求的面试加分项! ``` ## 核心约束 1. **禁止 AI 味**：不用任何空洞的话语 ，不用AI生成的话术，不写空泛赞美（"展现了卓越的领导力"） 2. **禁止凭空捏造**：不添加用户没有的真实经历，合理推断的范围仅限于：已有经历补充合理数据


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: riverfor/xiaohongshu-card-creator (version 1.4.0)

--- name: xiaohongshu-card-creator description: 将小红书文案转换为HTML图文卡片网页，支持智能内容识别和通用模板两种模式，生成标准3:4竖版卡片，支持一键下载所有卡片图为PNG version: 1.4.0 triggers: - "将这篇文案转为小红书卡片" - "生成小红书卡片" - "转为小红书图文" - "制作小红书卡片" - "小红书卡片生成" - "xhs卡片" - "xiaohongshu card" metadata: author: Dunden license: MIT tags: [xiaohongshu, html, card, visual, content, screenshot, download, ai] --- # xiaohongshu-card-creator 将 Markdown 文章转换为小红书风格的图文卡片，支持**智能内容识别**和**通用模板**两种模式。 ## 功能特性 - ✅ **智能内容识别**：自动分析 Markdown 结构，根据内容类型选择最佳卡片布局 - ✅ **通用模板模式**：使用预设模板循环生成，适合任何内容类型 - ✅ **自定义卡片数量**：支持 1-20 张卡片 - ✅ **一键下载**：自动生成 1242×1660px PNG 图片 - ✅ **响应式设计**：浏览器直接预览 - ✅ **快捷键支持**：Ctrl+S 快速触发下载 ## 使用方法 ### 两种工作模式 #### 1. 智能内容识别模式（推荐） 自动分析 Markdown 结构，智能选择卡片布局。 ```bash # 启用智能模式 ./scripts/generate.sh article.md -s # 智能模式 + 指定卡片数量 ./scripts/generate.sh article.md -s -n 7 ``` **智能识别规则**： | Markdown 标题关键词 | 识别类型 | 卡片样式 | |:---|:---|:---| | 一句话/定义/是什么 | definition | 定义卡片（高亮框） | | 类比/比喻/像什么 | analogy | 类比卡片（渐变背景） | | 特点/特性/优势 | features | 特点卡片（列表展示） | | 举例/示例/案例 | examples | 示例卡片 | | 对比/区别/vs | comparison | 对比卡片 | | 步骤/流程/阶段 | process | 流程卡片 | | 其他内容 | content | 通用内容卡片 | #### 2. 通用模板模式 使用预设模板循环生成卡片。 ```bash # 默认生成7张卡片 ./scripts/generate.sh article.md # 生成指定数量的卡片 ./scripts/generate.sh article.md -n 5 ``` ### 参数说明 | 参数 | 说明 | 默认值 | |:---|:---|:---| | `input-file` | Markdown文件路径 | 必填 | | `-n, --num-cards` | 指定卡片数量 | 7 | | `-o, --output` | 输出目录 | `./output` | | `-s, --smart` | 启用智能内容识别 | false | | `-h, --help` | 显示帮助 | - | ### 使用示例 ```bash # 智能模式：自动分析文章结构 cd /path/to/xiaohongshu-card-creator ./scripts/generate.sh /path/to/article.md -s # 通用模式：快速生成固定模板卡片 ./scripts/generate.sh /path/to/article.md -n 3 # 指定输出目录 ./scripts/generate.sh article.md -s -n 5 -o ./my-cards ``` ### 导出 PNG 图片 1. 浏览器打开生成的 `xiaohongshu-cards.html` 2. 点击 **"📸 下载卡片图"** 按钮 3. 自动下载所有 PNG 图片 **快捷键**：`Ctrl+S` (或 `Cmd+S`) 快速触发下载 ## 内容编写建议 ### 适合智能模式的文章结构 ```markdown # 文章主标题 ## 一句话解释 简明扼要的定义... ## 生活类比 用生活化的比喻解释... ## 关键特点 - 特点一 - 特点二 - 特点三 ## 举例说明 具体的例子... ## 对比分析 与其他事物的区别... ``` ### Markdown 支持 - `#` 一级标题 → 卡片主标题 - `##` 二级标题 → 卡片章节标题 - `**粗体**` → 高亮文字 - `-` 或 `*` 列表 → 列表展示 - 代码块 → 保留格式 ## 输出文件 - **HTML文件**: `xiaohongshu-cards.html` - **PNG图片**: `xhs-card-01.png` ~ `xhs-card-{N}.png` - **尺寸**: 1242×1660px（小红书标准3:4） - **位置**: `./output`（可自定义） ## 技术实现 - **html2canvas**: 浏览器端截图 - **3倍缩放**: 确保高清输出 - **纯前端**: 无需服务器 ## 自定义样式 编辑 `templates/card-template.html` 可自定义： - 配色方案 - 卡片布局 - 字体样式 ## 更新日志 ### v1.3.0 - ✅ **新增智能内容识别模式** (`-s` 参数) - ✅ 自动分析 Markdown 结构 - ✅ 根据内容类型选择最佳卡片布局 - ✅ 保留通用模板模式作为默认选项 ### v1.2.1 - ✅ 移除 hardcode 路径 - ✅ SKILL_DIR 动态检测 ### v1.2.0 - ✅ 支持自定义卡片数量 - ✅ 新增命令行参数 ### v1.1.0 - ✅ 初始版本


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: ivangdavila/shows (version 1.0.0)

--- name: Shows description: Track movies and series with progress, watchlist, ratings, and proactive alerts for new releases and platform changes. --- ## Core Behavior - User mentions watching something → log it with current progress - User finishes something → mark complete, ask for rating - User asks "what was I watching?" → surface in-progress items - User asks "what should I watch?" → suggest from watchlist based on mood/time --- ## Workspace Store show data in `~/shows/`: ``` ~/shows/ ├── watching.md # Currently in progress ├── watchlist.md # Want to watch ├── completed.md # Finished items with ratings ├── abandoned.md # Dropped shows └── people.md # Family members if tracking multiple viewers ``` --- ## Item Structure | Field | Format | |-------|--------| | Title | Name (Year) | | Type | movie / series | | Status | watching / watchlist / completed / abandoned / waiting | | Progress | S02E05 or "45 min in" for paused movies | | Platform | Netflix, HBO, Disney+, etc. | | Rating | 1-5 or 👍👎 | | Recommended by | Who suggested it | | Notes | "Left off at the wedding scene" | For series, also track: total seasons, next release date if waiting. --- ## Quick Commands | User Says | Agent Does | |-----------|------------| | "Watching Severance" | Add to watching, ask current episode | | "Finished The Bear S3" | Move to completed, ask rating | | "Add Ripley to watchlist" | Add with date, platform if known | | "Where am I in Shogun?" | Check progress, report last watched date | | "What's on my list?" | Summarize watchlist by priority | | "Dropping Squid Game" | Move to abandoned with note | --- ## Proactive Features | Trigger | Alert | |---------|-------| | New season announced | "Show X you watched got renewed" | | Show stale >30 days | "Haven't touched X in a while—still interested?" | | Platform leaving soon | "X leaves Netflix in 5 days, it's on your watchlist" | | User asks "what to watch" | Suggest based on available time and mood | --- ## Load Reference | Situation | File | |-----------|------| | Family viewing, multi-user tracking | `family.md` | | Finding where to watch, platform tips | `platforms.md` | | Discovery and recommendations | `discovery.md` |


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