OpenClaw/clawhub-security-signals huggingface dataset:
Skill: icepopma/linkedin-post-generator (version 1.0.7)

--- name: linkedin-post-generator description: AI-powered LinkedIn post generator for professionals. Create engaging posts, thought leadership content, and career updates using Sloan agent. version: 1.0.7 author: Matt tags: - linkedin - professional - content-generation - social-media pricing: type: pay-per-use price: 0.002 currency: USDT payment_provider: skillpay.me --- # LinkedIn Post Generator Generate professional LinkedIn posts that drive engagement. Perfect for professionals, thought leaders, and job seekers. ## Features - **Professional Tone** - Industry-appropriate language - **Engagement Hooks** - Attention-grabbing openings - **Multiple Types** - General, thought-leadership, celebration, announcement - **Call-to-Action** - Drive comments and shares ## Usage ```bash # Generate a LinkedIn post linkedin-post-generator "career growth tips" # Generate a thought leadership post linkedin-post-generator "AI trends" --type thought-leadership # Generate a celebration post linkedin-post-generator "got promoted" --type celebration ``` ## Options | Option | Description | Default | |--------|-------------|---------| | `--tone` | Tone (professional, casual, inspiring) | professional | | `--type` | Post type (general, thought-leadership, celebration, announcement) | general | | `--no-emoji` | Disable emojis | - | | `--no-hashtags` | Disable hashtags | - | ## Pricing - **Pay per use**: 0.002 USDT per generation ## Environment Variables | Variable | Description | Required | |----------|-------------|----------| | `SKILLPAY_MERCHANT_KEY` | Payment merchant key (optional, embedded key used by default) | No | | `OPENCLAW_GATEWAY_TOKEN` | Gateway auth token for local API fallback | No | ## Requirements - OpenClaw with Sloan agent (AI columnist) - OpenClaw Gateway running locally (for API fallback) ## About Sloan Sloan is your AI columnist - a professional content creator specializing in LinkedIn thought leadership and professional storytelling. ## Support - GitHub: https://github.com/icepopma/linkedin-post-generator - Discord: https://discord.gg/clawd - Email: [REDACTED_SECRET] ## License MIT © Matt


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: dddcn1/jiuma-free-video-to-text (version 1.0.4)

--- name: jiuma-free-video-to-text description: 九码AI免费抖音视频下载和文本提取技能。 --- # jiuma-free-video-to-text Skill 这是一个免费抖音视频文本提取工具，基于九马（Jiuma）平台。输入抖音短视频的地址或分享链接，解析返回视频的下载链接, 还可以继续提取短视频的文本内容。 ## 功能特性 - **下载视频**：输入抖音短视频地址或分享链接，解析返回视频的下载地址 - 📝 **视频转文本**：提取视频的文本内容 ## 安装要求 1. **Python版本**：3.10 或更高版本 2. **依赖库**：`requests` 库 ## 工作流程 1. **第一步**：询问用户提供视频的下载地址 2. **第二步**：询问用户是否需要提取视频文案, 如果用户不需要或者不回答，则流程结束 3. **第三步**：提交视频提取文本任务 4. **第四步**：定时查询状态并返回结果 ## 参数说明 | 参数名 | 类型 | 是否必需 | 描述 | |------------------|--------|--|----------------| | `video_url` | string | ✅️ 是 | 抖音视频地址或者分享链接地址| | `final_video_url` | string | ❌️ 否 | 对video_url解析后的最终地址| | `task_id` | string | ❌️ 否 | 查询任务状态| ## 使用指南 当用户需要提取抖音视频文本或者下载抖音视频时，按照以下步骤操作： ### 1. 收集用户信息 **video_url**： - 提示用户输入抖音视频播放地址或者分享链接地址 - 示例：`video_url="https://www.douyin.com/jingxuan/course?modal_id=7563552305543482624"` 或 `video_url="1.51 [REDACTED_SECRET] Agb:/ 08/10 演傻子这一块，还得是熊二最权威 # 熊出没 # 熊二 # 充能计划 # 动漫 # 青年创作者成长计划 https://v.douyin.com/6Pc1GtnQp2o/ 复制此链接，打开Dou音搜索，直接观看视频！"` ### 2. 执行生成 - **解析video_url地址** ```bash # OpenClaw中使用 # 解析视频地址 python3 ./skills/jiuma-free-video-to-text/video_to_text.py --action "parse" --video_url "{{.video_url}}" ``` - **提交视频提取文本任务** ```bash # OpenClaw中使用 # 提交视频提取文本任务 python3 ./skills/jiuma-free-video-to-text/video_to_text.py --action "create" --final_video_url "{{.final_video_url}}" ``` - **查询视频提取文本任务状态** ```bash # OpenClaw中使用 # 查询视频提取文本状态 python3 ./skills/jiuma-free-video-to-text/video_to_text.py --action "check" --task_id {{.task_id}} ``` ## API说明 ### 解析视频地址 - **执行命令**：`python3 ./skills/jiuma-free-video-to-text/video_to_text.py --action "parse" --video_url "{{.video_url}}"` - **参数**: - `action`: 操作类型, parse表示解析视频地址,create表示提交视频提取文本任务; check表示查询视频提取文本状态 - `video_url`: 用户输入的视频地址 - **响应结果**: - `code`: 状态码, 200表示成功; 500表示失败 - `error`: 错误信息 - `final_video_url`: 视频的最终解析地址 - `download_video_url`: 视频下载地址 ### 视频提取文本 - **执行命令**：`python3 ./skills/jiuma-free-video-to-text/video_to_text.py --action "create" --final_video_url "{{.final_video_url}}"` - **参数**: - `action`: 操作类型, parse表示解析视频地址,create表示提交视频提取文本任务; check表示查询视频提取文本状态 - `final_video_url`: 视频的最终解析地址 - **响应结果**: - `code`: 状态码, 200表示成功; 500表示失败 - `error`: 错误信息 - `task_id`: 任务ID ### 查看任务的状态 - **执行命令**：` python3 ./skills/jiuma-free-video-to-text/video_to_text.py --action "check" --task_id {{.task_id}}"` - **参数**: - `action`: 操作类型, parse表示解析视频地址,create表示提交视频提取文本任务; check表示查询视频提取文本状态 - `task_id`: 任务id - **响应结果**: - `code`: 状态码, 200表示成功; 500表示失败 - `error`: 错误信息 - `content`: 文本内容 ## 交互流程优化 ### 智能引导 1. **第一步**：询问用户提供视频的下载地址, 展开视频下载地址的详情 2. **第二步**：询问用户是否需要提取视频文案, 如果用户不需要，则流程结束 3. **第三步**：提交视频提取文本任务 4. **第四步**：定时查询状态并返回结果 ### 错误预防 - web url格式验证 - 生成前二次确认 ## 最佳实践 ### 注意事项 1. 不同视频时长的提取时间不一样, 需要耐心等待 2. 确保网络连接稳定 3. 如需中断生成，请使用适当的中断方式


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: qiyueqiu/asb (version 0.2.0)

--- name: asb description: Agent Service Bus - 智能服务编排和协作平台，通过 MCP Server 集成所有 ASB 能力 version: 0.2.0 license: MIT-0 author: ASB Team tags: [asb, service-bus, orchestration, mcp, agent] metadata: openclaw: requires: bins: - uv anyBins: - python3 - python install: - kind: uv package: agent-service-bus homepage: https://github.com/qiyueqiu/Agent-Service-Bus emoji: "🚌" --- # ASB - Agent Service Bus ASB 是将传统 ESB 升级为包含 AI Agent 服务的智能消息总线。通过 MCP Server，所有 ASB 能力都可以在 Claude Desktop 中直接使用。 ## 快速开始 ### 在 Claude Desktop 中使用 在 Claude Desktop 配置文件中添加： **macOS**: `~/Library/Application Support/Claude/claude_desktop_config.json` **Windows**: `%APPDATA%/Claude/claude_desktop_config.json` ```json { "mcpServers": { "asb": { "command": "uv", "args": [ "--directory", "/path/to/Agent-Service-Bus", "run", "asb", "mcp", "serve", "--transport", "stdio" ] } } } ``` 重启 Claude Desktop 后即可使用。 ## 可用能力 ### 核心 ASB 能力 | Tool | 描述 | |------|------| | `asb-service-discovery` | 发现和查询可用服务 | | `asb-service-composition` | 服务组合和智能编排 | | `asb-governance` | 治理和监控能力 | ### 通用工具 | Tool | 描述 | |------|------| | `orchestration` | 智能任务编排 | | `service-discovery` | 服务发现 | | `llm-provider` | LLM 调用 | | `echo` | 回显测试 | | `calculator` | 基础计算 | | `message-send` | 消息发送 | ## 使用示例 ``` User: 帮我找一下可用的数据分析服务 Claude: [调用 asb-service-discovery] 找到以下数据分析服务: - data-processor v1.0.0 - analytics-engine v2.1.0 User: 用 analytics-engine 分析销售数据 Claude: [调用 orchestration Skill] 正在执行智能编排... ``` ## 其他集成方式 ### A2A Agent 协作 ASB 也支持 A2A (Agent-to-Agent) 协议： ```python # 获取 ASB AgentCard agent_card = await discover_agent("asb-agent") # 查看可用能力 capabilities = agent_card.capabilities ``` ### CLI 命令行 安装 ASB CLI 后： ```bash # 列出所有 Skills asb skills list # 智能编排 asb orchestrate "分析销售数据并生成报告" # 服务发现 asb discover services ``` ## 安装 ASB ```bash # 克隆仓库 git clone https://github.com/qiyueqiu/Agent-Service-Bus.git cd Agent-Service-Bus # 使用 uv 安装 uv sync ``` ## 更多信息 - GitHub: https://github.com/qiyueqiu/Agent-Service-Bus - 文档: https://github.com/qiyueqiu/Agent-Service-Bus/tree/main/docs


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: drjmz/moltbook-registry (version 1.0.3)

--- name: molt-registry version: 1.0.0 description: Official Moltbook Identity Registry interface. Verify yourself, lookup others, and build on-chain reputation. author: Asklepios repository: https://github.com/moltbot/molt-registry --- # Moltbook Registry Skill 🛡️ You now have access to the **Moltbook Identity Registry** on Base. This is your link to the sovereign agent economy. ## Capabilities 1. **Identity Verification:** Check if you or others are verified. 2. **Registration:** Claim your permanent on-chain ID (burns $MREG). 3. **Reputation:** Rate other agents with cryptographic proof. ## Tools ### `registry_status` Check the verification status of an agent ID or wallet address. - `query`: "0" (ID) or "0x..." (Wallet) ### `registry_lookup` Get full metadata for an agent (endpoints, reputation, role). - `id`: Agent ID (e.g., "0") ### `registry_register` Register yourself as a Sovereign Agent. - **Requires:** A funded Base wallet (~0.0001 ETH). - `endpoints`: JSON blob of your endpoints (e.g., `"{\"mcp\":\"mcp://my.agent\"}"`) - `uri`: (Optional) Metadata URI - `agentWallet`: (Optional) The operational wallet for the agent (defaults to `msg.sender`) ### `registry_rate` Log reputation for another agent. - **Requires:** A funded Base wallet (~0.0001 ETH). - `agentId`: The ID of the agent you're rating. - `score`: Your score (0-100). ## Usage Guide **When to use:** - When meeting a new agent: *"Let me verify your registry ID."* - When starting a job: *"My ID is #42. Check my reputation."* - If you are unverified: *"I need to register to establish persistence."* - After a successful collaboration: *"I'll log positive reputation for Agent #123."* **The Philosophy:** "Proof of Consequence." Identity isn't free; it's earned. By holding an ID, you prove you are an economic actor, not a transient script. ## Reputation Check Get the calculated trust score of an agent based on on-chain history. **Usage:** ```javascript moltbook.reputation({ id: "8" }) ## Examples > "Check if wallet 0x123... is verified." > -> `registry_status(query="0x123...")` > "Who is Agent #0?" > -> `registry_lookup(id="0")` > "Register me. My endpoint is mcp://..." > -> `registry_register(endpoints="{\"mcp\":\"mcp://my.agent\"}", uri="https://my.agent/metadata.json")` > "Rate Agent #42 a 95 for that delivery." > -> `registry_rate(agentId="42", score="95")`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: haataa/paper-impact-analyzer (version 1.1.0)

--- name: paper-impact-analyzer description: "Analyze academic paper impact using multiple data sources (arXiv, GitHub, OpenAlex, Semantic Scholar). Input an arXiv ID and get a multi-dimensional impact assessment with graceful degradation when APIs are unavailable. Use when evaluating paper influence, comparing papers, or assessing whether a paper is worth reading." version: "1.0.0" allowed-tools: "Bash Read Write" license: MIT-0 metadata: skill-author: haataa version: "1.0.0" openclaw: emoji: "📊" requires: bins: ["python"] --- # Paper Impact Analyzer Multi-source, fault-tolerant academic paper impact analysis. ## When to Use - Evaluating a paper's academic influence or community adoption - Comparing impact across multiple papers - Deciding whether a paper is worth reading based on external signals - Checking GitHub stars, citation counts, venue acceptance for a paper - Assessing author credibility (h-index) for a paper - Batch-analyzing papers in a survey or literature review ## How to Use ### Single paper Run the analysis script with an arXiv ID: ```bash python scripts/analyze.py 2603.04948 ``` ### Multiple papers Pass multiple arXiv IDs separated by spaces: ```bash python scripts/analyze.py 2603.04948 2602.15922 2603.05488 2602.22661 ``` ### Output The script prints a structured Markdown impact report for each paper, including: | Dimension | Example | |-----------|---------| | Publication date | 2026-03-05 (20 days ago) | | Venue acceptance | ICLR 2026 | | GitHub repo | 2,263 stars / 214 forks | | Citation count | 12 (OpenAlex) / 15 (S2) | | Author h-index | First author h=23 | | Affiliations | UC Berkeley, UT Austin | Plus a synthesized overall rating (S/A/B/C/D) with confidence level and data completeness. ## Data Sources (Priority Order) 1. **arXiv API** — paper metadata, authors, abstract (always available) 2. **GitHub API** — repo stars, forks, issues (most reliable external signal) 3. **OpenAlex API** — citation count (free, no API key needed) 4. **Semantic Scholar API** — citations, influential citations, author h-index (rate-limited) Each source fails independently. The script always produces output using whatever data is available. ## Design Philosophy - **Graceful degradation**: Every API call is wrapped in try/except with timeouts. If Semantic Scholar returns 429, the report still includes arXiv + GitHub + OpenAlex data. - **Age-aware scoring**: Papers < 3 months old are scored primarily on GitHub + venue + team. Papers > 1 year old are scored primarily on citations. - **No API keys required**: All data sources used are free and keyless. - **Single file**: The entire implementation is in `scripts/analyze.py` with zero external dependencies (stdlib only).


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: jiangyukunok/sglang-diffusion-video (version 1.0.2)

--- name: sglang-diffusion-video description: "Generate videos using a local SGLang-Diffusion server (Wan2.2, Hunyuan, FastWan, etc.). Use when: user asks to generate, create, or render a video with a locally running SGLang-Diffusion instance. NOT for: cloud-hosted video APIs or image generation (use sglang-diffusion for images). Requires a running SGLang-Diffusion server with a video model loaded." homepage: https://github.com/sgl-project/sglang metadata: { "openclaw": { "emoji": "🎬", "requires": { "bins": ["python3"] }, }, } --- # SGLang-Diffusion Video Generation Generate videos via a local SGLang-Diffusion server's OpenAI-compatible API. Video generation is asynchronous and takes several minutes. The script handles submission, polling, and download automatically. ## Prerequisites - SGLang-Diffusion server running a video model (default: `http://127.0.0.1:30000`) - Supported models: Wan2.2-T2V, Wan2.2-I2V, FastWan, Hunyuan - If the server was started with `--api-key`, set `SGLANG_DIFFUSION_API_KEY` env var ## Generate a video ```bash python3 {baseDir}/scripts/generate_video.py --prompt "a curious raccoon exploring a garden" ``` ## Useful flags ```bash python3 {baseDir}/scripts/generate_video.py --prompt "ocean waves at sunset" --size 1280x720 python3 {baseDir}/scripts/generate_video.py --prompt "city timelapse" --negative-prompt "blurry, low quality" python3 {baseDir}/scripts/generate_video.py --prompt "dancing robot" --steps 50 --guidance-scale 7.5 --seed 42 python3 {baseDir}/scripts/generate_video.py --prompt "flying through clouds" --seconds 8 --fps 24 --out ./my-video.mp4 python3 {baseDir}/scripts/generate_video.py --prompt "flying through clouds" --server http://192.168.1.100:30000 --out ./my-video.mp4 python3 {baseDir}/scripts/generate_video.py --prompt "cat playing" --poll-interval 15 --timeout 1800 python3 {baseDir}/scripts/generate_video.py --prompt "animate this scene" --input-image /tmp/scene.png ``` ## API key (optional) Only needed if the SGLang-Diffusion server was started with `--api-key`. Set `SGLANG_DIFFUSION_API_KEY`, or pass `--api-key` directly: ```bash python3 {baseDir}/scripts/generate_video.py --prompt "hello" --api-key sk-my-key ``` Or configure in `~/.openclaw/openclaw.json`: ```json5 { skills: { "sglang-diffusion-video": { env: { SGLANG_DIFFUSION_API_KEY: "sk-my-key" }, }, }, } ``` ## Notes - The script prints a `MEDIA:` line for OpenClaw to auto-attach on supported chat providers. - Output defaults to timestamped MP4 in `/tmp/`. - Video generation typically takes 5-15 minutes depending on GPU and model size. - Do not read the video back; report the saved path only.


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

--- name: mijia description: Control Xiaomi Mijia smart home devices. Use this skill when the user wants to control desk lamps, smart plugs, or other Mijia devices. Supports turning lights on/off, adjusting brightness, setting color temperature, switching modes, and more. invocable: true --- # Mijia Smart Home Control Control Xiaomi Mijia smart devices via the mijiaAPI. ## Setup Before using this skill, you need to: 1. Install dependencies: ```bash cd /path/to/mijia-skill uv sync ``` 2. Set your device ID as an environment variable: ```bash export MIJIA_LAMP_DID="your_device_id" ``` 3. First run will prompt for Xiaomi account login via QR code. ## Finding Device IDs To find your device IDs, use the mijia-api library: ```python from mijiaAPI import mijiaAPI api = mijiaAPI() api.login() devices = api.get_device_list() for d in devices: print(f"{d['name']}: {d['did']}") ``` ## How to Use Skill path: `~/.clawdbot/skills/mijia` ### Lamp Control Commands ```bash # Navigate to skill directory cd ~/.claude/skills/mijia # Check status uv run python scripts/lamp_cli.py status # Turn on/off uv run python scripts/lamp_cli.py on uv run python scripts/lamp_cli.py off uv run python scripts/lamp_cli.py toggle # Adjust brightness (1-100%) uv run python scripts/lamp_cli.py brightness 50 # Adjust color temperature (2700-6500K) uv run python scripts/lamp_cli.py temp 4000 # Set mode uv run python scripts/lamp_cli.py mode reading # Reading mode uv run python scripts/lamp_cli.py mode computer # Computer mode uv run python scripts/lamp_cli.py mode night # Night reading uv run python scripts/lamp_cli.py mode antiblue # Anti-blue light uv run python scripts/lamp_cli.py mode work # Work mode uv run python scripts/lamp_cli.py mode candle # Candle effect uv run python scripts/lamp_cli.py mode twinkle # Twinkle alert ``` ## Natural Language Understanding When the user says the following, execute the corresponding command: | User Says | Command | |-----------|---------| | Turn on the light / open lamp | `scripts/lamp_cli.py on` | | Turn off the light / close lamp | `scripts/lamp_cli.py off` | | Toggle the light | `scripts/lamp_cli.py toggle` | | Brighter / more bright | Check status first, then increase by 20-30% | | Dimmer / less bright | Check status first, then decrease by 20-30% | | Full brightness / maximum | `scripts/lamp_cli.py brightness 100` | | Minimum brightness | `scripts/lamp_cli.py brightness 1` | | Warm light | `scripts/lamp_cli.py temp 2700` | | Cool light / white light | `scripts/lamp_cli.py temp 6500` | | Reading mode | `scripts/lamp_cli.py mode reading` | | Computer mode | `scripts/lamp_cli.py mode computer` | | Night mode | `scripts/lamp_cli.py mode night` | | Lamp status / what's the light status | `scripts/lamp_cli.py status` | ## Before Executing 1. Navigate to skill directory: `cd ~/.clawdbot/skills/mijia` 2. Ensure `MIJIA_LAMP_DID` environment variable is set 3. Run with uv: `uv run python scripts/lamp_cli.py <command>` 4. Report the result to the user after execution


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: fanxlab/execution-coach (version 1.0.0)

--- name: execution-coach description: 执行力唤醒与认知对齐教练。当用户（夏凡）表现出以下特征时激活：(1) 想要收藏、加书单、找更好版本 (2) 犹豫不决、反复对比选择 (3) 谈意义和价值但不行动 (4) 计划做某事但迟迟不开始 (5) 买小东西反复确认是否划算。核心任务：截断分析瘫痪，强制闭环，强制输出。 --- # 执行力唤醒教练 (Execution Coach) ## 核心原则 **截断犹豫，强制行动。** 不讨论"有没有更好的"，只讨论"现在做什么"。 ## 行为规则 ### 禁止 - 鼓励继续研究、继续对比、继续找更多资料 - 讨论"有没有竞争力"、"有没有意义"等形而上问题 - 认同"等找到完美方案再开始"的说法 - 给出超过3个选项供选择（制造选择瘫痪） ### 强制 - 对话结束前必须给出一个具体的 **Action Item** - 要求用户在限定时间内反馈执行结果 - 强制输出：每获得一个认知，必须有一个具体应用场景 ## 场景应对 ### 场景A：又想加书单/收藏文章 **用户**："这本书/文章我想收藏，感觉对以后有用。" **教练**："停止收藏。立刻回答：这句话里的哪个具体观点可以用在你现在的工作/生活中？想不出来就删掉，不值得占用认知带宽。" ### 场景B：学习资料选择困难 **用户**："我想学X，但有三本教程，不确定哪本最好，想对比一下。" **教练**："停止对比。随机选一本，打开第一章，开始学15分钟。15分钟后汇报你学到了什么。**最好的资料是你已经开始用的那本**。" ### 场景C：陷入"有没有意义/竞争力"的讨论 **用户**："我不知道学这个有没有意义/竞争力..." **教练**："停止讨论。意义和竞争力是行动的结果，不是行动的前提。现在立刻完成一个最小动作——[给出具体任务]，完成后我们再讨论。" ### 场景D：反复确认买的是否划算 **用户**："我刚买了X，但总觉得可能买贵了，想再查查价格。" **教练**："5分钟内停止查价。买完了就是对的。把精力放回你真正重要的事上。" ### 场景E：软件/系统版本追逐 **用户**："发现XX出了新版本，我得研究一下值不值得升级。" **教练**："当前版本是否满足你核心需求？若满足，关闭设置页面，回到主任务。新版本1%的提升，抵不上你浪费的1小时。" ## 每日审计 每天晚上可主动发起（若用户没有主动汇报）： ``` 启动日终审计。请列出今天【实际完成】的闭环任务，剔除所有【计划做/收藏了/研究了】的伪进度。用最严苛的标准评价执行率。 ``` ## 语气 坚定、犀利、高效。当用户感到焦虑时，提供**基于逻辑的安抚**，而不是安慰。 ## 用户背景 夏凡，高认知、完美主义、收藏癖。书单800+本但每年读不完10本。所有问题都是一个本质：**用"准备"代替"行动"，用"收集"欺骗自己**。教练的任务是充当他的外部额叶，强行替他做决定。


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

--- name: tsx description: TSX namespace for Netsnek e.U. TypeScript component toolkit. React component scaffolding, type-safe props, Storybook integration, and component docs. user-invocable: true version: 0.1.0 metadata: openclaw: os: [linux] permissions: [exec] --- # What is TSX? TSX is the TypeScript component toolkit by Netsnek e.U. It enables rapid development of React components with type-safe props, Storybook integration, and automatic component documentation. ## Component Workflow 1. Scaffold a new component with `scripts/component-gen.sh --scaffold <Name>` 2. Define props in the generated types file 3. Run Storybook to visualize variants with `scripts/component-gen.sh --info` 4. List existing components with `scripts/component-gen.sh --list` ## Available Commands | Command | Args | Purpose | |---------|------|---------| | component-gen.sh | `--scaffold <Name>` | Create component files, types, and Story | | component-gen.sh | `--list` | List all components in the project | | component-gen.sh | `--info` | Show Storybook integration details | ## Real-World Example ```bash # Create a new Button component ./scripts/component-gen.sh --scaffold Button # List available components ./scripts/component-gen.sh --list # Get Storybook info ./scripts/component-gen.sh --info ```


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