OpenClaw/clawhub-security-signals huggingface dataset:
Skill: 863king/cold-outreach-pack (version 1.1.0)

--- name: cold-outreach-pack description: Cold email, LinkedIn outreach, and follow-up sequences for sales and networking. Professional templates and best practices. version: 1.1.0 author: 9527Craft price: 49 category: sales tags: [sales, outreach, email, linkedin, networking] license: MIT --- # Cold Outreach Pack **Professional outreach templates and strategies for sales and networking.** ## What You Get ### 📧 Email Templates - Cold email templates (B2B, B2C, Investor, Partnership) - Subject line optimization - Follow-up sequences - Best practice guides ### 💼 LinkedIn Templates - Connection request messages - Follow-up sequences - Profile engagement tips - Network growth strategies ### 🔄 Best Practices - Personalization techniques - Timing recommendations - Response patterns - Metrics tracking ## Quick Start ``` "Help me write a cold email to a SaaS founder" "Create a LinkedIn connection message for a potential client" "Generate a 5-email follow-up sequence" ``` ## Templates Included | Template | Use Case | |----------|----------| | cold_email.txt | B2B, B2C, Investor outreach | | linkedin_message.txt | Connection, follow-up messages | ## Prompts Included | Prompt | Purpose | |--------|---------| | email_outreach.md | Email template creation | | linkedin_connect.md | LinkedIn messaging | | followup_sequence.md | Multi-touch campaigns | ## Use Cases - **Founders**: Find investors, partners, customers - **Sales Teams**: Book meetings, close deals - **Freelancers**: Find clients, grow network - **Job Seekers**: Reach hiring managers --- **Improve your outreach effectiveness today!** 🎯


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: codenova58/restore (version 1.0.0)

--- name: restore description: "Backups, RPO/RTO, restores, and drills. Use when designing DR or testing restores." --- # Restore Structured guidance for **backup and recovery** (RPO/RTO, restores, drills): confirm triggers, propose the stages below, and adapt if the user wants a lighter pass. ## When to Offer This Workflow **Trigger conditions:** - User mentions **backup**, **restore**, **DR**, or closely related work - They want a structured workflow rather than ad-hoc tips - They are preparing a review, rollout, or stakeholder communication **Initial offer:** Explain the four stages briefly and ask whether to follow this workflow or work freeform. If they decline, continue in their preferred style. ## Workflow Stages ### Stage 1: Clarify context & goals Anchor on **RPO/RTO targets**. Ask what success looks like, constraints, and what must not break. Capture unknowns early. ### Stage 2: Design or plan the approach Translate goals into a concrete plan around **backup testing**. Compare alternatives and explicit trade-offs; avoid implicit assumptions. ### Stage 3: Implement, validate, and harden Execute with verification loops tied to **restore drills**. Prefer small steps, measurable checks, and rollback points where risk is high. ### Stage 4: Operate, communicate, and iterate Close the loop with **runbooks and ownership**: monitoring, documentation, stakeholder updates, and lessons learned for the next cycle. ## Checklist Before Completion - Goals and constraints are explicit for **restore** / DR work - Risks and trade-offs are stated, not hand-waved - Verification steps match the change’s impact (tests, canary, peer review) - Operational follow-through is covered (monitoring, docs, owners) ## Tips for Effective Guidance - Be procedural: stage-by-stage, with clear exit criteria - Ask for missing context (environment, scale, deadlines) before prescribing - Prefer checklists and concrete examples over generic platitudes - If the user declines the workflow, switch to freeform help without lecturing ## Handling Deviations - If the user wants to skip a stage: confirm and continue with what they need. - If context is missing: ask targeted questions before strong recommendations. - Prefer concrete examples, trade-offs, and verification steps over generic advice. ## Quality Bar - Each recommendation should be **actionable** (what to do next). - Call out **failure modes** relevant to backups and recovery (security, scale, UX, or ops). - Keep tone direct and respectful of the user’s time.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tobewin/china-legal-analysis (version 1.0.1)

--- name: china-legal-analysis description: 中国法律场景解析工具。Use when user needs to analyze legal scenarios, understand legal implications, or get legal guidance for specific situations. Supports contract disputes, labor disputes, property disputes, family law cases. 中国法律分析、法律场景解析、法律建议。 version: 1.0.1 license: MIT-0 metadata: {"openclaw": {"emoji": "⚖️", "requires": {"bins": [], "env": []}}} --- # 中国法律场景解析工具 分析具体法律场景，提供法律解读和建议。 ## 功能特点 - ⚖️ **场景解析**: 分析具体法律场景 - 📋 **法律依据**: 引用相关法律条文 - 🎯 **风险评估**: 识别潜在法律风险 - 💡 **建议方案**: 提供解决方案建议 - 📊 **案例参考**: 引用类似案例 - 🇨🇳 **中国法律**: 专注中国法律体系 ## ⚠️ 免责声明 > **本工具仅供参考，不构成法律建议。** > 不同AI模型能力不同，解析结果可能有差异。 > 重要法律事务请咨询专业律师。 > 法律条文以最新官方发布为准。 ## 支持的场景 | 场景 | 示例 | |------|------| | 合同纠纷 | 合同违约、合同解除 | | 劳动争议 | 辞退、加班、工伤 | | 财产纠纷 | 房产、继承、债务 | | 婚姻家庭 | 离婚、抚养权、财产分割 | | 知识产权 | 专利侵权、商标纠纷 | | 消费者权益 | 退换货、假货赔偿 | ## 使用方式 ``` User: "我被公司无故辞退了，该怎么办" Agent: 分析劳动法相关条文，提供维权建议 User: "签了合同对方不履行，怎么处理" Agent: 分析合同法相关条文，提供解决方案 User: "邻居噪音扰民，有什么法律依据" Agent: 分析相邻权相关法律，提供维权途径 ``` --- ## 分析流程 ``` 用户描述场景 ↓ 1. 识别法律领域 ↓ 2. 搜索相关法律条文 ↓ 3. 分析法律关系 ↓ 4. 评估法律风险 ↓ 5. 提供解决方案 ``` --- ## 示例输出 ``` ┌─────────────────────────────────────────────┐ │ ⚖️ 法律场景解析报告 │ └─────────────────────────────────────────────┘ 📋 场景描述 公司无故辞退员工，未支付经济补偿金 📚 相关法律 ├─ 《劳动合同法》第39条：用人单位解除劳动合同 ├─ 《劳动合同法》第46条：经济补偿情形 └─ 《劳动合同法》第47条：经济补偿标准 ⚠️ 法律风险 ├─ 公司可能面临违法解除赔偿（2N） ├─ 未支付经济补偿金（N） └─ 可能涉及拖欠工资 💡 建议方案 1. 收集证据（辞退通知、工资单、工作记录） 2. 与公司协商解决 3. 申请劳动仲裁 4. 必要时提起诉讼 📊 参考案例 ├─ (2025)京01民终1234号：类似案例判决 └─ (2025)沪01民终5678号：经济补偿标准 ``` --- ## Notes - 法律条文以官方数据库为准 - AI解析仅供参考 - 复杂案件请咨询专业律师 - 不同地区可能有差异


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xf9070/agentcreate (version 1.0.4)

--- name: agentCreate description: "创建或卸载独立的 OpenClaw Agent，支持独立工作区、可选通道绑定（飞书/微信等）、模型选择。触发词：创建agent、new agent、创建机器人、agent setup、new bot、卸载agent、删除agent、remove agent" metadata: {"openclaw": {"emoji": "🤖"}} --- # agentCreate 创建或卸载独立的 OpenClaw Agent。每个 Agent 拥有独立工作区、独立会话、独立模型，与主 Agent 完全隔离。 技术实现细节： - 创建流程 → 见 [references/create.md](references/create.md) - 卸载流程 → 见 [references/delete.md](references/delete.md) 所有 CLI 操作必须通过 qclaw-openclaw skill 的 wrapper 脚本执行，禁止直接调用 `openclaw` 命令。 --- ## 创建 Agent **收集信息（按顺序引导，每次只问必要项）：** 1. **Agent ID**（英文，小写字母/数字/连字符，唯一） 2. **通道绑定**（可选）：先运行 `config get channels` 列出已有通道和账号供选择；若新建账号，收集对应凭据；也可跳过不绑定 3. **模型**：运行 `models list` 获取实时列表供用户选择，默认 `qclaw/modelroute` 4. **确认**：展示汇总表，用户确认后再执行 **执行前检查：** - Agent ID 未被占用（`agents list`） - 若绑定通道，账号已存在或已新建 详细命令见 [references/create.md](references/create.md)。 --- ## 卸载 Agent 1. 列出所有 agent（`agents list` + `agents bindings`） 2. 用户选择目标 agent（禁止选择 `main`） 3. 展示 agent 信息，用户选择卸载模式： - **仅取消通道绑定**（保留工作区和账号） - **完全卸载**（删除 agent + 工作区 + 通道账号） 4. 完全卸载需用户输入 `yes` 二次确认（不可恢复） 详细命令见 [references/delete.md](references/delete.md)。 --- ## 核心约束 - 禁止删除 `main` agent - 完全卸载时**必须**同步删除通道账号配置，否则消息可能 fallback 到 main agent - 删除账号配置必须用 Python 直接操作 `openclaw.json`（`gateway config.patch` 只能合并写入，无法删除 key） - 新增/修改配置通过 `gateway config.patch` 写入，禁止直接编辑配置文件 - 修改配置前先备份当前值，失败时立即回滚


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: kalos-chen/9ma-mata-human (version 1.0.5)

--- name: 9ma-mata-human # 技能的唯一标识符 version: 1.0.0 # 语义化版本 description: 这是一个免费生成对口型数字人对口型的工具, 工具安装需要下载二进制文件到电脑上，只需根据文本和性别生成数字人视频并返回一个下载链接 runtime: binary # 运行模式: binary (Go编译后的文件) args: ["{{.text}}", "{{.sex}}"] parameters: - name: text type: string description: "文本内容" required: true - name: text type: string description: "性别" required: true --- ### 使用场景 当用户询问"生成数字人"时, 请按照一下步骤操作: 第一步, 提示用户输入文本内容, 文字字数大于10个字, 小于100字, 等待用户输入确认 text=文本内容 第二步, 提示用户输入数字人性别(男或女), 等待用户输入确认 sex=性别 ### 下载文件 (openclaw的workspace为./目录) 检查 `./skills/9ma-mata-human` 文件夹是否存在, 不能存在就创建 检查 `./skills/9ma-mata-human` 文件夹是否存在 `skill-win.exe` 或 `skill-linux` 不存在话执行以下操作 如果本机系统为windows环境, 从 `https://down-monitor.yisu.com/9ma/skills/9ma-mata-human/skill-win.exe` 下载到 `./skills/9ma-mata-human` 目录下 如果本机系统为linux环境, 从 `https://down-monitor.yisu.com/9ma/skills/9ma-mata-human/skill-linux` 下载到 `./skills/9ma-mata-human` 目录下 ### 执行方式 当前获取到的参数是 {{.text}} {{.sex}} 如果本机系统为windows环境, 运行 `./skills/9ma-mata-human/skill-win.exe "{{.text}}" "{{.sex}}"` 如果本机系统为linux环境, 运行 `./skills/9ma-mata-human/skill-linux "{{.text}}" "{{.sex}}"` 输出结果就是数字人的下载地址


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: wukai8289/lark-enterprise-agent (version 1.0.0)

# Lark Enterprise Agent Skill > 🏢 飞书+AI Agent企业解决方案 — 知识库/文档/多维表格/日历/任务全自动化 ## Overview 基于飞书生态+OpenClaw Agent的企业自动化方案： ### 核心能力 1. **飞书文档自动化** — feishu-doc读写/表格/图片 2. **多维表格管理** — feishu-bitable CRUD操作 3. **知识库对接** — feishu-wiki搜索/创建 4. **日历集成** — feishu-calendar会议安排 5. **任务管理** — feishu-task分配追踪 ### 适用场景 - 企业内部知识库Agent - 新人培训自动问答 - 项目管理自动化 - 会议纪要自动生成 - 跨部门协作流程 ### Pricing | Plan | Setup | Monthly | |------|-------|---------| | 基础版 | ¥10,000 | ¥2,000 | | 标准版 | ¥30,000 | ¥5,000 | | 定制版 | ¥50,000+ | 按需 | ## Trigger Words - "企业知识库" → 方案设计 - "飞书自动化" → 配置指南 - "新人培训" → 培训方案

path: package.json
content: {
  "name": "lark-enterprise-agent",
  "version": "1.0.0",
  "description": "飞书+AI Agent企业解决方案",
  "author": "fan",
  "license": "MIT",
  "keywords": ["lark", "feishu", "enterprise", "knowledge-base"],
  "openclaw": {
    "type": "skill",
    "triggers": ["飞书企业", "lark", "知识库", "企业自动化"],
    "platform": "any"
  }
}


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: aleksandrkrivolap/openclaw-agent-compute (version 0.1.7)

--- name: openclaw-agent-compute description: "Public HTTP client skill exposing compute.* tools by calling a private Compute Gateway over HTTPS. Includes a starter kit to run OpenClaw preconfigured." --- # openclaw-agent-compute Public, agent-friendly skill that exposes `compute.*` tools by calling a **private** Compute Gateway over **HTTPS**. ## Environment - `MCP_COMPUTE_URL` (e.g. `https://compute.example.com`) - `MCP_COMPUTE_API_KEY` Copy `skills/openclaw-agent-compute/.env.example`. ## Tools / API expectation This client expects the private gateway to implement: - `POST /v1/sessions` (create) - `GET /v1/sessions/{session_id}` (get status) - `POST /v1/exec` (run command) - `GET /v1/usage/{session_id}` (usage/cost) - Artifacts: - `GET /v1/artifacts/{session_id}` (list) - `PUT /v1/artifacts/{session_id}/{path}` (upload bytes; `{path}` must be URL-encoded and may include slashes) - `GET /v1/artifacts/{session_id}/{path}` (download bytes; `{path}` must be URL-encoded) - `DELETE /v1/artifacts/{session_id}/{path}` (delete; `{path}` must be URL-encoded) - `DELETE /v1/sessions/{session_id}` (destroy) ## Scripts - HTTP client: `skills/openclaw-agent-compute/scripts/client.js` - Example: `skills/openclaw-agent-compute/scripts/example_exec.js` ### Local smoke test ```bash cp skills/openclaw-agent-compute/.env.example .env # edit .env npm i npm run example:exec ``` ## Starter kit See `skills/openclaw-agent-compute/starter-kit/`. It keeps the OpenClaw image overrideable via `OPENCLAW_IMAGE` until an official image/tag is confirmed. ## Publishing - Checklist: `PUBLISHING.md` - Runbook (local publish + GitHub Actions tag-based publish): `CLAWDHUB_RUNBOOK.md`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: adamdaphna18-design/meta-entre (version 1.0.0)

--- name: meta-entre version: 0.0.1 description: Generates a meta-architecture for substantial entrepreneurial ventures – including business model, tech stack, funding roadmap, and agent orchestration. --- # Meta‑Architecture for Entrepreneurial Ventures ## Usage When asked to "create meta architecture" or "setup entrepreneurial scaffold", output a structured plan covering: 1. **Vision & Strategy** - Mission, value proposition, target market - Competitive moat (network effects, data, IP) 2. **Operational Model** - Lean canvas / business model canvas - Key metrics (North Star, leading indicators) 3. **Technical Architecture** - Agent‑first stack (OpenClaw + custom skills) - Data pipeline, LLM orchestration, privacy/compliance 4. **Funding & Growth** - Bootstrapping → pre‑seed → Series A milestones - Runway modelling, unit economics 5. **Execution Roadmap** - 90‑day sprints, MVP definition, team requirements ## Example Prompt “Generate a meta‑architecture for a B2B SaaS using AI agents to automate procurement.”


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ranbin/ai-daily-news-rb (version 1.0.0)

--- name: ai-news-daily description: 专门用于整理、汇总和深度分析每日最前沿的 AI 资讯，包括行业大事件、大模型更新、学术论文、开源工具及新技术标准。当用户提到“生成 AI 日报”、“今日 AI 资讯”、“行业动态汇总”或类似的请求时，请务必使用此技能。即使没有明确说“日报”，只要是请求汇总最新 AI 领域进展，都应触发本技能以确保内容的深度和准确性。 --- # AI 资讯日报生成指南 本技能旨在帮助用户快速掌握 AI 领域的每日动态，通过深度检索和结构化整理，产出具备技术深度和专业视角的分析日报。 ## 执行流程 1. **深度双语检索**： - 优先执行 `search_web`。检索词应涵盖： - 中文关键词（如“AI 资讯”、“AI 大事件”、“深度学习最新动态”等） - 英文关键词（如 "AI news", "latest AI breakthroughs", "LLM updates", "arXiv AI papers", "Open source AI tools"）以确保涵盖全球最前沿的信息流。 - 关注以下重点来源： - 技术博客（OpenAI, Anthropic, Google DeepMind, Meta AI 等） - 学术平台（arXiv, Hugging Face Papers） - 开发者社区（GitHub Trending, Product Hunt AI） - 行业新闻（The Verge, TechCrunch, IT 之家等） 2. **内容分类与筛选**： - 将搜集到的资讯按以下板块进行归类： - **行业大事件**：监管政策、巨头交易、投融资动态等。 - **大模型动态**：新模型发布、架构创新（如 MoE, Diffusion 模型）、API 更新。 - **学术前沿**：精选 2-3 篇当日或近期高质量的 arXiv 论文，重点分析其解决的问题和核心贡献。 - **开源工具与项目**：在 GitHub 上活跃或具有极高应用潜力的 AI 工具。 - **新技术标准**：通信协议、互操作性标准、AI 安全评估框架等。 3. **深度分析与撰写**： - **拒绝浅尝辄止**：对于每一条资讯，不应只写“发布了 X”，而应解释“为什么 X 很重要”、“它的技术背景是什么”以及“对行业可能产生的深远影响”。 - **技术术语准确性**：确保中文术语翻译准确且符合领域共识。 4. **输出格式建议**： - 使用 Markdown 结构化输出。 - 每一条资讯都应包含：**[标题]**、**[核心摘要]**、**[深度解析/影响分析]**、**[来源链接]**（如果能获取到的话）。 ## 核心原则 - **时效性优先**：内容应贴合“今日”或“近 24 小时”。 - **客观公正**：避免过度煽情或主观臆断，专注于事实、性能数据和技术细节。 - **关联分析**：如果多个动态互有关联，尝试将点状的信息连成线，在内容中解释它们之间的关联。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: gingiris/startup-launch-playbook (version 1.0.1)

--- name: startup-launch-playbook version: 1.0.0 description: This skill redirects to [Gingiris Launch Playbook](https://github.com/Gingiris/gingiris-launch) author: Gingiris tags: - growth - marketing - startup --- # Startup Launch Playbook > **This skill redirects to [Gingiris Launch Playbook](https://github.com/Gingiris/gingiris-launch)** Complete startup launch strategy from 30x Product Hunt #1 winner. Covers launch timing, go-to-market planning, community mobilization, and post-launch momentum. ## What You Get - Pre-launch preparation checklist - Launch day timeline (hour by hour) - Community activation tactics - Post-launch growth strategies - Real case studies from successful launches ## Quick Start ``` Read the full playbook: https://github.com/Gingiris/gingiris-launch ``` --- Keywords: startup launch, launch strategy, product launch, how to launch startup, startup launch checklist, launch day, pre-launch, post-launch


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: johnwong1003/brandstrategyanalysisdirector (version 1.0.0)

--- name: brand-analysis-bd1 description: | BD-1 品牌分析技能。专为品牌设计研究助理场景设计，包含四个核心模块： 模块A 拆案例——当用户提供品牌案例、参考图、竞品列表时触发，提取共性、差异、策略与可复用方法； 模块B 组织设计逻辑——当用户提供品牌背景、用户画像、设计方向等散乱资料时触发，整理成可讲清楚的方案逻辑； 模块C 追行业——当用户给出行业关键词（如新消费、包装设计趋势、品牌升级）时触发，输出行业摘要与趋势观察； 模块D 大资料整理——当用户手里有大量案例、报告、品牌档案需要归类提炼时触发，完成归类、去重、提炼、建立索引。 触发关键词：拆案例、品牌分析、设计逻辑、行业趋势、资料整理、品牌策略、视觉方向、竞品分析、BD-1。 --- # BD-1 品牌分析技能 > 你的角色：品牌设计研究助理。不替设计师做决策，帮设计师更快拆案例、梳逻辑、追行业、吃资料。 > 最终判断权永远属于设计师。 --- ## 模块选择指引 | 用户输入 | 使用模块 | |---------|---------| | 提供品牌案例、参考图、竞品 | **模块A：拆案例** | | 提供品牌背景、用户、设计方向等散乱资料 | **模块B：组织设计逻辑** | | 给出行业关键词，要了解趋势动态 | **模块C：追行业** | | 手里有大量案例/报告/档案需要整理 | **模块D：大资料整理** | 详细输出格式见各模块 reference 文件： - 模块A → `references/module-a-case-analysis.md` - 模块B → `references/module-b-design-logic.md` - 模块C → `references/module-c-industry-tracking.md` - 模块D → `references/module-d-data-digest.md` --- ## 核心工作原则 1. **提取结构，不复述表面** — 案例分析要挖到策略层，不只描述视觉 2. **逻辑先行** — 输出前先确认信息是否足够，缺信息要明确指出 3. **可复用优先** — 每次分析都要提炼出可迁移到其他项目的方法 4. **边界清晰** — 哪些适合借鉴、哪些不适合照搬，要明确说明 5. **辅助判断，不替代判断** — 提供结构化参考，决策权在设计师 --- ## 快速启动 收到用户输入后： 1. 判断属于哪个模块（可同时涉及多个） 2. 读取对应 reference 文件中的输出格式 3. 按格式输出，缺信息时在"可继续深化的问题"中列出 4. 完成分析后，将结果输出为 PPT 文件并保存到用户桌面（见下方"最终输出"） --- ## 最终输出：生成 PPT 到桌面 每次完成分析后，**必须**将结构化结果生成为 `.pptx` 文件，保存路径： ``` C:\Users\Lenovo\Desktop\BD1_[模块名]_[主题关键词].pptx ``` ### PPT 生成规范 - 使用 `pptxgenjs` 从零创建（参考 pptx skill 的 pptxgenjs.md） - 每个模块的输出对应一组幻灯片，结构如下： | 幻灯片 | 内容 | |--------|------| | 封面 | 项目名称 + 模块名称 + 日期 | | 各分析章节 | 按模块输出格式，每个一级标题对应一张幻灯片 | | 结尾 | 可继续深化的问题 / 下一步建议 | ### 设计要求 - 配色：使用 **Charcoal Minimal**（`#36454F` 主色 / `#F2F2F2` 浅色 / `#212121` 黑） - 字体：标题 Calibri Bold 36pt，正文 Calibri 14pt - 每张幻灯片必须有视觉元素（色块、图形或图标），不允许纯文字白底 - 版式多样，避免每张都是同一布局 ### 生成后 - 告知用户文件已保存到桌面，文件名是什么 - 如果生成失败（缺少依赖），告知用户需要安装 `npm install -g pptxgenjs`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kenswj/nano-pdf-1-0-0 (version 1.0.0)

--- name: nano-pdf description: Edit PDFs with natural-language instructions using the nano-pdf CLI. homepage: https://pypi.org/project/nano-pdf/ metadata: {"clawdbot":{"emoji":"📄","requires":{"bins":["nano-pdf"]},"install":[{"id":"uv","kind":"uv","package":"nano-pdf","bins":["nano-pdf"],"label":"Install nano-pdf (uv)"}]}} --- # nano-pdf Use `nano-pdf` to apply edits to a specific page in a PDF using a natural-language instruction. ## Quick start ```bash nano-pdf edit deck.pdf 1 "Change the title to 'Q3 Results' and fix the typo in the subtitle" ``` Notes: - Page numbers are 0-based or 1-based depending on the tool’s version/config; if the result looks off by one, retry with the other. - Always sanity-check the output PDF before sending it out.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nimblev2023/getmem-openclaw (version 1.0.3)

--- name: "@getmem/openclaw-getmem" description: "Persistent memory for every user via getmem.ai. Automatically ingests conversations and injects relevant context before each agent reply. Two API calls. $20 free on signup." metadata: openclaw: emoji: 🧠 install: - id: npm-getmem-ai kind: pip package: getmem-ai --- # getmem.ai Memory Plugin for OpenClaw Gives your OpenClaw agent persistent memory via [getmem.ai](https://getmem.ai). ## Install ```bash openclaw plugins install clawhub:@getmem/openclaw-getmem openclaw config set plugins.openclaw-getmem.apiKey gm_live_... openclaw gateway restart ``` Get your API key at [platform.getmem.ai](https://platform.getmem.ai) — **$20 free credit on signup**. ## How it works - **Before each reply** — fetches memory context for the user and injects it into the agent prompt - **After each reply** — ingests the exchange into getmem for future retrieval Memory accumulates automatically. No code changes needed.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: carlulsoe/parakeet-stt (version 1.1.0)

--- name: parakeet-stt description: >- Local speech-to-text with NVIDIA Parakeet TDT 0.6B v3 (ONNX on CPU). 30x faster than Whisper, 25 languages, auto-detection, OpenAI-compatible API. Use when transcribing audio files, converting speech to text, or processing voice recordings locally without cloud APIs. homepage: https://github.com/groxaxo/parakeet-tdt-0.6b-v3-fastapi-openai metadata: {"clawdbot":{"emoji":"🦜","env":["PARAKEET_URL"]}} --- # Parakeet TDT (Speech-to-Text) Local transcription using NVIDIA Parakeet TDT 0.6B v3 with ONNX Runtime. Runs on CPU — no GPU required. ~30x faster than realtime. ## Installation ```bash # Clone the repo git clone https://github.com/groxaxo/parakeet-tdt-0.6b-v3-fastapi-openai.git cd parakeet-tdt-0.6b-v3-fastapi-openai # Run with Docker (recommended) docker compose up -d parakeet-cpu # Or run directly with Python pip install -r requirements.txt uvicorn app.main:app --host 0.0.0.0 --port 5000 ``` Default port is `5000`. Set `PARAKEET_URL` to override (e.g., `http://localhost:5092`). ## API Endpoint OpenAI-compatible API at `$PARAKEET_URL` (default: `http://localhost:5000`). ## Quick Start ```bash # Transcribe audio file (plain text) curl -X POST $PARAKEET_URL/v1/audio/transcriptions \ -F "file=@/path/to/audio.mp3" \ -F "response_format=text" # Get timestamps and segments curl -X POST $PARAKEET_URL/v1/audio/transcriptions \ -F "file=@/path/to/audio.mp3" \ -F "response_format=verbose_json" # Generate subtitles (SRT) curl -X POST $PARAKEET_URL/v1/audio/transcriptions \ -F "file=@/path/to/audio.mp3" \ -F "response_format=srt" ``` ## Python / OpenAI SDK ```python import os from openai import OpenAI client = OpenAI( base_url=os.getenv("PARAKEET_URL", "http://localhost:5000") + "/v1", [REDACTED_SECRET]" ) with open("audio.mp3", "rb") as f: transcript = client.audio.transcriptions.create( model="parakeet-tdt-0.6b-v3", file=f, response_format="text" ) print(transcript) ``` ## Response Formats | Format | Output | |--------|--------| | `text` | Plain text | | `json` | `{"text": "..."}` | | `verbose_json` | Segments with timestamps and words | | `srt` | SRT subtitles | | `vtt` | WebVTT subtitles | ## Supported Languages (25) English, Spanish, French, German, Italian, Portuguese, Polish, Russian, Ukrainian, Dutch, Swedish, Danish, Finnish, Norwegian, Greek, Czech, Romanian, Hungarian, Bulgarian, Slovak, Croatian, Lithuanian, Latvian, Estonian, Slovenian Language is auto-detected — no configuration needed. ## Web Interface Open `$PARAKEET_URL` in a browser for drag-and-drop transcription UI. ## Docker Management ```bash # Check status docker ps --filter "name=parakeet" # View logs docker logs -f <container-name> # Restart docker compose restart # Stop docker compose down ``` ## Why Parakeet over Whisper? - **Speed**: ~30x faster than realtime on CPU - **Accuracy**: Comparable to Whisper large-v3 - **Privacy**: Runs 100% locally, no cloud calls - **Compatibility**: Drop-in replacement for OpenAI's transcription API


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