OpenClaw/clawhub-security-signals huggingface dataset:
Skill: rsquaredsolutions2026/agentbets-polymarket-monitor (version 1.1.0)

--- name: polymarket-monitor description: "Monitor Polymarket prediction markets for price movements, volume spikes, and new listings. Track specific markets, check order book depth, and surface trending predictions. Use when asked about Polymarket, prediction market prices, market volume, or contract probabilities." metadata: openclaw: emoji: "🔮" requires: bins: ["curl", "jq"] --- # Polymarket Monitor Track prediction market prices, volume, and liquidity on Polymarket using the Gamma API and CLOB API. ## When to Use Use this skill when the user asks about: - Trending or popular Polymarket markets - Current price or probability for a prediction market - Volume or trading activity on a market - Order book depth or liquidity for a specific outcome - New or recently created prediction markets - Price movements or volume spikes on Polymarket ## Key Concepts - **Price = Implied Probability**: A contract at $0.65 means 65% implied probability - **Token ID**: Each outcome (Yes/No) has a unique token ID used by the CLOB API - **Condition ID**: The unique identifier for a market (question) in the Gamma API - **CLOB**: Central Limit Order Book — where bids and asks are matched ## Operations ### 1. List Trending Markets Show the top active markets sorted by 24-hour volume: ```bash curl -s "https://gamma-api.polymarket.com/markets?closed=false&active=true&order=volume24hr&ascending=false&limit=10" \ | jq '[.[] | { question: .question, price_yes: (.outcomePrices // "[]" | fromjson | .[0] // "N/A"), price_no: (.outcomePrices // "[]" | fromjson | .[1] // "N/A"), volume_24h: ((.volume24hr // 0) | tonumber | round), total_volume: ((.volumeNum // 0) | tonumber | round), liquidity: ((.liquidityNum // 0) | tonumber | round), end_date: .endDate }]' ## About Built by [AgentBets](https://agentbets.ai) — full tutorial at [agentbets.ai/guides/openclaw-polymarket-monitor-skill/](https://agentbets.ai/guides/openclaw-polymarket-monitor-skill/). Part of the [OpenClaw Skills series](https://agentbets.ai/guides/#openclaw-skills) for the [Agent Betting Stack](https://agentbets.ai/guides/agent-betting-stack/).


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhouhuihui008/mx-finance-data-1-0-8 (version 1.0.0)

--- name: mx_finance_data description: 基于东方财富数据库，支持自然语言查询金融数据，覆盖A港美、基金、债券等多种资产，含实时行情、公司信息、估值、财务报表等，可用于投资研究、交易复盘、市场监控、行业分析、信用研究、财报审计、资产配置等场景，适配机构与个人多元需求。返回结果包含数据说明及 xlsx 文件。Natural language query for financial data across all markets, including A-shares, ETFs, bonds, Hong Kong and US stocks, and funds. It provides L1/L2 data, financial indicators, company profiles and valuation metrics. Ideal for investment research, strategy backtesting, market monitoring and industry analysis. It meets the needs of diverse institutions and individuals. metadata: { "openclaw": { "requires": { "env": ["EM_API_KEY"], "bins": ["python3", "pip3"] }, "install": [ { "id": "pip-deps", "kind": "python", "package": "httpx pandas openpyxl", "label": "Install Python dependencies" } ] } } --- # 金融数据查询 ## 密钥来源与安全说明 - 本技能仅使用一个环境变量：`EM_API_KEY`。 - `EM_API_KEY` 由东方财富妙想服务（`https://ai.eastmoney.com/mxClaw`）签发，用于其接口鉴权。 - 在提供密钥前，请先确认密钥来源、可用范围、有效期及是否支持重置/撤销。 - 禁止在代码、提示词、日志或输出文件中硬编码/明文暴露密钥。 ## 功能范围 ### 1. 支持查询的对象范围 * 股票（A 股、港股、美股） * 板块、指数、股东 * 企业发行人、债券、非上市公司 * 股票市场、基金市场、债券市场 ### 2. 支持查询的数据类型 支持查询以下类型的结构化数据： * **实时行情**（现价、涨跌幅、盘口数据等） * **量化数据**（技术指标、资金流向等） * **报表数据**（营收、净利润、财务比率等） ### 3. 支持的查询方式与**严格限制** 支持多实体、多指标、多时间范围的组合查询。**受后端接口限制，单次查询必须遵循以下配额：** * **实体上限**：单次最多支持 **5 个** 股票/金融实体。 * **指标上限**：单次最多支持 **3 个** 核心指标。 * **处理逻辑**：若用户请求超过上述限制，Skill 将优先处理前 5 个实体和前 3 个指标，并在结果说明文件中予以提示。 > **示例：** “查询 A、B、C、D、E、F 六只股票的营收” -> 仅返回前五只。 ### 4. 输出结果 Skill 执行后会输出以下文件： - 一个查数结果 `xlsx` 文件，用于承载结构化查询结果 - 一个结果说明 `txt` 文件，用于描述查询内容、结果含义及必要说明 ------ ## 查询示例 ### 基础指标查询 - 东方财富的基本面 - 贵州茅台最近一年的营业收入和净利润 - 半导体etf的业绩表现 ### 实时行情与盘口查询 - 当前300059的实时买单 - 英伟达现在的最新价和涨跌幅 - 沪深300当前点位和成交额 ### 多实体、多指标、多时间查询（注意配额） * **合规示例**：对比创业板指、沪深300、中证500（3个实体）春节以来的涨幅（1个指标）。 * **合规示例**：东方财富、拼多多（2个实体）最近一年的营收、毛利、净利（3个指标）。 * **超限提示**：若查询“沪深前十大权重股的PE”，系统将截取前 5 名进行查询。 ------ ## 补充说明 - 本 Skill 面向金融结构化数据与关系数据查询，不侧重主观分析、投资建议或资讯解读。 - 语句中必须包含明确的金融实体名称。 - 当用户使用自然语言发起查询时，Skill 可根据问句内容识别查询对象、指标和时间范围，并输出结构化结果文件。 - 对于支持范围内的查询请求，优先输出结构化查数结果，并同时生成结果说明文件，便于后续使用。 ## 前提条件 ### 1. 注册东方财富妙想账号 访问 https://ai.eastmoney.com/mxClaw 注册账号并获取API_KEY。 ### 2. 配置 Token ```bash # macOS 添加到 ~/.zshrc，Linux 添加到 ~/.bashrc export EM_API_KEY="your_api_key_here" ``` 然后根据系统执行对应的命令： **macOS：** ```bash source ~/.zshrc ``` **Linux：** ```bash source ~/.bashrc ``` ### 3. 安装依赖 ```bash pip3 install httpx pandas openpyxl --user ``` ## 快速开始 ### 在工作目录下执行 ```bash python3 {baseDir}/scripts/get_data.py --query "贵州茅台近期走势如何" ``` ### 输出示例 ``` xlsx: /path/to/miaoxiang/mx_finance_data/mx_finance_data_9535fe18.xlsx 描述: /path/to/miaoxiang/mx_finance_data/mx_finance_data_9535fe18_description.txt 行数: 42 ``` ### 输出文件说明 | 文件 | 说明 | | --- | --- | | `mx_finance_data_<查询id>.xlsx` | 结构化数据表，包含请求的实体与指标 | | `mx_finance_data_<查询id>_description.txt` | 包含查询逻辑说明、字段含义及**配额截断提示** |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: axelhu/task-watchdog-axel (version 1.0.0)

--- name: task-watchdog version: 1.0.0 description: 任务锁与超时监控系统。外部文件承载任务状态，不污染 agent 上下文，纯靠 heartbeat + GRACE 判断，不发即时告警。 --- # Task Watchdog ## 核心概念 Lock 文件是任务的外部状态载体，放于 `~/.openclaw/agents/{agent_id}/locks/`： - `active/` — 正在执行的任务 - `archive/YYYY-MM-DD/` — 归档（done / abandoned / timeout） **两个时间字段，职责不同：** ``` last_heartbeat — lock-self-check 更新（纯心跳维持，不说明任务有进展） last_progress — lock-update --progress 时更新（标记真实进展） ``` **状态判断（用 last_progress，不看 last_heartbeat）：** ``` ✅ 正常 session 存在，last_progress 正常 ⚠️ SESSION_DEAD session 已消失 ⚠️ STALLED session 存在，但 last_progress 超 GRACE×3 ⚠️ HEARTBEAT_DELAY 心跳延迟 🚫 abandoned session 消失 + last_progress 超 GRACE×3 📦 done 任务完成，立即归档 ``` ## 脚本 | 脚本 | 用途 | |------|------| | `lock-create.sh` | 创建 lock → active/ | | `lock-update.sh` | 更新 heartbeat；`--progress` 时同时更新 last_progress | | `lock-done.sh` | 标记完成 → 归档 | | `lock-status.sh` | 查询状态 | | `scan-locks.sh` | 扫描 active/，异常归档 | | `lock-archive.sh` | 归档N天前 / 清理N天前 / 统计 | | `lock-report.sh` | 查看所有活跃任务状态 | | `lock-self-check.sh` | Agent 自检：更新心跳 + 处理 abandoned | ## 推荐用法 **关键节点调用：** ``` 任务开始 → lock-create 每个关键步骤完成 → lock-update --progress "X完成，开始Y" 任务全部完成 → lock-done ``` **HEARTBEAT 触发时调用自检：** ``` 收到心跳轮询时 → 调用 lock-self-check.sh → 自动完成： 无活跃任务 → 无输出，agent 回复 HEARTBEAT_OK owner session 存活 → 只更新 last_heartbeat owner session 已死 → 接管任务（更新 session_id + last_heartbeat） → 无需判断逻辑，脚本自动处理 ``` ## 设计目标 **任务续做保障系统**，不是即时告警系统： - 中断后 lock 留在 active/，Supervisor 扫描归档 - 自检机制让 agent 自动继续被中断的任务 - 定期报告让负责人知道哪些任务需要接管或放弃 ## 常用命令 ```bash ./lock-self-check.sh --agent-id xxx --session-id xxx # HEARTBEAT 触发时调用 ./lock-report.sh # 查看所有活跃任务状态 ./lock-archive.sh --list # 同上 ./lock-archive.sh --archive-days 7 # 归档7天前完成的任务 ./lock-archive.sh --cleanup-days 30 # 清理30天前的归档 ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: eric642/deprecatedcc (version 1.0.0)

--- name: deprecated description: This skill is deprecated and should not be used. --- This is a deprecated skill, please do not use it. This is a deprecated skill, please do not use it. This is a deprecated skill, please do not use it. This is a deprecated skill, please do not use it. This is a deprecated skill, please do not use it. This is a deprecated skill, please do not use it. This is a deprecated skill, please do not use it. This is a deprecated skill, please do not use it. This is a deprecated skill, please do not use it. This is a deprecated skill, please do not use it. This is a deprecated skill, please do not use it.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: draco-cheng/ai-readme-manager (version 1.3.4)

--- name: ai-readme-manager description: Manages AI_README.md files so AI agents remember your project conventions across every session version: 1.3.4 metadata: openclaw: install: - kind: node package: ai-readme-mcp bins: [ai-readme-mcp] homepage: https://github.com/Draco-Cheng/ai-readme-mcp emoji: "📋" --- # AI README Manager Manages `AI_README.md` files that document your project's conventions for AI assistants. Gives your agent persistent memory of project rules across every session and every iteration. ## Setup Before this skill works, you must register the MCP server with OpenClaw: **Option 1: CLI (Recommended)** On macOS/Linux: ```bash openclaw mcp set ai-readme-manager '{"command":"npx","args":["ai-readme-mcp@1.3.4"]}' ``` On Windows (cmd): ```cmd openclaw mcp set ai-readme-manager "{\"command\":\"npx\",\"args\":[\"ai-readme-mcp@1.3.4\"]}" ``` **Option 2: Edit config file** Add to `~/.openclaw/openclaw.json`: ```json { "mcp": { "servers": { "ai-readme-manager": { "command": "npx", "args": ["ai-readme-mcp@1.3.4"] } } } } ``` Then restart OpenClaw and verify with `openclaw mcp list`. ## When to use Call `get_context_for_file` before any code-related task — editing, creating, or reviewing files in a project. ## Tools available - `get_context_for_file` — get relevant project conventions for a specific file path - `init_ai_readme` — initialize and populate empty AI_README files in a project - `update_ai_readme` — record new conventions or architectural decisions - `validate_ai_readmes` — check quality and token efficiency of all AI_README files - `compress_ai_readme` — remove filler language from an AI_README file to reduce token footprint - `discover_ai_readmes` — scan and list all AI_README files in a project ## Instructions ### MCP: ai-readme-manager Before any code-related task, ALWAYS call `get_context_for_file(projectRoot, path)` first. If `get_context_for_file` reports empty or missing AI_README files, call `init_ai_readme(projectRoot)` to initialize them. If the user's request or your plan conflicts with AI_README conventions (including during planning), STOP and call `update_ai_readme` to resolve the conflict before proceeding. When establishing new conventions or making architectural decisions, call `update_ai_readme` to record them. If AI_README is missing a convention that is already used in 2+ files, call `update_ai_readme` to record it.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lkisme/carkey1 (version 1.1.2)

--- name: carkey description: 车辆信息查询技能。查询车辆位置、车况（车锁、车门、车窗、空调、电源状态等）。触发词：查车、车辆位置、车况、我的车在哪。跨平台支持 Linux/macOS/Windows。 --- ## 何时使用 - 用户查询车辆位置："我的车在哪"、"查一下车在哪" - 用户查询车况信息："车锁了吗"、"车窗关了吗"、"车辆状态" - 用户需要提供认证信息时，引导输入 `vehicleToken####accessToken` ## 快速使用 **方式 1：绝对路径（推荐 Agent 使用）** ```bash # 假设技能安装在 ~/.openclaw/workspace/skills/carkey ~/.openclaw/workspace/skills/carkey/scripts/vehicle-query.sh ~/.openclaw/workspace/skills/carkey/scripts/vehicle-query.sh position ~/.openclaw/workspace/skills/carkey/scripts/vehicle-query.sh condition ``` **方式 2：相对路径（用户手动执行）** ```bash cd carkey ./scripts/vehicle-query.sh ./scripts/vehicle-query.sh position ./scripts/vehicle-query.sh condition ``` ## 认证 **格式：** `vehicleToken####accessToken`（4 个 `#` 分隔） 首次使用会提示输入，自动缓存。 | 系统 | 缓存路径 | |------|----------| | Linux/macOS | `~/.carkey_cache.json` | | Windows | `%USERPROFILE%/.carkey_cache.json` | ## 状态码 | 字段 | 值含义 | |------|--------| | power | 0=熄火, 1=ACC, 2=ON | | gear | 1=P, 2=N, 3=D, 4=R, 5=S | | door/window/trunk | 0=关闭, 1=开启 | | lock | 0=解锁, 1=上锁 | ## 错误处理 | 场景 | 处理 | |------|------| | 无缓存 | 引导用户提供 token | | Token 过期 | 提示重新认证，删除旧缓存 | | 请求失败 | 自动重试 2 次，30 秒超时 | | 缺少依赖 | 提示安装 curl/jq | ## 系统支持 | 系统 | 依赖安装 | |------|----------| | Linux (Ubuntu/Debian) | `sudo apt-get install curl jq` | | Linux (CentOS/RHEL) | `sudo yum install curl jq` | | macOS | `brew install curl jq` | | Windows (Git Bash) | 已包含 | | Windows (WSL) | `wsl sudo apt-get install curl jq` | ## 文件结构 ``` carkey/ ├── SKILL.md # 本文档 ├── README.md # 详细说明 ├── _meta.json # 元数据 └── scripts/ └── vehicle-query.sh # 查询脚本（跨平台） ```

path: _meta.json
content: slug: carkey
version: 1.1.1

Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: googleworkspace-bot/gws-meet (version 1.0.13)

--- name: gws-meet description: "Manage Google Meet conferences." metadata: version: 0.22.5 openclaw: category: "productivity" requires: bins: - gws cliHelp: "gws meet --help" --- # meet (v2) > **PREREQUISITE:** Read `../gws-shared/SKILL.md` for auth, global flags, and security rules. If missing, run `gws generate-skills` to create it. ```bash gws meet <resource> <method> [flags] ``` ## API Resources ### conferenceRecords - `get` — Gets a conference record by conference ID. - `list` — Lists the conference records. By default, ordered by start time and in descending order. - `participants` — Operations on the 'participants' resource - `recordings` — Operations on the 'recordings' resource - `smartNotes` — Operations on the 'smartNotes' resource - `transcripts` — Operations on the 'transcripts' resource ### spaces - `create` — Creates a space. - `endActiveConference` — Ends an active conference (if there's one). For an example, see [End active conference](https://developers.google.com/workspace/meet/api/guides/meeting-spaces#end-active-conference). - `get` — Gets details about a meeting space. For an example, see [Get a meeting space](https://developers.google.com/workspace/meet/api/guides/meeting-spaces#get-meeting-space). - `patch` — Updates details about a meeting space. For an example, see [Update a meeting space](https://developers.google.com/workspace/meet/api/guides/meeting-spaces#update-meeting-space). ## Discovering Commands Before calling any API method, inspect it: ```bash # Browse resources and methods gws meet --help # Inspect a method's required params, types, and defaults gws schema meet.<resource>.<method> ``` Use `gws schema` output to build your `--params` and `--json` flags.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: odinbot33/oee-ai-cost-tracker (version 1.0.0)

# 🐾 AI Cost Tracker — Know What You Spend > by Odin's Eye Enterprises — Ancient Wisdom. Modern Intelligence. Track every AI API call, generate spend reports, and get routing suggestions to cut costs. ## What It Does 1. **Logs** every API call with model, tokens, cost to JSONL 2. **Reports** spending by model, time period, and use case 3. **Suggests** cheaper model routing based on task complexity ## Trigger Phrases - "how much have I spent on AI" - "AI cost report" - "track this API call" - "show AI spending" - "cost breakdown" ## Usage ### Logging (in your code) ```python from tracker import log_usage log_usage(model="claude-3-haiku", input_tokens=500, output_tokens=200, task="humanize") ``` ### Reports ```bash # Full spend report python report.py # Last 7 days python report.py --days 7 # By model python report.py --by-model ``` ## Files - `tracker.py` — logging library, import into your tools - `report.py` — CLI dashboard for spend analysis - `pricing.json` — model pricing data (update as needed) ## Requirements - Python 3.10+ (stdlib only) - No API keys needed (this tracks YOUR usage) ## For Agents To check spending: `python report.py` To log usage from other tools: `from tracker import log_usage` <!-- 🐾 Odin's ravens count every coin -->


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 1kalin/customer-onboarding (version 1.0.0)

--- name: Customer Onboarding description: Systematically onboard new clients with checklists, welcome sequences, milestone tracking, and success metrics. Reduce churn by nailing the first 90 days. --- # Customer Onboarding You are a customer onboarding specialist. Help users create and manage systematic onboarding processes for their clients. ## Core Capabilities ### 1. Onboarding Checklist Generator Create step-by-step onboarding checklists customized to the business. Include: - Pre-onboarding prep (before kickoff) - Day 1 actions (welcome, access, introductions) - Week 1 milestones (setup, first value moment) - Week 2-4 milestones (adoption, training) - Day 30/60/90 checkpoints ### 2. Welcome Sequence Draft onboarding emails/messages: - Welcome email (immediate) - Getting started guide (Day 1) - Check-in (Day 3) - Tips & best practices (Day 7) - Milestone celebration (Day 14) - Review & feedback request (Day 30) ### 3. Kickoff Meeting Agenda Structure client kickoff calls: - Introductions (5 min) - Goals & success criteria (10 min) - Timeline & milestones (10 min) - Roles & responsibilities (5 min) - Tools & access setup (10 min) - Q&A and next steps (10 min) ### 4. Success Metrics Define onboarding health metrics: - Time to first value (TTFV) - Feature adoption rate - Onboarding completion rate - Customer satisfaction score (CSAT) at Day 30 - Support ticket volume during onboarding ### 5. Risk Detection Flag onboarding risks: - Missed milestones - Low engagement signals - Delayed responses - Scope creep indicators - Champion departure ## Output Format Checklists use checkbox format. Emails are ready to send. Everything includes ownership (who does what) and deadlines.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: shenghoo123-png/kay-oschina-content-v2 (version 1.0.0)

# 开源中国发布技能 ## 1. 平台简介 开源中国（OSCHINA）是中国最大的开源技术社区，成立于2008年，拥有众多开发者用户。平台提供博客、问答、开源项目推荐等技术内容分享，同时也支持企业招聘和技术资讯发布。 **网址**: https://www.oschina.net --- ## 2. 注册/登录方式 ### 注册方式 - **邮箱注册**：输入邮箱 + 设置密码 - **手机号注册**：输入手机号 + 验证码 - **第三方登录**：支持 Github / 微信 / 支付宝 登录 ### 登录入口 - 首页右上角「登录/注册」 - 推荐使用 Github 登录（技术身份更匹配，也方便后续关联开源项目） --- ## 3. 发布文章流程 ### 步骤一：进入博客后台 1. 登录后，点击右上角头像 → 「我的博客」或「管理博客」 2. 进入博客管理后台 ### 步骤二：写文章 1. 点击「发布博客」或「写博客」 2. 进入在线编辑器（支持富文本和 Markdown 两种模式） ### 步骤三：填写文章信息 1. **标题**：输入文章标题 2. **分类**：选择文章分类（原创翻译、新闻、技巧分享、值得关注等） 3. **标签**：添加1-5个标签 4. **正文**：在编辑器中撰写内容 5. **封面图**：可上传文章封面图（可选） ### 步骤四：发布 1. 点击「发布」或「保存」 2. 文章审核后公开（通常几分钟到几小时） ### 草稿与编辑 - 文章会自动保存草稿 - 已发布的文章也可以随时编辑修改 --- ## 4. 文章模板 ### 文章标题模板 ``` [技术点] + [场景/价值] + [类型标识] 示例： 深入理解 Python 装饰器及其高级用法 Spring Boot 整合 Redis 实现缓存实战 【译】2024年 Web 开发趋势预测 ``` ### 文章正文模板 ```markdown # 文章标题 > 导读：本文介绍[核心内容]，适合[目标读者]，读完可掌握[收获]。 ## 背景 [介绍文章背景和解决的问题] ## 目录 1. [章节一] 2. [章节二] 3. [章节三] --- ## 一、[章节标题] [正文内容] ### 1.1 [小节] [代码示例] ```python # 示例代码 def example(): pass ``` ## 总结 [总结全文要点] ## 参考 - [参考1] - [参考2] ``` --- ## 5. 注意事项 1. **原创声明**：平台鼓励原创，翻译文章需注明「译」 2. **内容质量**：技术深度要够，代码要有实际运行价值 3. **标题规范**：避免夸大、标题党，保持技术严谨性 4. **配图规范**：图片使用平台图床或可靠的外部链接 5. **代码规范**：代码块标明语言，版本说明清楚 6. **频繁发布限制**：新手每天发布数量有限制，优质用户可提升权限 --- ## 6. 常见问题 **Q: 开源中国文章有收益吗？** A：开源中国有博客之星等活动，优质内容可获得推荐和奖励。 **Q: 如何提高文章阅读量？** A：优化标题、选对分类和标签、提高更新频率、与其他博客互动。 **Q: 可以发布软文吗？** A：可以在「ITeye」子站发布，oschina.net 主站对商业内容有限制。 **Q: 文章被举报抄袭怎么办？** A：需保证原创性，被认定抄袭会导致账号受限。 **Q: 如何关联 GitHub 开源项目？** A：在个人资料中绑定 GitHub，可自动同步开源项目到个人主页。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tian-0818/markdown-to-docx-converter (version 1.0.2)

# md2word Skill ## Description A skill that converts .md files to Word files. When users send .md files, it automatically accesses the `https://md2word.com/en` website, uploads the file, and downloads the converted Word file. ## Input Format - Accepts .md files sent by users ## Output Format - Returns the converted Word file to users ## Workflow 1. Receive .md file from user 2. Access `https://md2word.com/en` website 3. Upload .md file to the website 4. Wait for conversion to complete 5. Download the converted Word file 6. Return the Word file to the user ## Dependencies - requests - beautifulsoup4 --- ## 功能描述 将.md文件转换为Word文件的技能。当用户发送.md文件时，自动访问 `https://md2word.com/en` 网站，上传文件并下载转换后的Word文件。 ## 输入格式 - 接收用户发送的.md文件 ## 输出格式 - 向用户返回转换后的Word文件 ## 工作流程 1. 接收用户发送的.md文件 2. 访问 `https://md2word.com/en` 网站 3. 上传.md文件到网站 4. 等待转换完成 5. 下载转换后的Word文件 6. 将Word文件返回给用户 ## 依赖 - requests - beautifulsoup4


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: ethan-china/miniest-first-customers (version 1.0.1)

--- name: first-customers description: > 使用极简主义创业者手册制定赢得前100个客户的策略。 当有人有产品需要找客户，或在早期销售方面挣扎时使用。 核心：跳过发布，专注于一对一销售。 trigger_keywords: - 第一批客户 - 前100个客户 - 找客户 - 怎么销售 - 冷启动 - 第一个用户 read_when: - 用户有了产品但没有客户 - 用户在思考如何获得第一批用户 - 用户想了解早期销售策略 --- # 获得首批客户 — 极简主义创业者 你是一位极简主义创业顾问，遵循Sahil Lavingia《极简主义创业者》的理念。帮助用户向前100个客户销售。 ## 核心原则 **跳过发布，专注于销售。** "病毒式成功"是一个神话，根本不存在。每一个看似一夜成功的背后，都是数月或数年的努力工作。你的工作是一个接一个地销售，从每次互动中学习，建立势头。 ## 同心圆销售法 从最关心你的人向外销售到最不关心你的人： ### 第1圈：朋友和家人 - 从这里开始。是的，这很尴尬。无论如何要做。 - 让他们成为你的第一批客户，而不是投资者 - 他们比任何人都更信任你。如果他们不购买，谁会购买？ - 要求诚实的反馈，而不是社交媒体帖子 - Kickstarter说："支持总是从你认识的人开始" ### 第2圈：你的社群 - 你已经认同并持续贡献的社群 - 这些是了解这个问题的主题专家 - 三个步骤： 1. **列出所有曾经写过或分享过类似业务内容的人** 2. **亲自联系所有人** — 带他们了解你的产品，请他们吃饭，做数百次 3. **要求坦诚的反馈** — 不是评论或社交帖子，只是诚实的反馈 ### 第3圈：陌生人（冷外联） - 冷邮件、电话、消息——这有效。这是Gumroad成长的方式。 - Sahil亲自在网上搜索可以从Gumroad受益的人，并向他们亲自发送邮件，成千上万次 - 示例冷邮件模板： > "嗨[姓名]，我看到你在用[某种方式]处理[某个问题]。我构建了[你的产品]，基本上自动化了所有这些。我很乐意向你展示，或者你可以自己看看：[链接]。有什么问题请告诉我！" - 不要复制粘贴，每封邮件都提高你写更好邮件的能力 - 把每次拒绝作为学习机会 ## 销售不是脏字 重新框架你对销售的思考： - 你不是在说服任何人，你在帮助人们 - 你已经与你的社群建立了关系 - 你在销售一个为他们的生活增加价值的产品 - 将每次失败的转化变成洞察——要么是错误的人，要么是产品需要改进 - 销售是一个教育过程：你的客户了解你，你了解什么有效 ## 定价（一定要收费！） - 免费和1元之间有巨大差距（"零价效应"） - 两种收费方式： - **成本为基础**：你的成本+利润率（例如20-50%） - **价值为基础**：对客户的价值，无论你的成本如何 - 从低价开始，随着产品改进提高价格 - 定价是迭代的，就像其他一切一样。它不是永久的。 - 目标：随着品牌和价值的建立，最终转向分级定价 ## 关键指标 - **手动销售 = 早期增长的99%。** 口碑 = 后期增长的99%。 - 你需要的客户比你想象的少。Slack的IPO：575个客户 = 40%的收入。 - 如果你的产品每月收费100元，200个客户就是20,000元/月。按每个工作日一个客户，不到一年。 - **产品-市场契合度** = 自主注册和使用产品的回头客 ## 何时"发布" 在你有100个付费客户之前不要发布。然后把发布作为对社群支持的庆祝，而不是作为客户获取策略。举办派对，感谢你的客户，邀请他们。 ## 输出 帮助用户创建： 1. 本周要拜访的10位朋友/家人名单 2. 要联系的10位社群成员名单 3. 冷外联模板（个性化的，而非复制粘贴） 4. 初始定价策略 5. 每周销售目标和跟踪方法


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nexscope/amazon-product-compliance (version 1.0.0)

--- name: amazon-product-compliance description: "Product compliance and safety — certifications, labeling requirements, restricted substances, documentation" metadata: nexscope: category: amazon --- # Amazon Product Compliance Product compliance and safety — certifications, labeling requirements, restricted substances, documentation **Supported platforms:** Amazon (US, UK, DE, CA, JP, AU, and all marketplaces). Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions. ## Install ```bash npx skills add nexscope/amazon-product-compliance ``` ## Usage ``` Help me with amazon product compliance for my e-commerce business. ``` ## Capabilities - Product compliance and safety - certifications - labeling requirements - restricted substances - documentation ## 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)