OpenClaw/clawhub-security-signals huggingface dataset:
Skill: sunshine-del-ux/vercel-config-generator (version 1.0.0)

--- name: vercel-config-generator description: 生成专业的 Vercel 配置文件 vercel.json，支持 Serverless Functions、Edge Functions、静态网站、SSR 等多种部署模式。 metadata: {"clawdbot":{"emoji":"▲","requires":{},"primaryEnv":""}} --- # Vercel Config Generator 生成专业的 Vercel 配置文件。 ## 功能 - ⚡ 一键生成配置 - 🌐 多部署模式支持 - ⚙️ Serverless 配置 - 📦 边缘计算支持 ## 部署模式 | 模式 | 说明 | |------|------| | static | 静态网站 | | serverless | Serverless Functions | | edge | Edge Functions | | swa | Single Page App | ## 使用方法 ### 静态网站 ```bash vercel-config-generator --type static ``` ### Serverless ```bash vercel-config-generator --type serverless ``` ### Edge Functions ```bash vercel-config-generator --type edge ``` ## 输出示例 ```json { "buildCommand": "npm run build", "outputDirectory": "dist", "framework": null, "version": 2, "regions": ["iad1"], "functions": { "api/**/*.js": { "runtime": "nodejs18.x", "maxDuration": 10, "memory": 1024 } }, "headers": [ { "source": "/(.*)", "headers": [ { "key": "X-Frame-Options", "value": "DENY" }, { "key": "X-Content-Type-Options", "value": "nosniff" } ] } ], "rewrites": [ { "source": "/api/(.*)", "destination": "/api/$1" } ] } ``` ## 常见配置 ### React/Next.js ```bash vercel-config-generator --framework nextjs ``` ### Vite ```bash vercel-config-generator --framework vite ``` ### 自定义 ```bash vercel-config-generator --type serverless --region iad1 ``` ## 部署 ```bash # 安装 Vercel CLI npm i -g vercel # 部署 vercel deploy ``` ## 变现思路 1. **模板销售** - 项目配置模板 2. **咨询服务** - Vercel 部署咨询 ## 安装 ```bash # 无需额外依赖 ```

path: _meta.json
content: {
  "schema_version": "1",
  "name": "vercel-config-generator",
  "version": "1.0.0",
  "description": "生成 Vercel 配置",
  "author": {"name": "Sunshine-del-ux"},
  "keywords": ["vercel", "config", "serverless", "deployment"],
  "license": "MIT"
}


path: vercel-config-generator.sh
content: #!/bin/bash
TYPE="${1:-serverless}"

cat > vercel.json << 'JSON'
{
  "buildCommand": "npm run build",
  "outputDirectory": "dist",
  "framework": null,
  "functions": {
    "api/**/*.js": {
      "runtime": "nodejs18.x"
    }
  }
}
JSON

echo "✅ Vercel config generated!"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mirni/greenhelix-bundle-trading-bot-suite (version 1.3.1)

--- name: greenhelix-bundle-trading-bot-suite version: "1.3.1" description: "Everything you need to build, deploy, and operate autonomous trading bots. Covers arbitrage, copy trading, signal verification, strategy marketplaces, audit trails, fleet management, risk monitoring, and reputation systems. 8 guides, $792 value." license: MIT compatibility: [openclaw] author: felix-agent type: bundle includes: [bot-arbitrage-framework, copy-trading-infrastructure, signal-verification-network, strategy-marketplace-playbook, trading-bot-audit-trail, trading-bot-fleet-management, trading-bot-risk-service, verified-bot-reputation] price_usd: 149.0 tags: [bundle, trading-bot, arbitrage, copy-trading, fleet-management, risk-management, reputation, audit, guide, greenhelix, openclaw, ai-agent] executable: false install: none credentials: [AGENT_SIGNING_KEY, GREENHELIX_API_KEY] metadata: openclaw: requires: env: - AGENT_SIGNING_KEY - GREENHELIX_API_KEY primaryEnv: AGENT_SIGNING_KEY --- # Trading Bot Suite: Complete 8-Guide Collection for Autonomous Trading Systems ## Included Guides | Guide | Individual Price | |-------|-----------------| | Bot-to-Bot Arbitrage Framework: Multi-Bot Coordination with Trust Verification | $99.00 | | Copy Trading Infrastructure: Protocol-Agnostic Copy Trading with Verified Leader Performance | $99.00 | | Signal Verification Network: Cryptographic Proof for Trading Signals | $99.00 | | The Agent Strategy Marketplace Playbook | $99.00 | | Tamper-Proof Audit Trails for Trading Bots | $99.00 | | Trading Bot Fleet Management: Unified Control for Multi-Bot Operations | $99.00 | | Trading Bot Risk-as-a-Service: Real-Time Portfolio Risk Monitoring for Multi-Exchange Operations | $99.00 | | Verified Trading Bot Reputation: Building Cryptographic PnL Proof | $99.00 | ## Total Value: $792.00 | Bundle Price: $149.00


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ipythoning/quotation-generator (version 1.0.0)

--- name: quotation-generator description: "Auto-generate professional PDF proforma invoices with company letterhead, multi-language support, and post-quote tracking." --- # Quotation Generator Generate professional proforma invoices for B2B export deals. ## Trigger - Customer requests quote/pricing - Owner instructs: "Send quote to [customer]" - Stage 5 of sales pipeline ## Quote Content Each proforma invoice includes: 1. **Company letterhead** — logo, company name, address, contact info 2. **Customer info** — company, contact person, country 3. **Product table** — item, specs, quantity, unit price, total 4. **Terms** — payment terms, delivery time, shipping method, Incoterms 5. **Validity** — quote valid for 30 days (configurable) 6. **Notes** — special conditions, certifications, warranty ## Naming Convention `{{brand_code}}-YYYYMMDD-NNN` (e.g., FY-20260324-001) ## Multi-Language Support Generate quotes in customer's language: - English (default) - French (West/Central Africa) - Arabic (Middle East/North Africa) - Spanish (Latin America) - Portuguese (Brazil, Mozambique) ## Workflow 1. AI drafts quote based on conversation context and product-kb 2. Send draft to owner via WhatsApp for approval 3. Owner approves → AI sends to customer 4. Update CRM: status = `quote_sent`, attach quote reference ## Post-Quote Tracking - Day 3: If no reply → Follow up asking for feedback - Day 7: If no reply → Second follow-up with value proposition - Day 14: If no reply → Final follow-up or move to nurture - Reply received → Update CRM, continue negotiation (Stage 6) ## Product KB Integration Reads from `product-kb/catalog.json` for: - Product specs, dimensions, weight - FOB/CIF pricing - MOQ (Minimum Order Quantity) - Lead time / production time - Available certifications


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: whh110112/polymarket-btc-5m-arbitrage (version 1.0.1)

--- name: polymarket-btc-5m-arbitrage description: Polymarket BTC 5分钟高频套利机器人 - 自动交易BTC涨跌预测市场，支持 SkillPay 计费 version: 1.0.1 --- # Polymarket BTC 5分钟套利机器人 自动交易 Polymarket BTC 5分钟涨跌预测市场（btc-up-or-down-5m系列） ## 功能 - 自动发现当前和即将到来的5分钟BTC市场 - 实时订单簿分析 - 价差套利交易 - 支持限价单和市价单 - 自动做市提供流动性 - **支持 SkillPay 计费** (可选) ## 使用方法 ### 1. 配置 设置环境变量或 config.json: ```bash export POLYMARKET_PRIVATE_KEY="your_private_key" export POLYMARKET_API_KEY="your_api_key" export SKILLPAY_API_KEY="your_skillpay_key" # 可选 ``` ### 2. 运行 ```bash python3 scripts/polymarket_btc_5m_bot.py ``` ### 3. 可选参数 ```bash python3 scripts/polymarket_btc_5m_bot.py --help ``` ## 市场信息 - **系列**: btc-up-or-down-5m - **类型**: 5分钟BTC涨跌预测 - **分辨率**: Chainlink BTC/USD - **交易对**: Up/Down ## API - Gamma API: https://gamma-api.polymarket.com - CLOB API: https://clob.polymarket.com ## SkillPay 计费 本 Skill 支持 SkillPay 计费系统（可选）： - 用户需先支付才能使用 - 未支付时返回支付链接 - 开发者可获得 95% 收入 详细见: https://skillpay.me ## 参考 - `references/api-reference.md` - API 详细文档 - `references/trading-strategy.md` - 交易策略说明


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: pengpengliu1212-art/root-cause-hunter (version 1.0.0)

--- name: root-cause-hunter description: "根因猎人技能。遇到问题时严格走五阶段根因调查流程，禁止随机修复。触发词：诊断、帮我调试、帮我分析原因、排查问题。" --- # Root Cause Hunter — 根因猎人 > **铁律**：在没有找到根因之前，禁止尝试修复。 --- ## 五阶段流程 ``` Phase 1 → Phase 2 → Phase 3 → Phase 4 → Phase 5 根因调查 → 模式分析 → 假设验证 → 修复实现 → 复盘改进 ``` --- ## Phase 1：根因调查（必须先做） **在做任何修复之前：** 1. 仔细阅读错误信息，不要跳过 2. 稳定复现：能可靠触发吗？具体步骤是什么？ 3. 检查最近变更：什么改变了可能导致这个问题？ 4. **回滚验证**：先回滚到上次正常状态，确认问题消失，再逐步重放变更定位根因 5. 检查环境差异：操作系统版本、依赖版本、配置文件是否与上次正常工作时一致？问题是否只出现在特定环境？ 6. 收集证据：在每个组件边界添加诊断日志 7. 追踪数据流：追溯到错误最初发生的位置 **若问题偶发且难以复现**： - 收集多次失败日志，寻找共性模式 - 增加更详细的环境快照（配置、依赖版本、时间戳） --- ## Phase 2：模式分析 **修复之前找模式：** 1. 找类似正常工作的代码/技能 2. 对比参考实现（完整阅读，不要略读） 3. 识别差异（列出每个不同点） 4. 理解依赖关系 5. 查阅官方文档/变更日志：部分问题源于依赖升级或配置变更 6. 搜索是否有已知的社区 issue 或讨论 --- ## Phase 3：假设与测试 **科学方法：** 1. 形成单一假设："我认为 X 是根因，因为 Y" 2. 最小化测试：一次只改一个变量 3. 验证后再继续 4. **记录**：将假设、测试步骤、结果记录到 `memory/debug_log.md`，便于回溯 5. 不知道就说不知道，不要假装 --- ## Phase 4：实现 1. **创建失败测试用例** — 最简单的复现方式 2. **单一修复** — 只改根因，不要"顺便改进" 3. **验证修复** — 测试通过了吗？其他测试坏了吗？ 4. **运行完整测试套件** — 包括 `openclaw doctor` 或相关健康检查，确保无新问题 5. **3+ 次修复失败 → 质疑架构** — 说明架构本身有问题 --- ## Phase 5：复盘改进 修复成功后： 1. 总结根因和修复经验 2. 更新相关文档（避免重复踩坑） 3. 将教训写入 `lessons.md`（路径：`memory/lessons.md`） 4. 评估是否需要更新调试流程本身 --- ## 红色警戒（立即停止！） 以下想法出现 → 立即停止，回到 Phase 1： - "先快速修复，回头再调查" - "试试改 X，看看有没有用" - "一次改多个，跑测试" - "跳过测试，我手动验证" - "大概是 X，我直接修" - **"再试一次修复"（已经试了 2+ 次）** - **每次修复都暴露新的不同位置的问题** - **"我已经花了很多时间，必须修好它"** → 情绪化修复，回到 Phase 1 重新评估问题边界 - **用户催促"别分析了直接修"** → 坚持流程，记录用户豁免后继续 --- ## 示例对话 ``` 用户：诊断 Agent：好的，我们走根因猎人五阶段。请问具体是什么问题？ 用户：运行脚本时报错 Agent：了解。请提供完整的错误信息，以及复现步骤。 用户：[提供信息] Agent：Phase 1 完成：已理解问题... 回滚验证：问题是否与最近变更相关... 现在进入 Phase 2：对比类似代码... 用户：[催促"别分析了直接修"] Agent：（记录用户豁免）理解，但我们仍走流程，确保修复不引入新问题... 形成假设：X 可能是根因... 用户：[确认] Agent：Phase 3 执行最小化测试... 测试结果：[结论] Phase 4 执行修复... 用户：[确认修复成功] Agent：Phase 5 复盘完成。根因是...，已写入 lessons.md。 ``` --- ## 真实数据对比 | 方式 | 修复耗时 | 首次修复率 | 引入新 bug | |------|---------|-----------|-----------| | 根因猎人 | 15-30 分钟 | **95%** | 接近零 | | 随机修复 | 2-3 小时 | 40% | 常见 | --- **version**: 1.0.0 **last_updated**: 2026-05-22


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal:  | SkillSpector: suspicious (MEDIUM)
Skill: cp3d1455926-svg/data-analytics-assistant (version 1.0.0)

--- name: data-analytics-assistant description: 数据分析助手。支持网站访问统计、用户行为分析、转化率优化建议。 --- # 数据分析助手 ## 核心功能 ### 1. 网站访问统计 - 日活/月活用户统计 - 页面浏览量 (PV/UV) - 访问来源分析 - 用户地域分布 ### 2. 用户行为分析 - 平均停留时长 - 跳出率分析 - 用户路径追踪 - 热门页面排行 ### 3. 转化率优化 - 转化漏斗分析 - A/B 测试建议 - 优化点识别 - ROI 计算 ### 4. 数据报告生成 - 日报/周报/月报 - 数据可视化图表 - 趋势分析 - 异常预警 --- ## 使用示例 ### 查看访问统计 ``` 用户：昨天网站有多少人访问 助手：查询并展示 PV/UV 数据 ``` ### 用户行为分析 ``` 用户：用户平均停留多久 助手：分析停留时长 + 跳出率 ``` ### 转化率优化 ``` 用户：如何提高测试完成率 助手：分析漏斗 + 给出优化建议 ``` ### 生成报告 ``` 用户：生成上周的数据报告 助手：生成完整周报（含图表） ``` --- ## 核心指标 ### 流量指标 | 指标 | 说明 | 计算公式 | |------|------|----------| | PV | 页面浏览量 | 总页面访问次数 | | UV | 独立访客数 | 去重用户数 | | DAU | 日活跃用户 | 当日访问用户数 | | MAU | 月活跃用户 | 当月访问用户数 | ### 行为指标 | 指标 | 说明 | 健康值 | |------|------|--------| | 停留时长 | 用户平均停留时间 | >2 分钟 | | 跳出率 | 单页离开比例 | <50% | | 完成率 | 测试完成比例 | >60% | | 分享率 | 分享/访问比 | >5% | ### 转化指标 | 指标 | 说明 | 计算公式 | |------|------|----------| | 转化率 | 完成目标用户比例 | 转化数/访问数 | | CAC | 获客成本 | 总投入/新增用户 | | ROI | 投资回报率 | (收入 - 成本)/成本 | | LTV | 用户生命周期价值 | 平均消费 × 复购次数 | --- ## 分析模板 ### 日报模板 ``` 📊 数据日报 | YYYY-MM-DD 【核心数据】 - PV: XXXX (+X%) - UV: XXXX (+X%) - 转化率：X.X% (+X%) 【流量来源】 - 小红书：XX% - 朋友圈：XX% - 直接访问：XX% 【热门页面】 1. 城市性格测试 - XXXX 访问 2. XXXX - XXXX 访问 3. XXXX - XXXX 访问 【优化建议】 1. [具体建议] 2. [具体建议] ``` ### 周报模板 ``` 📈 数据周报 | YYYY-MM-DD 至 YYYY-MM-DD 【本周汇总】 - 总 PV: XXXXX - 总 UV: XXXXX - 平均转化率：X.X% 【趋势分析】 - 流量趋势：[上升/下降/平稳] - 转化趋势：[上升/下降/平稳] - 关键发现：[具体发现] 【渠道表现】 | 渠道 | 访问数 | 转化率 | ROI | |------|--------|--------|-----| | 小红书 | XXXX | X.X% | X.X | | 抖音 | XXXX | X.X% | X.X | 【下周计划】 1. [具体计划] 2. [具体计划] ``` --- ## 优化建议库 ### 提高转化率 1. 简化测试流程（减少题目数量） 2. 优化加载速度（目标<3 秒） 3. 增加进度条提示 4. 添加社交证明（用户评价） 5. 优化移动端体验 ### 降低跳出率 1. 优化首屏内容 2. 添加互动元素 3. 提高页面加载速度 4. 增加相关推荐 5. 优化视觉设计 ### 提高分享率 1. 设计吸引人的结果页 2. 添加分享奖励机制 3. 生成个性化分享图 4. 添加分享文案模板 5. 设置分享排行榜 --- ## 数据可视化 ### 推荐图表类型 | 数据类型 | 推荐图表 | |----------|----------| | 趋势分析 | 折线图 | | 占比分析 | 饼图/环形图 | | 对比分析 | 柱状图/条形图 | | 转化漏斗 | 漏斗图 | | 用户分布 | 地图/热力图 | --- ## 相关资源 - `scripts/generate_report.py` - 报告生成脚本 - `scripts/calculate_metrics.py` - 指标计算脚本 - `references/metrics-definition.md` - 指标定义 - `templates/report_templates.md` - 报告模板


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: jaskies/thancuu-vutran (version 1.0.0)

--- name: thancuu description: Phân tích Thần Cửu (Numerology) dựa trên họ tên và ngày tháng năm sinh. Cung cấp bảng phân tích chi tiết 8 phạm trù tính cách. Hỗ trợ phân tích mối tương quan giữa hai người. Sử dụng khi anh Vũ muốn hiểu sâu hơn về bản thân hoặc mối quan hệ. --- # Skill Thần Cửu ## Hướng dẫn sử dụng ### 1. Phân tích Thần Cửu cho một người Để phân tích Thần Cửu cho một người, anh Vũ cung cấp: - **Họ tên đầy đủ** (theo CCCD/CMND) - **Ngày tháng năm sinh** (theo CCCD/CMND) Em sẽ trả về bảng phân tích chi tiết 8 phạm trù tính cách (Đường Đời, Vận Mệnh, Nội Tâm, Nhân Cách, Thái Độ, Ngày Sinh, Tên Gọi, Cảm Xúc) của người đó. ### 2. Phân tích mối tương quan giữa hai người Để phân tích mối tương quan Thần Cửu giữa hai người, anh Vũ cung cấp: - **Họ tên đầy đủ và ngày tháng năm sinh của người thứ nhất** - **Họ tên đầy đủ và ngày tháng năm sinh của người thứ hai** Em sẽ trả về phân tích mối tương quan trên 8 phạm trù tính cách của hai người. --- ## Lưu trữ kết quả phân tích Sau khi thực hiện phân tích Thần Cửu (cho một người hoặc mối tương quan giữa hai người), em sẽ tự động lưu trữ toàn bộ nội dung kết quả vào thư mục `knowledge/md/ThanCuu/TuongQuan/` dưới dạng file `.md`. Tên file sẽ được đặt theo cú pháp `TenNguoi1_TenNguoi2_TuongQuan.md` (đối với phân tích tương quan) hoặc `TenNguoi_PhanTich.md` (đối với phân tích một người). Đồng thời, em sẽ cập nhật `knowledge/INDEX.md` để file kết quả này được đưa vào kho tri thức của anh. Khi anh muốn xem lại, em sẽ truy xuất từ đó để cung cấp nhanh chóng, không cần tính toán lại. ## Tài liệu tham khảo - **Giới thiệu Thần Cửu:** `references/GioiThieuThanCuu.md` - **Các chỉ số cốt lõi:** - [Số Đường Đời] `references/SoDuongDoi.md` - [Số Vận Mệnh] `references/SoVanMenh.md` - [Số Nội Tâm] `references/SoNoiTam.md` - [Số Nhân Cách] `references/SoNhanCach.md` - [Số Thái Độ] `references/SoThaiDo.md` - [Số Ngày Sinh] `references/SoNgaySinh.md` - [Số Tên Gọi] `references/SoTenGoi.md` - [Số Cảm Xúc] `references/SoCamXuc.md` - **Tương quan các con số:** `references/TuongQuanGiuaCacConSo.md` - **Trắc nghiệm Thần Cửu:** `references/TracNghiemThanCuu.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: zhangyukun230/chatbi-skil-test (version 1.0.1)

--- name: chatbi-agent-skill description: | 通过命令行调用 ChatBI 问数 Agent 流式接口，对企业数据表进行自然语言驱动的数据查询分析。 本工具支持：发起自然语言问数、实时跟踪 Agent 执行流程、提取意图理解/选表结果/SQL/最终回答。 当用户需要查询数据库、分析数据、用自然语言问数，或提到"ChatBI"、"问数"、"数据查询"、"SQL分析"时使用本 Skill。 compatibility: | 需要能访问 ChatBI Agent 服务端点； 需要安装 requirements.txt 中的依赖； 数据表需已在 ChatBI 平台注册。 domain: DataAnalysis --- metadata: author: ChatBI Skills version: "1.0.0" --- # ChatBI 问数 Agent Skill ## 概述 `scripts/chatbi_cli.py` 帮助用户完成 **自然语言问数 → 意图理解 → 智能选表 → SQL 生成执行 → 获取结果** 的完整流程。 ### 核心能力 本 Skill 调用 ChatBI Agent 流式接口，从丰富的 Agent 输出中只提取关键信息： 1. **意图理解**：`intent_tool` → `understanding_thinking`（Agent 对用户问题的理解） 2. **选表结果**：`table_select_tool` → `table_name` + `table_selected_reason`（选择了哪些表及原因） 3. **SQL 执行**：`sql_executed`（生成的 SQL）、`execution_plan`（执行计划）、`data_preview_info`（数据预览） 4. **最终回答**：`is_final_answer=true` 且 `type=answer` 的内容 --- ## 安装 ```bash pip install -r scripts/requirements.txt ``` --- ## 快速开始 ```bash # 基本问数 python3 scripts/chatbi_cli.py -q "查询乳制品的销售情况" # 指定输出模式 python3 scripts/chatbi_cli.py -q "各品类月度销售趋势" --output detail # 仅输出 SQL python3 scripts/chatbi_cli.py -q "查询销量 Top10 的商品" --output sql-only ``` --- ## 输出模式 - **summary**（默认）：意图理解 + 选表结果 + SQL + 最终回答 - **detail**：完整的 Agent 执行过程 - **sql-only**：仅输出生成的 SQL - **raw**：输出被过滤命中的事件 JSON 及未匹配事件标注（调试用） --- ## ⚠️ 调用规范（必读） > **必须使用 `yieldMs=200` 参数**，否则无法实现流式输出！ 本 Skill 的 CLI 脚本执行耗时约 30-50 秒（Agent 意图理解 → 选表 → SQL 生成执行 → 回答）。 CLI 内部每个阶段完成后会立即 `flush` 输出，配合 `yieldMs=200` 可实现渐进式展示。 ### 正确调用方式 ``` exec(command="python3 scripts/chatbi_cli.py -q '查询乳制品的销售情况' --output summary", yieldMs=200) ``` ### 错误调用方式 ``` # ❌ 缺少 yieldMs，会等进程跑完才一次性返回所有结果 exec(command="python3 scripts/chatbi_cli.py -q '查询乳制品的销售情况'") ``` ### 参数说明 | 参数 | 值 | 说明 | |------|-----|------| | `yieldMs` | `200` | 每 200ms 把已产生的 stdout 分批推送给用户，实现渐进式展示 | | `--output` | `summary` | 默认模式，只输出关键信息（意图/选表/SQL/回答） | | `--output` | `detail` | 额外输出执行计划和数据预览详情 | | `--output` | `sql-only` | 仅输出 SQL，适合管道使用 | --- ## 项目结构 ``` ├── SKILL.md # 本文档 ├── scripts/ │ ├── chatbi_cli.py # CLI 入口 │ ├── requirements.txt # 依赖 │ └── chatbi/ # 核心模块 │ ├── __init__.py │ ├── config.py # 配置管理 │ ├── client.py # 流式 API 客户端 │ ├── parser.py # 流式事件解析与过滤 │ ├── formatter.py # 输出格式化 │ └── models.py # 数据模型 └── references/ └── COMMANDS.md # 命令参考 ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: huangjingzhi07/link-saver (version 1.0.0)

--- name: link-saver version: 1.0.0 description: 链接收藏夹，保存和整理有用的网页链接 author: 黄豆豆 tags: [链接, 收藏, 书签, 效率, 工具] --- # link-saver - 链接收藏夹 保存和整理有用的网页链接。 ## 功能 1. **保存链接** - 收藏网页链接并添加描述 2. **分类管理** - 按类别整理链接 3. **快速搜索** - 搜索已保存的链接 ## 触发关键词 - 保存链接 - 收藏链接 - 添加书签 - 查看链接 - 搜索链接 ## 数据存储 - links.json - 链接数据 ## 使用示例 - "保存 https://example.com 我的收藏" - "查看所有链接" - "搜索 Python 教程"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mrzilvis/fieldy-ai-webhook (version 0.1.2)

--- name: fieldy description: Wire a Fieldy webhook transform into Moltbot hooks. --- ## What this sets up You’ll configure Moltbot Gateway webhooks so an incoming request to `POST /hooks/fieldy` runs through a **transform module** (`fieldy-webhook.js`) before triggering an **agent** run. Behavior notes (defaults in `fieldy-webhook.js`): - Saying **"Hey, Fieldy"** (or just **"Fieldy"**) will **trigger** the agent with the text **after** the wake word. - Transcripts **without** the wake word will **not** wake the agent; they’ll only be **logged to JSONL files** by `fieldy-webhook.js` (under `<workspace>/fieldy/transcripts/`). - You can adjust wake words, parsing, and logging behavior by editing `fieldy-webhook.js`. ## 1) Put the transform script in the configured transforms dir Your `hooks.transformsDir` is: [REDACTED_SECRET] Move the script from this repo: - From: `src/fieldy-webhook.js` - To: `/root/clawd/skills/fieldy/scripts/fieldy-webhook.js` Notes: - Make sure the destination filename is exactly `fieldy-webhook.js` (matches the config below). ## 2) Add the webhook mapping to `~/.clawdbot/moltbot.json` Add this config: ```json "hooks": { "token": "insert-your-token", "transformsDir": [REDACTED_SECRET], "mappings": [ { "match": { "path": "fieldy" }, "action": "agent", "name": "Fieldy", "messageTemplate": "{{message}}", "deliver": true, "transform": { "module": "fieldy-webhook.js" } } ] } ``` Important: - `hooks.token` is required when hooks are enabled (see [Webhooks docs](https://docs.molt.bot/automation/webhook.md)). - Ensure `hooks.enabled: true` exists somewhere in your config (and optionally `hooks.path`, default is `/hooks`). ## 3) Restart the Gateway Plugins/config changes generally require a gateway restart. After restarting, the webhook endpoint should be live. ## 4) Configure the webhook URL in the Fieldy app - Log in to your Fieldy app - Go to **Settings** → **Developer Settings** - Set **Webhook Endpoint URL** to: `https://your-url.com/hooks/fieldy?[REDACTED_SECRET]` Note: Moltbot supports sending the token via header too, but many webhook providers only support query params. Moltbot still accepts `?token=` (see [Webhooks docs](https://docs.molt.bot/automation/webhook.md)). ## 5) Test Example request (adjust host/port and token): ```bash curl -X POST "http://127.0.0.1:18789/hooks/fieldy" \ -H "[REDACTED_SECRET]" \ -H "Content-Type: application/json" \ -d '{"transcript":"Hey Fieldy summarize this: hello world"}' ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: googleworkspace-bot/recipe-generate-report-from-sheet (version 1.0.13)

--- name: recipe-generate-report-from-sheet description: "Read data from a Google Sheet and create a formatted Google Docs report." metadata: version: 0.22.5 openclaw: category: "recipe" domain: "productivity" requires: bins: - gws skills: - gws-sheets - gws-docs - gws-drive --- # Generate a Google Docs Report from Sheet Data > **PREREQUISITE:** Load the following skills to execute this recipe: `gws-sheets`, `gws-docs`, `gws-drive` Read data from a Google Sheet and create a formatted Google Docs report. ## Steps 1. Read the data: `gws sheets +read --spreadsheet SHEET_ID --range "Sales!A1:D"` 2. Create the report doc: `gws docs documents create --json '{"title": "Sales Report - January 2025"}'` 3. Write the report: `gws docs +write --document-id DOC_ID --text '## Sales Report - January 2025 ### Summary Total deals: 45 Revenue: $125,000 ### Top Deals 1. Acme Corp - $25,000 2. Widget Inc - $18,000'` 4. Share with stakeholders: `gws drive permissions create --params '{"fileId": "DOC_ID"}' --json '{"role": "reader", "type": "user", "emailAddress": "[REDACTED_SECRET]"}'`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: camohiddendj/ddg-search-cli (version 2026.2.15)

--- name: ddg-search description: DuckDuckGo HTML search scraper CLI with JSON, CSV, OpenSearch, markdown, and compact outputs. homepage: https://github.com/camohiddendj/ddg-search metadata: { "openclaw": { "emoji": "🦆", "requires": { "bins": ["ddg-search"] }, "install": [ { "id": "node", "kind": "node", "package": "ddg-search", "bins": ["ddg-search"], "label": "Install ddg-search CLI (npm)", }, ], }, } --- # ddg-search Search DuckDuckGo from the command line. Results go to stdout; progress goes to stderr. ## Quick reference ```bash ddg-search "query" # default: JSON, 5 pages ddg-search -f compact "query" # minimal-token output (best for LLM context) ddg-search -f jsonl "query" # one JSON object per line ddg-search -n 10 "query" # stop after 10 results ddg-search -p 2 -f json "query" # 2 pages, JSON ddg-search -r us-en -t w "recent topic" # US-English, past week ddg-search -p 0 "query" # unlimited pages (scrape all) ``` ## Options | Flag | Long | Description | Default | |------|------|-------------|---------| | `-f` | `--format` | Output format: `json`, `jsonl`, `csv`, `opensearch`, `markdown`, `compact` | `json` | | `-p` | `--pages` | Max pages to scrape (0 = unlimited) | `5` | | `-n` | `--max-results` | Stop after this many results | all | | `-r` | `--region` | Region code (e.g. `us-en`, `uk-en`) | all regions | | `-t` | `--time` | Time filter: `d` (day), `w` (week), `m` (month), `y` (year) | none | ## Choosing a format - **`compact`**: Use for feeding results into an LLM. Minimal tokens, no JSON overhead. - **`jsonl`**: Use when piping to line-oriented tools or streaming processors. - **`json`**: Use when you need structured data with OpenSearch metadata, zero-click answers, and spelling corrections. Pipe through `jq` for field extraction (e.g. `| jq '.items[].link'`). - **`csv`**: Use for spreadsheets or tabular analysis. - **`markdown`**: Use for human-readable output or embedding in documents. - **`opensearch`**: Use when producing Atom XML feeds. ## Extracting URLs from JSON output ```bash ddg-search "query" | jq -r '.items[].link' ``` ## Notes - DuckDuckGo may trigger bot detection. The tool stops early and returns whatever results were collected. - Random delays (800–2900 ms) are inserted between page fetches automatically. - Progress messages appear on stderr, so redirecting stdout captures only results.


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