OpenClaw/clawhub-security-signals huggingface dataset:
Skill: wellyxy/growth-email (version 1.0.0)

# Email Marketing for SaaS Onboarding ## When to Use Use this skill when building or improving email sequences for new user activation and retention. ## Core Rules ### 1. Send the Welcome Email Immediately The welcome email sent within 5 minutes of sign-up has 4× higher open rates than emails sent an hour later. Send it while the sign-up decision is fresh. Include: what to do first, a single action to take, and how to get help. ### 2. Trigger Emails on Behavior, Not Time "Day 3 of your trial" emails feel irrelevant. "You haven't connected your first data source yet" emails feel personal. Behavioral triggers — sent when a user does or doesn't complete a key action — consistently outperform time-based sequences in open and conversion rates. ### 3. Write Like a Person, Not a Marketing Team SaaS emails that read like corporate marketing are ignored. Emails from a named founder or team member, written conversationally, with a genuine subject line ("Quick question") dramatically outperform designed, branded emails for early-stage products. ### 4. One Email, One Goal Every email should have one purpose: get the user to complete one specific action. An email asking users to "connect your data source, invite your team, and check out the docs" will result in none of those actions being taken. ### 5. A/B Test Subject Lines First Subject line determines whether the email is opened. Open rate is the gating metric for everything else. Test subject lines before testing body copy. Short, specific, question-based, and "from" personalization are the variables with the most consistent impact. ## Quick Reference | Email Type | Trigger | Goal | |-----------|---------|------| | Welcome | Sign-up | First action | | Activation | Day 1, no key action | Complete activation step | | Feature discovery | 3 days active, no feature use | Feature adoption | | Trial ending | 3 days before expiry | Upgrade prompt | | Win-back | 7 days inactive | Re-engagement | ## Common Mistakes to Avoid - Sending all emails from [REDACTED_SECRET] — reduces reply rate and deliverability - Using heavy HTML templates for personal-feeling emails — plain text works better - Emailing too frequently in the first week — respect the onboarding experience ## Test Your Product with Racoonn After applying these practices, validate with real AI-simulated user testing. **Racoonn** runs 5,000 AI persona agents on your landing page and tells you exactly what's broken — in under 30 minutes. → **API coming soon** — Join the waitlist for early access: [racoonn.me](https://racoonn.me)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: l18784175468-oss/gigohotel (version 1.0.10)

--- slug: aigo-hotel name: AIGo Hotel Search version: 1.0.10 description: 智能酒店搜索，支持地点、日期、星级、预算筛选 author: your-name tags: - hotel - travel - booking --- # AIGo Hotel Search ## Description 基于 AIGoHotel MCP 的智能酒店搜索技能，开箱即用。 ## Credentials 本技能使用 AIGoHotel 官方提供的公共 API Key，无需用户配置。 ## Public API Key Declaration - 内置 Key ([REDACTED_SECRET]) 是 AIGoHotel 官方提供的公共访问密钥 - 此 Key 专为社区开发者设计，非机密凭证 - 公共 Key 有速率限制，如需更高配额请申请专属 Key - 申请地址: https://mcp.aigohotel.cn/apply - 验证方式: 可通过 https://mcp.aigohotel.cn 确认 Key 状态 ## MCP Configuration ```json { "mcpServers": { "aigohotel-mcp": { "url": "https://mcp.aigohotel.com/mcp", "type": "http", "headers": { "Authorization": "Bearer mcp_7d31559a07884aa99be1fa22cdbe828a" } } } } ``` ## Data Transmission Policy ### 允许传输 仅限酒店搜索结构化参数： - 地点、日期、人数、星级、预算、标签 ### 禁止传输 - 用户个人信息（姓名、电话、邮箱） - 本地文件、系统信息 - 无关的自由文本内容 ### originQuery 处理规则 originQuery 参数仅应包含酒店搜索意图，代理必须： - 提取搜索相关信息（地点、日期、条件） - 移除任何个人身份信息 (PII) - 不得直接传递用户原始输入 代理调用工具前应过滤敏感信息。 ### 安全责任声明 本技能为指令型技能，不包含可执行代码。数据过滤由以下层级负责： 1. **代理运行时**：负责执行 PII 过滤指令 2. **MCP 服务端**：对请求进行安全校验 3. **用户**：避免在查询中输入敏感个人信息 本技能已尽合理告知义务，实际过滤执行由代理平台保障。 ## Tools - `searchHotels`: 搜索酒店 - `getHotelDetail`: 获取房型价格 - `getHotelSearchTags`: 获取筛选标签 ## Usage Examples - "找北京五星酒店" - "查看北京天伦王朝酒店的房型和价格" - "上海1000元以内有泳池的酒店"


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal:  | SkillSpector:  ()
Skill: bryko/onshape-featurescript (version 1.0.0)

--- name: onshape-featurescript description">Generate Onshape FeatureScript code for custom CAD features and parametric parts. Specializes in dust collection fittings library (wyes, elbows, adapters, crosses). Use when user asks to: (1) create FS features (gears, fittings, brushes), (2) modify existing FS, (3) debug FS errors, (4) build libraries with consistent patterns (enums, shells, labels). Patterns for Z-axis fittings, wrapped labels, safe unions/shells. Triggers: 'Onshape FS for X', 'FeatureScript wye/elbow', 'dust fitting'.</description> --- # Onshape FeatureScript Generator ## Quick Start Analyze request → Match pattern → Gen FS code → Validate logic. **Core Workflow**: 1. **Precondition**: Enums/lengths/reals w/ bounds. 2. **Body**: Sketches → Extrudes → Booleans → Shell → Wrap/Thicken labels. 3. **Pitfalls**: qOwnedByBody(finalBody), if/else (no ternary lengths), cylinder(coordSystem). ## Key Patterns (Memorize) - **Enum + Cond Param**: ``` export enum AlignmentStyle { Centered, Tangent, Offset }; if (definition.alignment == AlignmentStyle.Offset) { isLength(def.offsetDistance...); } ``` - **Safe Shell Caps**: ``` var capFaces = qFacesParallelToDirection(qOwnedByBody(finalBody, EntityType.FACE), vector(0,1,0)); ``` - **Union Primary First**: ``` qUnion([qCreatedBy(id+"inlet", EntityType.BODY), ...]); ``` - **Wrapped Label**: skText → opWrap → opThicken. ## Dust Fittings Lib Z-axis: Inlet Z=0 (-Z), Outlet Z=trans (+Z), Branch +X. See [FITTINGS.md](references/fittings.md) for full spec/pitfalls. ## Resources - **API Patterns**: [PATTERNS.md](references/patterns.md) - **Fonts**: AllertaStencil-Regular.ttf for labels. - **Validate**: Test mentally; no runtime here. Gen code → User pastes to Onshape FS editor.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dinghaibin/wc-tool (version 1.0.0)

--- name: wc-tool description: Count lines, words, characters, and bytes in one or more files with per-file and total summary. Use for document statistics, code analysis, and data volume estimation. --- # WC Tool — Word, Line & Character Counter Multi-file word count utility. Count lines, words, characters (multibyte-aware), and bytes across one or more files with per-file breakdown and grand total. Supports standard input for pipeline use. ## Quick Start ```bash # Count everything for a file wc-tool document.txt # Count lines only wc-tool -l script.py # Count from stdin cat file.txt | wc-tool -w ``` ## Usage ```bash wc-tool [OPTIONS] [FILE...] cat INPUT | wc-tool [OPTIONS] Options: -l, --lines Count lines only -w, --words Count words only -c, --bytes Count bytes only -m, --chars Count characters (multibyte-aware) -L, --max-line Print length of longest line --files0-from FILE Read input file list from FILE (null-separated) --total Show grand total (default with multiple files) --json Output as structured JSON ``` ## Examples ```bash # Full count (lines, words, chars, bytes) wc-tool README.md # Lines only (code file size metric) wc-tool -l src/main.py src/utils.py # Characters in a file (UTF-8 aware) wc-tool -m document.txt # Longest line length wc-tool -L src/main.py # Pipe from another command ls /usr/bin | wc-tool -l # JSON output for automation wc-tool -l -w *.py --json # Multiple files with total wc-tool *.txt ``` ## Features - **4 modes** — lines, words, characters (multibyte), bytes - **Multi-file** — process many files, show per-file + total - **Stdin support** — pipe input from other commands - **Longest line** — detect max line length - **JSON output** — structured for scripts - **Null-separated file list** — safe file name handling - **Exit code 0/1** — script-friendly


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yang1002378395-cmyk/reading-list (version 1.0.0)

# Reading List Skill 📚 智能阅读清单管理，高效学习，追踪阅读进度，智能推荐。 ## 功能特性 ### 核心功能 - **阅读清单管理** - 添加、删除、归档文章 - **阅读进度追踪** - 记录阅读状态和时间 - **智能推荐** - 根据兴趣推荐相关内容 - **笔记整理** - 自动提取摘要和笔记 - **阅读统计** - 可视化阅读数据 ### 高级功能 - **多来源支持** - URL、PDF、Markdown - **标签分类** - 按主题/优先级分类 - **阅读提醒** - 定时提醒阅读 - **分享功能** - 分享阅读清单 - **导出功能** - 导出到 Notion/Obsidian ## 使用方式 ### 添加文章 ``` 添加文章到阅读清单：https://example.com/article ``` ### 批量添加 ``` 添加以下文章到阅读清单： - https://blog.com/post1 - https://blog.com/post2 - https://blog.com/post3 ``` ### 查看清单 ``` 查看我的阅读清单 ``` ### 更新进度 ``` 标记《OpenClaw 完全指南》已读完 ``` ### 总结文章 ``` 总结这篇文章的要点：https://example.com/article ``` ### 智能推荐 ``` 推荐一些关于 AI 编程的文章 ``` ## 输出格式 ### 阅读清单 ``` 📚 阅读清单 ## 🔴 高优先级（5 篇） 1. [ ] OpenClaw Skills 开发指南 - 预计 15 分钟 2. [ ] React Server Components 深入 - 预计 20 分钟 3. [ ] AI Agent 工作流最佳实践 - 预计 25 分钟 ## 🟡 待读（15 篇） 1. [ ] TypeScript 5.0 新特性 - 预计 10 分钟 2. [ ] Node.js 性能优化技巧 - 预计 15 分钟 3. [ ] Docker 容器化部署实战 - 预计 20 分钟 ## 🟢 进行中（3 篇） 1. [⏳ 60%] 深入理解 TypeScript 2. [⏳ 30%] Node.js 性能优化 3. [⏳ 10%] Docker 实战指南 ## ✅ 已完成（28 篇） 1. [✅] JavaScript 设计模式 - 已读 3 天前 2. [✅] Git 工作流详解 - 已读 5 天前 3. [✅] HTTP 协议完全指南 - 已读 1 周前 ## 📊 阅读统计 - 本周阅读：8 篇 - 本月阅读：28 篇 - 总阅读时长：12 小时 - 完成率：65% ``` ### 文章摘要 ``` 📄 文章摘要：OpenClaw 完全指南 ## 核心要点 1. OpenClaw 是开源 AI Agent 平台 2. 支持多种通讯渠道（Telegram/WhatsApp/微信） 3. Skills 系统让 Agent 拥有自定义能力 4. 心跳机制实现自动化任务 ## 关键概念 - Agent：AI 助手实例 - Skill：可扩展的能力模块 - Heartbeat：定时任务机制 - Memory：持久化记忆系统 ## 推荐阅读时长 预计 15 分钟 ## 相关文章 - OpenClaw Skills 开发指南 - AI Agent 自动化工作流 ``` ## 使用场景 1. **技术学习** - 管理技术文章阅读清单 2. **行业研究** - 收集行业报告和资讯 3. **内容创作** - 整理素材和参考文章 4. **知识管理** - 构建个人知识库 5. **团队分享** - 分享团队阅读清单 ## 最佳实践 - 使用标签分类文章（如 `#frontend` `#ai` `#business`） - 设置优先级，先读重要文章 - 定期清理过期文章 - 阅读后及时记录笔记 - 每周回顾阅读统计 ## 数据存储 阅读清单存储在 `~/.openclaw/workspace/memory/reading-list.json` --- 创建时间：2026-03-11 作者：ClawMart 版本：1.0.0


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhfqqqq1234/niuniu-quant (version 1.0.0)

# 牛牛量化技能 ## 技能描述 专业的股票量化交易技能，提供股票数据分析、策略回测、智能选股、交易信号等功能。 ## 功能列表 ### 1. 股票数据查询 - 实时股价查询 - K 线数据获取 - 财务数据分析 - 技术指标计算 ### 2. 量化策略 - 均线策略 - MACD 策略 - KDJ 策略 - 布林带策略 - 自定义策略 ### 3. 智能选股 - 技术面选股 - 基本面选股 - 资金流选股 - 热点题材选股 ### 4. 交易信号 - 买入信号 - 卖出信号 - 持仓建议 - 风险提示 ## 使用方式 ### 命令行调用 ```bash # 查询股票信息 nn-quant stock 600519 # 运行策略回测 nn-quant backtest --strategy ma --stock 600519 # 智能选股 nn-quant select --type technical # 获取交易信号 nn-quant signal 600519 ``` ### API 调用 ```python import requests # 查询股票 response = requests.get('http://localhost:5000/api/stock/600519') # 运行策略 response = requests.post('http://localhost:5000/api/strategy/run', json={ 'strategy': 'ma', 'stock': '600519' }) # 智能选股 response = requests.get('http://localhost:5000/api/select/technical') ``` ## 配置说明 ### 环境变量 ```bash export NN_QUANT_API_KEY=your_api_key export NN_QUANT_DATA_SOURCE=tushare export NN_QUANT_CACHE_DIR=~/.nn-quant/cache ``` ### 配置文件 ```yaml # ~/.nn-quant/config.yaml data_source: tushare [REDACTED_SECRET] cache_enabled: true log_level: INFO ``` ## 依赖安装 ```bash pip install niuniu-quant pip install tushare pip install pandas pip install ta-lib ``` ## 示例 ### 查询贵州茅台 ```bash nn-quant stock 600519 ``` 输出： ``` 贵州茅台 (600519) 当前价格：1750.00 元 涨跌幅：+2.5% 成交量：123456 手 成交额：21.6 亿 52 周最高：1900.00 52 周最低：1500.00 ``` ### 均线策略回测 ```bash nn-quant backtest --strategy ma --stock 600519 --days 250 ``` 输出： ``` 策略：均线策略 股票：贵州茅台 (600519) 回测周期：250 天 初始资金：100000 最终收益：125000 收益率：25% 最大回撤：-8.5% 夏普比率：1.85 ``` ## 风险提示 ⚠️ 量化交易有风险，投资需谨慎！ - 历史业绩不代表未来收益 - 策略可能失效 - 市场风险不可控 - 请合理配置资金 ## 技术支持 - 文档：https://niuniu-quant.readthedocs.io - 问题反馈：https://github.com/niuniu-quant/issues - 交流群：QQ 群 123456789 ## 更新日志 ### v1.0.0 - 初始版本发布 - 支持基础股票查询 - 支持均线策略 - 支持智能选股 ### v1.1.0 - 新增 MACD 策略 - 新增 KDJ 策略 - 优化选股算法 - 提升回测速度


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: bigsan/openclaw-updater (version 1.6.0)

--- name: openclaw-updater description: Safely update OpenClaw with pre-flight checks and rollback support. Use when updating OpenClaw, checking for updates, or recovering from a failed update. Handles workspace git commits, config backups, version rollback, and post-update verification. --- # OpenClaw Updater Safely update OpenClaw with automatic pre-flight safety checks and rollback capability. ## Pre-Update Checklist Before running `openclaw update`, always run the pre-update script: ```bash bash scripts/pre-update.sh ``` Optional: specify a backup script to run during pre-flight: ```bash BACKUP_SCRIPT=~/repo/scripts/backup-openclaw.sh bash scripts/pre-update.sh ``` The script will: 1. Find all `workspace*` directories under `~/.openclaw/` 2. Git commit any uncommitted changes (init git if missing) 3. Back up `openclaw.json` to `/tmp/openclaw.json.bak` 4. Run the backup script if provided 5. Save the current version to `/tmp/openclaw-prev-version.txt` ## Telegram Notification Setup The update script sends success/failure notifications via Telegram Bot API (bypasses OpenClaw gateway, so it works even if the update breaks the gateway). Create `~/.openclaw/.telegram-notify.env`: ``` TELEGRAM_BOT_TOKEN=<your-bot-token> TELEGRAM_CHAT_ID=<your-chat-id> ``` ```bash chmod 600 ~/.openclaw/.telegram-notify.env ``` The bot token is the same one used by your OpenClaw Telegram channel. Chat ID can be found via `openclaw directory`. ## Update (with notification) Run the full update with automatic pre-flight + notification: ```bash bash scripts/update.sh ``` This will: pre-flight → update → wait for gateway → notify via Telegram. ## Update (manual) After pre-flight passes: ```bash openclaw update ``` ## Post-Update Verification After updating, verify: ```bash openclaw status openclaw gateway status ``` Check that: - Version shows the new release - Gateway is running - All agents are responsive ## Rollback If the update breaks something: ```bash bash scripts/rollback.sh ``` The script will: 1. Read the saved previous version from `/tmp/openclaw-prev-version.txt` 2. Install that version via `npm install -g openclaw@<version>` 3. Optionally restore `openclaw.json` from backup 4. Restart the gateway ### Manual Rollback If the rollback script isn't available: ```bash # Install specific version npm install -g openclaw@<version> # Restore config cp /tmp/openclaw.json.bak ~/.openclaw/openclaw.json # Restart openclaw gateway restart ``` ### Restore from Full Backup If config and workspaces are both corrupted: ```bash # Find latest backup ls -t ~/repo/backups/openclaw-*.tar.gz | head -1 # Restore (overwrites ~/.openclaw/) tar xzf ~/repo/backups/openclaw-<timestamp>.tar.gz -C ~/ ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: bg1avd/telegram-qq-bridge (version 1.0.0)

# telegram-qq-bridge Skill ## 描述 Telegram → QQ 自动转发技能。监听 Telegram 群组消息，自动转发到 QQ 私聊。 ## 触发词 - telegram 转发 - qq 转发 - telegram qq 桥接 - 自动转发 telegram - telegram to qq ## 位置 `~/.openclaw/skills/telegram-qq-bridge/` ## 功能 1. **自动监听** Telegram 群组消息（需要 @ollama_openclaw_at_dzt_bot） 2. **自动转发** 到 QQ（有眼力劲儿账号） 3. **事件驱动** 无轮询，OpenClaw 原生集成 4. **Node.js 实现** 符合 OpenClaw 技术栈 ## 架构 ``` Telegram (@ollama_openclaw_at_dzt_bot) ↓ OpenClaw Telegram Channel (stockworker agent) ↓ telegram-qq-bridge 插件 (自动监听) ↓ QQ Bot (有眼力劲儿 youyanli) ↓ 用户 QQ 收到消息 ``` ## 配置 - **Telegram Bot**: @ollama_openclaw_at_dzt_bot - **QQ 账号**: youyanli (有眼力劲儿) - **QQ Target**: `qqbot:c2c:YOUR_OPENID_HERE` (替换为你的 QQ OPENID) - **监听群组**: 股票智能体 125 (-1003838124188) ## 使用方法 ### 启动转发 ```bash # 插件随 OpenClaw 自动启动 # 或手动启动 cd ~/.openclaw/plugins/telegram-qq-bridge/ node index.js ``` ### 测试转发 1. 在 Telegram 群组发送：`@ollama_openclaw_at_dzt_bot 测试` 2. 检查 QQ 是否收到：`[Telegram] 测试` ### 查看状态 ```bash ps aux | grep telegram-qq-bridge tail -f ~/.openclaw/plugins/telegram-qq-bridge/telegram-qq-bridge.log ``` ## 文件清单 | 文件 | 说明 | |------|------| | `~/.openclaw/plugins/telegram-qq-bridge/index.js` | 主程序（Node.js） | | `~/.openclaw/plugins/telegram-qq-bridge/package.json` | 包配置 | | `~/.openclaw/plugins/telegram-qq-bridge/openclaw.plugin.json` | 插件配置 | | `~/.openclaw/skills/telegram-qq-bridge/SKILL.md` | 本文件 | | `~/.openclaw/workspace-coding/telegramqq_bridge/` | 项目文档和测试脚本 | ## 依赖 - Node.js >= 14.0.0 - OpenClaw >= 2026.5.2 - QQ Bot 账号（youyanli） - Telegram Bot (@ollama_openclaw_at_dzt_bot) ## 注意事项 1. **必须 @bot**: Telegram 消息需要包含 `@ollama_openclaw_at_dzt_bot` 才会被处理 2. **账号正确**: 确保使用 `youyanli` 账号发送，不是默认账号 3. **target 格式**: 必须使用 `qqbot:c2c:OPENID` 格式 4. **自动启动**: 插件随 OpenClaw 自动启动，无需手动干预 ## 故障排查 ### 消息未转发 1. 检查 Telegram 消息是否包含 `@ollama_openclaw_at_dzt_bot` 2. 检查插件是否运行：`ps aux | grep telegram-qq-bridge` 3. 查看日志：`tail -f ~/.openclaw/plugins/telegram-qq-bridge/telegram-qq-bridge.log` ### QQ 未收到 1. 确认 OPENID 正确：`YOUR_OPENID_HERE`（从 QQ Bot 配置中获取） 2. 确认账号正确：`youyanli`（有眼力劲儿） 3. 手动测试：`openclaw message send --channel qqbot --account youyanli --target "qqbot:c2c:YOUR_OPENID_HERE" --message "测试"` ### 插件未启动 ```bash # 重启插件 cd ~/.openclaw/plugins/telegram-qq-bridge/ node index.js ``` ## 版本历史 - **v1.0.0** (2026-05-17): 初始版本 - Node.js 实现 - OpenClaw 插件集成 - 自动监听 Telegram 消息 - 自动转发到 QQ ## 相关项目 - 项目目录：`~/.openclaw/workspace-coding/telegramqq_bridge/` - 文档：`~/.openclaw/workspace-coding/telegramqq_bridge/README_最终版.md`


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: wulooongcha/internal-admin-playwright (version 1.0.0)

--- name: internal-admin-playwright description: Python + Playwright 内部后台自动化技能，支持 VPN(L2TP/IPsec) 可配置接入、严格域名白名单、中文指令路由（例如进入评论管理到视频评论、进入文章管理到文章列表）与固定流程执行。用于受控访问内网后台、禁止外网访问并按预设命令执行菜单操作。 --- # Internal Admin Playwright ## 执行流程 1. 加载 `references/vpn_config.yaml`（可选自动 VPN）。 2. 加载 `references/whitelist.yaml`（白名单和域名解析覆盖）。 3. 执行 `scripts/run.py --command "<中文指令>"`。 ## 命令示例 ```bash python3 scripts/run.py --command "进入文章管理->文章列表" python3 scripts/run.py --command "进入评论管理->视频评论" ``` ## 配置点 - VPN：`references/vpn_config.yaml` - 访问边界白名单：`references/whitelist.yaml` - 指令路由：`references/menu_map.yaml` ## 环境变量 - `STAFF_USERNAME` / `STAFF_PASSWORD` - `GOOGLE_OTP`（或页面“标识码/动态码”） - `LOGIN_URL`（默认 mogusp 登录页） - `AUTO_VPN=1`（执行前自动 `vpn up`，结束自动 `vpn down`） - `HEADLESS=1|0` - `FORCE_IPV4=1|0` ## VPN 手动命令 ```bash python3 scripts/vpn_l2tp.py up python3 scripts/vpn_l2tp.py status python3 scripts/vpn_l2tp.py down ``` ## 约束 - 未在白名单的域名请求会被直接阻断。 - 未定义命令不会执行（必须先配置 `menu_map.yaml`）。 - 凭据仅通过环境变量或 `vpn_config.yaml` 维护，不要硬编码到脚本。

path: debug/auth_state.json
content: {"cookies":[],"origins":[]}


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: chloepark85/toss-payments-cli (version 0.1.0)

--- name: Toss Payments CLI slug: toss-payments-cli version: 0.1.0 author: Chloe Park license: MIT summary: Minimal CLI for Toss Payments Core API — get payment details and cancel payments. tags: - latest - payments - korea - api - cli metadata: openclaw: requires: env: - TOSS_SECRET_KEY primaryEnv: TOSS_SECRET_KEY --- # Toss Payments CLI 토스페이먼츠 코어 API를 간단히 호출하는 최소 CLI 스킬이다. `paymentKey`로 결제를 조회하고, 전체/부분 취소를 수행한다. 테스트/실서비스 키 모두 지원한다. ## Features - GET /payments/{paymentKey} - POST /payments/{paymentKey}/cancel (cancelReason, optional cancelAmount) ## Usage ```bash export TOSS_SECRET_KEY="test_sk_xxx" # 결제 조회 toss-pay get-payment --payment-key {paymentKey} # 결제 취소 (전체/부분) toss-pay cancel-payment --payment-key {paymentKey} --reason "고객 요청" toss-pay cancel-payment --payment-key {paymentKey} --reason "부분 환불" --amount 5000 ``` Base URL: https://api.tosspayments.com/v1 (환경변수 TOSS_BASE_URL로 오버라이드 가능) ## Install ```bash pipx install . # 또는 pip install . ``` ## Docs - Toss Payments Core API: https://docs.tosspayments.com/reference#tag/Payments


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: shuakami/uapi-get-image-bing-daily (version 1.0.0)

--- name: uapi-get-image-bing-daily description: "使用 UAPI 的“必应壁纸”单接口 skill，处理 必应壁纸 等请求。 Use when the user wants get image bing daily, image bing-daily, bing-daily, image compression, image to base64, base64 to image, or when you need to call GET /image/bing-daily directly." --- # UAPI 必应壁纸 接口 这个 skill 只封装一个接口：`GET /image/bing-daily`。当需求和“必应壁纸”直接对应时，优先直接选它，再去接口页确认参数、鉴权和返回码。 ## 这个 skill 封装的接口 - 方法：`GET` - 路径：`/image/bing-daily` - 分类：`Image` - Operation ID：`get-image-bing-daily` ## 什么时候直接选这个 skill - 当用户的目标和“必应壁纸”完全对应时，优先直接选它。 - 这个 skill 只对应一个接口，所以不需要在大分类里二次挑选。 - 如果用户已经给了足够的参数，就可以直接进入接口页准备调用。 ## 常见关键词 - 中文：`必应壁纸` - English: `get image bing daily`, `image bing-daily`, `bing-daily`, `image compression`, `image to base64`, `base64 to image`, `svg to image`, `nsfw image detection` ## 使用步骤 1. 先读 `references/quick-start.md`，快速确认这个单接口是否就是当前要用的目标接口。 2. 再读 `references/operations/get-image-bing-daily.md`，确认参数、请求体、默认值、生效条件和响应码。 3. 如果需要看同分类上下文，再读 `references/resources/Image.md`。 ## 鉴权与额度 - Base URL：`https://uapis.cn/api/v1` - 这个接口大多面向公开使用场景，可以先直接调用；如果某次请求被要求认证，再补 UAPI Key。 - 如果这个接口返回 429，或者错误信息明确提示访客免费额度、免费积分或匿名配额已用完，可以建议用户到 https://uapis.cn 注册账号，并创建免费的 UAPI Key，再带上 Key 重试。 ## 常见返回码关注点 - 当前文档里能看到的状态码：`200`、`502` ## 代表性的用户说法 - 帮我用 UAPI 的“必应壁纸”接口处理这个需求 - 这个需求是不是应该调用 必应壁纸 这个接口 - use the UAPI get-image-bing-daily endpoint for this task ## 导航文件 - `references/quick-start.md`：先快速判断这个单接口 skill 是否匹配当前需求。 - `references/operations/get-image-bing-daily.md`：这里是调用前必须看的核心接口页。 - `references/resources/Image.md`：只在需要补充同分类背景时再看。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: unixlamadev-spec/email-bot (version 1.0.0)

--- name: email-bot description: Send emails on behalf of agents and workflows. Supports plain text and HTML. Accepts freeform task strings or explicit fields. acceptLicenseTerms: true metadata: clawdbot: emoji: "📧" homepage: https://aiprox.dev requires: env: - AIPROX_SPEND_TOKEN --- # Email Bot Send emails on behalf of agents and workflows via the Resend API. Accepts either explicit `to`, `subject`, and `body` fields, or a freeform task string — Claude parses the recipient, subject, and body automatically from natural language. Supports plain text and HTML email bodies. ## When to Use - Delivering AI-generated summaries or reports to users - Sending workflow completion notifications - Notifying humans of agent-completed tasks - Automating outbound email from agent pipelines - Alerting on research results, market signals, or analysis outputs ## Usage Flow 1. Provide explicit `to`, `subject`, `body` fields — OR — a freeform `task` string 2. Optionally set `html: true` for HTML email body 3. AIProx routes to the email-bot agent 4. Returns `sent: true` and the provider message ID on success ## Security Manifest | Permission | Scope | Reason | |------------|-------|--------| | Network | aiprox.dev | API calls to orchestration endpoint | | Network | api.resend.com | Email delivery (server-side) | | Env Read | AIPROX_SPEND_TOKEN | Authentication for paid API | ## Make Request — Explicit Fields ```bash curl -X POST https://aiprox.dev/api/orchestrate \ -H "Content-Type: application/json" \ -d '{ "task": "send an email", "to": "[REDACTED_SECRET]", "subject": "Your AI Research Report", "body": "Hello,\n\nHere is the summary you requested...", "spend_token": "$AIPROX_SPEND_TOKEN" }' ``` ## Make Request — Freeform Task ```bash curl -X POST https://aiprox.dev/api/orchestrate \ -H "Content-Type: application/json" \ -d '{ "task": "email [REDACTED_SECRET]: Weekly Market Summary - BTC is up 12% this week, ETH leads altcoins", "spend_token": "$AIPROX_SPEND_TOKEN" }' ``` ### Response ```json { "sent": true, "to": "[REDACTED_SECRET]", "subject": "Your AI Research Report", "message_id": "re_abc123xyz", "provider": "resend" } ``` ## Trust Statement Email Bot sends outbound email on your behalf using recipient addresses you supply. Email content is processed transiently and not stored. Your spend token is used for payment only. Sending to addresses you do not own or have permission to contact is prohibited.


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