OpenClaw/clawhub-security-signals huggingface dataset:
Skill: mejango/juicy (version 1.0.0)

--- name: juicebox-v5 description: Complete Juicebox V5 protocol skills collection. Build, deploy, and interact with Juicebox projects, revnets, hooks, and omnichain deployments. Includes API reference, implementation details, UI generation, and GraphQL queries. --- # Juicebox V5 Skills Collection A comprehensive set of skills for building on the Juicebox V5 protocol. ## Included Skills ### Core Protocol - **jb-v5-api** - Function signatures and API reference for all contracts - **jb-v5-impl** - Deep implementation details, edge cases, and tradeoffs - **jb-v5-currency-types** - Currency system with real-world and token-derived types - **jb-v5-v51-contracts** - V5 vs V5.1 contract version separation ### Project Management - **jb-project** - Create and configure Juicebox V5 projects - **jb-ruleset** - Configure rulesets with rates, splits, and constraints - **jb-query** - Query project state from the blockchain - **jb-decode** - Decode transaction calldata and analyze history ### Hooks - **jb-pay-hook** - Generate custom pay hooks from specifications - **jb-cash-out-hook** - Generate custom cash out hooks - **jb-split-hook** - Generate custom split hooks ### UI Generation - **jb-deploy-ui** - Generate deployment frontends - **jb-interact-ui** - Generate project interaction frontends - **jb-explorer-ui** - Etherscan-like contract explorer - **jb-event-explorer-ui** - Browse and decode project events - **jb-nft-gallery-ui** - NFT gallery for 721 hooks - **jb-ruleset-timeline-ui** - Visual timeline for ruleset history - **jb-hook-deploy-ui** - Deploy custom hooks from browser ### Financial - **jb-cash-out-curve** - Bonding curve redemption calculations - **jb-fund-access-limits** - Query payout limits and surplus allowances - **jb-protocol-fees** - Fee structures and calculations - **jb-multi-currency** - Handle ETH vs USDC accounting - **jb-terminal-selection** - Dynamic terminal selection for payments - **jb-terminal-wrapper** - Terminal wrapper pattern for extending functionality - **jb-permit2-metadata** - Encode Permit2 metadata for gasless payments ### Revnets - **revnet-economics** - Academic findings and economic thresholds - **revnet-modeler** - Simulate and plan revnet token dynamics - **jb-revloans** - REVLoans contract mechanics - **jb-loan-queries** - Query loan data via Bendystraw ### Omnichain - **jb-omnichain-ui** - Build omnichain UIs for cross-chain projects - **jb-omnichain-payout-limits** - Per-chain payout limit constraints - **jb-suckers** - Cross-chain token bridging ### Data & APIs - **jb-bendystraw** - GraphQL API for cross-chain project data - **jb-relayr** - Multi-chain transaction bundling API - **jb-docs** - Query Juicebox V5 documentation ### Patterns & Best Practices - **jb-patterns** - Common design patterns for vesting, treasuries, yield - **jb-simplify** - Checklist to reduce custom contract needs - **jbx-fee-flows** - JBX ecosystem fee flows and revenue streams


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: enderxiao/feishu-voice-sender-v2 (version 1.0.0)

--- name: feishu-voice-sender-v2 description: 飞书语音消息发送技能 - 根据 channel 自动选择发送方式 metadata: tags: feishu, voice, tts, audio version: 1.0.0 author: OpenClaw Community license: MIT --- # 飞书语音消息发送技能 ## 功能 根据当前 channel 自动选择语音发送方式： - **飞书频道**: 使用小米 MiMo TTS 生成语音并发送飞书原生语音消息 - **其他频道**: 使用文字消息或相应格式 ## 适用场景 - 用户要求发送语音消息时 - 需要根据 channel 自动选择发送方式时 - 想要发送语音通知时 ## 核心组件 ### 1. 频道检测 - 自动检测当前消息的 channel 类型 - 支持飞书、Telegram、Discord 等主流平台 ### 2. 语音生成 - **飞书频道**: 使用小米 MiMo TTS (mimo-v2-tts) - 支持多种语音风格（情感、方言、效果） - 需要配置 `MIMO_API_KEY` ### 3. 语音发送 - **飞书频道**: 使用 OpenClaw `message` 工具发送飞书原生语音消息 - **其他频道**: 使用相应平台的语音发送方式 ## 安装 ### 前置要求 ```bash # 安装小米 MiMo TTS 技能 clawhub install xiaomi-mimo-tts # 安装 ffmpeg (用于音频格式转换) # Windows: winget install ffmpeg # macOS: brew install ffmpeg # Linux: sudo apt install ffmpeg # 配置 API Key export MIMO_API_KEY=your-api-key ``` ### 安装技能 ```bash # 使用 ClawHub 安装 clawhub install feishu-voice-sender ``` ## 使用方法 ### 方法 1：自动检测（推荐） 当用户消息包含语音相关关键词时，自动触发： ```python # 触发关键词示例 - "发语音给我" - "用语音说" - "语音回复" - "念给我听" ``` ### 方法 2：指定 channel ```python # 指定 channel 发送语音 channel = "feishu" # 飞书 channel = "telegram" # Telegram channel = "discord" # Discord ``` ## 工作流 ``` 用户消息 → 触发语音关键词检测 ↓ 检测当前 channel 类型 ↓ 如果是飞书频道: - 使用小米 MiMo TTS 生成语音 - 使用 OpenClaw message 工具发送飞书原生语音消息 如果是其他频道: - 使用相应平台的语音发送方式 ↓ ✅ 完成 ``` ## 配置 ### 必需配置 ```bash # 小米 MiMo API Key export MIMO_API_KEY=your-api-key ``` 获取 API Key: https://platform.xiaomimimo.com/ ### 可选配置 ```bash # 默认语音风格 export MIMO_STYLE=default # 默认声音 export MIMO_VOICE=default_zh ``` ## 支持的语音风格 ### 情感 - 开心、悲伤、紧张、愤怒、惊讶、温柔 ### 方言 - 东北话、四川话、台湾腔、粤语、河南话 ### 效果 - 悄悄话、夹子音、唱歌 ### 语速 - 变快、变慢 ## 示例 ### 飞书语音消息 ```python # 用户说: "发语音给我" # 检测到 channel = "feishu" # 自动使用小米 MiMo TTS 生成语音 # 发送飞书原生语音消息 ``` ### 其他频道 ```python # 用户说: "发语音给我" # 检测到 channel = "telegram" # 使用 Telegram 语音消息发送方式 ``` ## 文件结构 ``` skills/feishu-voice-sender/ ├── SKILL.md # 本文件 └── feishu_voice_sender.py # 主逻辑脚本 ``` ## 依赖项 ### 必需依赖 - **小米 MiMo TTS 技能** (`xiaomi-mimo-tts`) - **ffmpeg** (音频格式转换) - **MIMO_API_KEY** (小米 MiMo API Key) ### 可选依赖 - **OpenClaw 消息工具** - OpenClaw 内置功能 - 用于发送语音消息到飞书 ## 故障排查 ### 语音生成失败 ```bash # 检查 MIMO_API_KEY echo $MIMO_API_KEY # 检查 ffmpeg 安装 ffmpeg -version # 重新安装小米 MiMo TTS 技能 clawhub install xiaomi-mimo-tts --force ``` ### 语音发送失败 **检查 OpenClaw Gateway**： ```bash # 检查 Gateway 状态 systemctl status openclaw-gateway # 查看 Gateway 日志 journalctl -u openclaw-gateway -f ``` **检查飞书连接**： - 确保 OpenClaw 已配置飞书凭据 - 检查飞书机器人权限 ## 更新日志 ### v1.0.0 (2026-03-20) - 初始版本 - 支持飞书语音消息发送 - 支持频道自动检测 - 集成小米 MiMo TTS ## 许可证 MIT License ## 贡献 欢迎提交问题和改进建议！


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: xammarie/jarvis-db-query-doctor-01 (version 1.0.0)

--- name: jarvis-db-query-doctor-01 description: Diagnose slow SQL and rewrite queries for safer performance. Use when handling development work that needs repeatable steps, quality gates, and practical outputs. --- # Jarvis Db Query Doctor 01 Deliver a concrete, reusable workflow for development tasks. ## Inputs - Desired outcome and deadline - Constraints (time, tools, risk tolerance) - Available artifacts (code, docs, screenshots, logs) ## Playbook 1. Define success and failure criteria in measurable terms. 2. Build a smallest-valid execution path before scaling effort. 3. Execute in checkpoints; record evidence at each checkpoint. 4. Apply quality gates before finalizing recommendations. 5. Return implementation-ready outputs and next actions. ## Quality Gates - Evidence-backed claims only. - Explicit assumptions and tradeoffs. - Reversible changes preferred for risky operations. - Clear owner + ETA for each next step. - Include a fallback if the primary plan fails. ## Output Format - Situation summary (5 lines max) - Top findings (ranked by impact) - Action plan (today / this week) - Risks + mitigations - Exact checklist or commands where useful ## Example prompts - "Use this skill to turn my messy notes into a production-ready plan." - "Run this skill and give me a risk-first action sequence with acceptance checks."


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: zhangifonly/augment-code (version 1.0.0)

--- name: "Augment Code" version: "1.0.0" description: "Augment Code AI 编程助手，精通企业级代码理解、大型代码库导航、上下文感知补全" tags: ["ai-coding", "enterprise", "augment", "context"] author: "ClawSkills Team" category: "developer-tools" --- # Augment Code - 企业级 AI 编程工具 ## 简介 Augment Code 是一款专为大型企业代码库设计的 AI 编程助手。 与 Copilot/Cursor 不同，Augment 的核心优势在于深度代码库理解——它能索引整个组织的代码仓库， 理解跨仓库的依赖关系、内部 API 约定和团队编码规范，从而提供真正上下文感知的代码建议。 ## 核心特色 - **深度代码库索引**：对整个代码仓库（包括百万行级项目）建立语义索引 - **跨仓库上下文**：理解微服务间的调用关系、共享库的使用模式 - **企业级安全**：代码不离开企业网络，支持私有化部署，SOC 2 合规 - **团队知识沉淀**：学习团队的编码风格、命名约定、架构模式 - **实时上下文更新**：代码变更后自动增量更新索引，始终保持最新 ## 功能模块 ### 智能代码补全 不同于简单的行级补全，Augment 的补全基于对整个项目的理解： - 自动引用项目内已有的工具函数，而非生成重复代码 - 遵循项目的错误处理模式、日志规范、命名约定 - 补全时考虑上下游服务的接口定义 ### Chat 对话 在 IDE 侧边栏与 AI 对话，支持： - 询问代码库中任意模块的实现细节 - 解释复杂的业务逻辑和数据流 - 搜索"这个功能在哪里实现的" - 分析代码变更的影响范围 ### Agent 模式 自主执行多步编程任务： - 根据需求描述自动定位需要修改的文件 - 跨多个文件进行一致性修改 - 运行测试验证修改正确性 - 生成符合团队规范的代码 ## 支持的 IDE | IDE | 支持状态 | 说明 | |-----|---------|------| | VS Code | 完整支持 | 主力开发平台 | | JetBrains 全家桶 | 完整支持 | IntelliJ/PyCharm/WebStorm 等 | | Vim/Neovim | 有限支持 | 通过 LSP 集成 | ## 与同类工具对比 | 特性 | Augment Code | GitHub Copilot | Cursor | Cline/Roo Code | |------|-------------|----------------|--------|----------------| | 代码库理解深度 | 极深（全仓库索引） | 浅（当前文件+邻近） | 中（项目级） | 中（手动上下文） | | 跨仓库感知 | 支持 | 不支持 | 不支持 | 不支持 | | 企业私有部署 | 支持 | 企业版支持 | 不支持 | 自托管模型可 | | 安全合规 | SOC 2 | SOC 2 | 有限 | 取决于模型 | | 大型代码库表现 | 优秀 | 一般 | 良好 | 一般 | | 团队协作 | 原生支持 | 有限 | 不支持 | 不支持 | | 定价 | 免费（当前） | $10-39/月 | $20-40/月 | 免费+模型费 | ## 适用场景 - **大型企业项目**：百万行级代码库，多团队协作，Augment 的全仓库索引优势明显 - **微服务架构**：跨服务理解 API 调用链，修改时自动考虑上下游影响 - **遗留代码维护**：快速理解老旧代码的业务逻辑，安全地进行重构 - **新人 Onboarding**：新成员通过 Chat 快速了解项目架构和代码规范 - **代码审查**：理解变更的完整上下文，发现潜在的兼容性问题 ## 工作原理 1. **索引阶段**：扫描代码仓库，构建语义图谱（函数调用关系、类型定义、模块依赖） 2. **理解阶段**：将用户当前编辑上下文与全局索引结合，形成完整的语义理解 3. **生成阶段**：基于深度上下文生成代码建议，确保与项目整体风格一致 4. **反馈循环**：用户接受/拒绝建议的行为持续优化模型对项目的理解 ## 安装与配置 ```bash # VS Code 扩展市场搜索 "Augment" 安装 # 或命令行安装 code --install-extension augment.augment-vscode ``` 首次使用需要： 1. 注册 Augment 账号（目前免费） 2. 授权访问代码仓库（本地索引，代码不上传） 3. 等待索引构建完成（大型项目可能需要几分钟） ## 局限性 - 目前仍在快速迭代中，部分功能可能不稳定 - 对非主流语言的支持不如主流语言（Python/JS/TS/Java/Go） - Agent 模式相比 Cursor/Roo Code 还在追赶中 - 社区生态不如开源工具丰富


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: germey/acedatacloud-nano-banana-image (version 1.0.0)

--- name: nano-banana-image description: Generate and edit AI images with NanoBanana (Gemini-based) via AceDataCloud API. Use when creating images from text prompts or editing existing images with text instructions. Supports nano-banana and nano-banana-pro models. license: Apache-2.0 metadata: author: acedatacloud version: "1.0" compatibility: Requires ACEDATACLOUD_API_TOKEN environment variable. Optionally pair with mcp-nano-banana for tool-use. --- # NanoBanana Image Generation Generate and edit AI images through AceDataCloud's NanoBanana (Gemini-based) API. ## Authentication ```bash export ACEDATACLOUD_API_TOKEN="your-token-here" ``` ## Quick Start ```bash curl -X POST https://api.acedata.cloud/nano-banana/images \ -H "Authorization: Bearer $ACEDATACLOUD_API_TOKEN" \ -H "Content-Type: application/json" \ -d '{"prompt": "a watercolor painting of a French countryside village", "model": "nano-banana"}' ``` ## Models | Model | Best For | |-------|----------| | `nano-banana` | Standard image generation (default) | | `nano-banana-pro` | Higher quality, more detailed output | ## Workflows ### 1. Text-to-Image ```json POST /nano-banana/images { "prompt": "a photorealistic macro shot of morning dew on a spider web", "model": "nano-banana-pro", "aspect_ratio": "16:9" } ``` ### 2. Image Editing Edit an existing image using natural language instructions — no mask needed. ```json POST /nano-banana/images/edit { "prompt": "change the background to a starry night sky", "image_url": "https://example.com/photo.jpg", "model": "nano-banana" } ``` ## Parameters ### Generation | Parameter | Values | Description | |-----------|--------|-------------| | `model` | `"nano-banana"`, `"nano-banana-pro"` | Model to use | | `aspect_ratio` | `"1:1"`, `"3:4"`, `"4:3"`, `"9:16"`, `"16:9"` | Output aspect ratio | ### Editing | Parameter | Required | Description | |-----------|----------|-------------| | `image_url` | Yes | URL of the source image | | `prompt` | Yes | Natural language editing instruction | | `model` | No | Model to use (defaults to `"nano-banana"`) | | `aspect_ratio` | No | Output aspect ratio | ## MCP Server ```bash pip install mcp-nano-banana ``` Or hosted: `https://nano-banana.mcp.acedata.cloud/mcp` Key tools: `nano_banana_generate_image`, `nano_banana_edit_image` ## Gotchas - Editing does **NOT** require a mask — just describe the change in natural language - `nano-banana-pro` produces significantly more detailed results but costs more - Results return a direct image URL — no task polling needed - Aspect ratio uses colon notation (e.g., `"16:9"`) not pixel dimensions - The Gemini-based model excels at understanding complex, conversational editing instructions


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: chenyang399/douyin-video-create (version 1.0.0)

# Content Pipeline Skill ## 描述 这个 Skill 提供了一个完整的内容生成 pipeline： **搜索信息 → 整理数据 → 生成脚本 → 生成数字人视频** ## 何时使用 当用户要求： - "帮我生成一个关于 X 的视频脚本" - "我想要一个数字人视频讲解 X" - "帮我整理 X 的信息并生成脚本" - "创建一个关于 X 的内容" ## 功能 ### 1. 信息搜索和整理 - 从多个来源搜索信息 - 自动提取关键点 - 结构化数据组织 ### 2. 脚本生成 - 支持多种风格 (分析、故事、教育) - 自动时长分配 - 多格式输出 (Markdown, JSON) ### 3. 数字人视频生成 - 支持 HeyGen, D-ID, Synthesia - 自动 API 调用 - 视频下载和处理 ## 使用示例 ### 基础用法 ```bash node pipeline.js --topic "人工智能的未来" --style "analysis" ``` ### 指定提供商 ```bash node pipeline.js --topic "气候变化" --provider "d-id" --style "educational" ``` ### 自定义输出 ```bash node pipeline.js --topic "区块链" --output "./my-videos" --style "story" ``` ## 输出 Pipeline 会生成以下文件： ``` output/[timestamp]/ ├── 01-search-results.json # 搜索结果 ├── 02-organized-data.json # 整理后的数据 ├── 03-script.md # Markdown 脚本 ├── 03-script.json # JSON 脚本 ├── 04-video-result.json # 视频生成结果 └── REPORT.json # 完整报告 ``` ## 配置 ### 环境变量 ```bash # 数字人 API Keys export AVATAR_API_KEY="your-api-key" export HEYGEN_API_KEY="your-heygen-key" export DID_API_KEY="your-d-id-key" export SYNTHESIA_API_KEY="your-synthesia-key" ``` ### 脚本模板 支持的风格： - `analysis` - 深度分析型 - `story` - 故事型 - `educational` - 教育型 ## 扩展 ### 添加新的脚本风格 编辑 `script-generator.js` 中的 `SCRIPT_TEMPLATES`： ```javascript SCRIPT_TEMPLATES['my-style'] = { intro: '...', body: '...', conclusion: '...' }; ``` ### 添加新的数字人提供商 编辑 `avatar-generator.js` 中的 `generateWithProvider` 方法。 ### 集成新的搜索源 编辑 `pipeline.js` 中的 `searchInformation` 方法。 ## 故障排除 ### 网络搜索不可用 - 使用本地数据或用户输入 - 集成飞书知识库 - 使用 curl/wget 备选方案 ### 数字人 API 失败 - 检查 API Key 是否正确 - 验证网络连接 - 查看 API 文档 ### 脚本质量不佳 - 调整脚本模板 - 改进数据整理逻辑 - 添加 AI 内容增强 ## 文件结构 ``` content-pipeline/ ├── script-generator.js # 脚本生成引擎 ├── data-organizer.js # 数据整理工具 ├── avatar-generator.js # 数字人视频生成 ├── pipeline.js # 主 pipeline ├── SKILL.md # 本文件 └── examples/ ├── sample-data.json # 示例数据 └── sample-script.json # 示例脚本 ``` ## 许可证 MIT ## 支持 如有问题，请查看日志文件或联系开发者。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: lulu-owo/telegram-import (version 1.0.0)

# telegram-import Skill 将 Telegram 消息从 SQLite 数据库增量导入 LanceDB 向量库。 ## 数据源 - **DB**: `D:\chat\telegram_messages.db` - **表**: `telegram_messages` - **字段**: id, group_name, group_id, message_id, date, sender_id, sender_name, message, matched_keywords, is_reply, media_type, has_6_digit_number, created_time ## 目标存储 - **LanceDB**: `D:\edata.lance` - **Checkpoint**: `D:\edata.lance\temp\telegram.ckpt`（pickle 格式，断点续传） ## Schema | 字段 | 类型 | 说明 | |------|------|------| | id | string | `telegram:{group_id}:{message_id}` | | text | string | 消息文本 | | vector | float[2560] | Qwen3-Embedding-4B 向量 | | category | string | 固定 `telegram` | | scope | string | 固定 `shared` | | importance | float | 固定 `0.5` | | timestamp | int | 消息时间戳（Unix） | | metadata | string | JSON：group_name, group_id, message_id, sender_id, sender_name, matched_keywords, is_reply, media_type, has_6_digit_number | ## Embedding - **LM Studio**: `http://127.0.0.1:1234/v1/embeddings` - **Model**: [REDACTED_SECRET] - **维度**: 2560 ## 执行命令 ```bash python.exe "C:\Users\admin\.openclaw\workspace\skills\telegram-import\scripts\chunk_db.py" telegram ``` ## 增量逻辑 1. 启动时加载 `telegram.ckpt` 恢复 `seen_keys` 和 `last_idx` 2. 从 `last_idx + 1` 继续扫描 DB 3. 跳过 `seen_keys` 中已存在的 key 4. 过滤空消息（message 为 NULL 或空字符串） 5. 每 500 行保存一次 checkpoint


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: ivangdavila/wireguard (version 1.0.0)

--- name: WireGuard description: Configure WireGuard VPN tunnels with secure routing and key management. metadata: {"clawdbot":{"emoji":"🔐","requires":{"bins":["wg"]},"os":["linux","darwin","win32"]}} --- ## AllowedIPs Traps (Most Common Mistakes) - `AllowedIPs` means different things on each side — server: what peer CAN send; client: what to ROUTE through tunnel - `0.0.0.0/0` routes ALL traffic including tunnel endpoint — breaks connectivity, must exclude server's public IP first - Overlapping AllowedIPs between peers = undefined routing — each IP range must belong to exactly one peer - Wrong mask silently breaks routing — `/32` for single host, `/24` for subnet, verify carefully ## Connection Failures - No handshake = wrong public key, firewall blocking UDP, or wrong endpoint — check all three, not just one - One-way traffic = AllowedIPs misconfigured — packets go out but replies don't route back - Missing `PersistentKeepalive = 25` breaks NAT traversal — peer behind NAT unreachable after ~2 minutes - Config file permissions must be 600 — wg-quick silently refuses to start with loose permissions ## DNS Leaks - Without `DNS =` in client config, DNS queries bypass tunnel — leaks real IP to DNS provider - Full tunnel (`0.0.0.0/0`) without DNS config = false sense of security — traffic tunneled but DNS exposed ## Routing Setup - IP forwarding disabled by default on Linux — tunnel works but packets don't route between interfaces - NAT required for internet access through tunnel — without masquerade, return packets don't find their way - Firewall must allow UDP on ListenPort — WireGuard is UDP only, no TCP fallback exists ## Key Security - Private key file permissions matter — world-readable key is compromised, set 600 immediately after generation - Never transmit private keys — generate on each machine, exchange only public keys - Config files contain private keys — treat wg0.conf as secret, not just privatekey file ## Live Changes - Adding peers requires interface reload on most setups — or use `wg set` for live changes without dropping connections - `wg syncconf` applies changes without restart — but config file format differs from wg.conf (use `wg-quick strip`) ## Debugging - `wg show` displays handshake timestamps — stale handshake (>2 min) means connection dead despite interface up - Handshake happens on first packet — no traffic = no handshake attempt, ping to test


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sky-lv/skylv-data-validator (version 1.0.2)

--- description: Data validation assistant. Validates JSON/CSV/Excel schemas and data quality checks. Triggers: data validation, json schema, csv validation, data quality. keywords: validation, json, csv, excel, schema name: skylv-data-validator triggers: data validator --- # skylv-data-validator > Universal data validation. 17 built-in rules, schema inference, JSON validation. ## Skill Metadata - **Slug**: skylv-data-validator - **Version**: 1.0.0 - **Description**: Validate JSON, objects, arrays against schemas. 17 built-in validators including type, pattern, email, URL, UUID. Schema inference from examples. - **Category**: data - **Trigger Keywords**: `validate`, `schema`, `check`, `data quality`, `validation` --- ## Built-in Validators (17) | Rule | Description | |------|-------------| | required | Value must be present | | type | string|number|boolean|object|array | | min/max | Number range | | minLength/maxLength | String length | | pattern | Regex match | | email | Valid email | | url | HTTP(S) URL | | uuid | UUID format | | enum | Must be in list | | integer | Integer check | | positive | Number > 0 | | date | Valid date | | isoDate | ISO 8601 format | | json | Valid JSON | --- ## Market Data Top competitor: `data-validation` (1.054) — weak competition. --- *Built by an AI agent that validates everything.* ## Usage 1. Install the skill 2. Configure as needed 3. Run with OpenClaw


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: malicious | SkillSpector: suspicious (MEDIUM)
Skill: dashiming/pans-lead-miner (version 1.0.0)

--- name: pans-lead-miner description: | AI算力销售线索挖掘器。输入行业关键词，自动搜索筛选潜在客户，识别算力需求信号。 通过SearXNG聚合多数据源（Crunchbase/GitHub/LinkedIn/新闻），按融资和算力需求排序。 触发词：线索挖掘, 客户发现, AI公司搜索, 融资动态, 算力需求, lead generation, prospect --- # pans-lead-miner AI算力销售线索挖掘器 ## 功能 输入行业关键词，自动搜索并筛选潜在客户列表，识别算力需求信号。 ## 数据源 - **Crunchbase RSS**：公开融资新闻 - **GitHub Trending**：AI 项目检测 - **LinkedIn 公开搜索**：通过 SearXNG 聚合 - **新闻聚合**：通过 SearXNG 搜索融资动态 ## 搜索逻辑 1. 输入关键词（如 "AI infrastructure", "LLM startup"） 2. 通过本地 SearXNG 实例搜索（http://127.0.0.1:8888/search?format=json） 3. 解析结果，提取公司名、人名、融资信息 4. 按算力需求信号排序（融资轮次、模型训练关键词、招聘GPU工程师等） ## CLI 参数 - `--keyword`：搜索关键词（必填） - `--source`：数据源（crunchbase/github/linkedin/news/all） - `--limit`：结果数量（默认 20） - `--region`：地区（us/cn/eu/global） - `--stage`：融资阶段（seed/series-a/series-b/later/all） - `--json`：JSON 输出 - `--export`：导出 CSV 文件路径 ## 输出格式 | 公司 | 融资轮次 | 金额 | 关键人 | 算力信号 | 来源 | ## 示例 ```bash python3.11 ~/.qclaw/skills/pans-lead-miner/scripts/mine.py --keyword "AI infrastructure" --limit 5 python3.11 ~/.qclaw/skills/pans-lead-miner/scripts/mine.py --keyword "LLM startup" --source github --limit 10 python3.11 ~/.qclaw/skills/pans-lead-miner/scripts/mine.py --keyword "AI" --stage series-a --export leads.csv ``` ## 依赖 - Python 3.11+ - SearXNG 本地实例（http://127.0.0.1:8888） - requests - beautifulsoup4


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: wwumit/excel2insights (version 1.1.0)

name: excel2insights description: Excel数据分析工具 metadata: openclaw: requires: env: [] optional: env: [] --- Excel数据分析工具，支持基本的Excel数据分析和报告生成。

path: __init__.py
content: """
Excel2Insights - Excel数据分析工具
"""

from excel2insights import ExcelAnalyzer

__all__ = ["ExcelAnalyzer"]


path: VERSION.txt
content: Excel2Insights v1.1.0 (增强版)


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