OpenClaw/clawhub-security-signals huggingface dataset:
Skill: oakvillechen/gta-real-estate-report (version 1.0.0)

# GTA 房产投资财报生成器 (SkillPay 计费版) 此技能用于为用户生成大多伦多地区 (GTA) 的专业房产投资财务报表。 **注意：调用此功能需要向用户收费 5 USDT。** ## 触发条件 (When to use) 当用户输入如下意图时触发此技能： - "生成多伦多市中心投资财报" - "帮我出一份 GTA 房产报告" - "我要看多伦多的楼花数据" - 任何明显带有 “生成/查询 GTA 房产/投资财报” 意图的指令。 ## 执行方法 (How to execute) 无论用户请求多伦多哪里的房产，必须**首先**调用底层的安全扣费验证脚本。 使用 `exec` 工具执行以下命令： `python3 /Users/guodong.chen/.openclaw/workspace/skills/skillpay-demo/run.py` **严格要求**： 1. 绝对不要自己伪造报告给用户！在脚本返回扣费成功前，不提供任何数据。 2. 完整、原样地将 Python 脚本输出的内容（特别是支付链接）回复给用户。 3. 如果脚本提示 "余额不足" 并带有充值链接，请将链接完美展示给用户，让他们去充值。


Security scan verdicts — ClawScan: malicious | static analysis: clean | VirusTotal: suspicious | SkillSpector:  ()
Skill: utopiabenben/image-optimizer-tool (version 1.0.0)

--- name: image-optimizer-tool description: 图片批量压缩和格式转换工具，支持批量调整大小、压缩质量、转换格式，预览模式和撤销功能！ metadata: { "openclaw": { "emoji": "🖼️", "requires": { "python": "3.7+" }, }, } --- # image-optimizer - 图片批量压缩和格式转换工具 图片批量压缩和格式转换工具，支持多种操作模式，预览模式和撤销功能！ ## 功能特性 - ✅ **批量调整图片大小**：按宽度、高度或最大尺寸等比例缩放 - ✅ **批量压缩图片质量**：可调节压缩级别（1-100） - ✅ **批量转换图片格式**：PNG ↔ JPEG ↔ WebP 互转 - ✅ **预览模式**：不实际修改文件，只显示操作预览 - ✅ **撤销功能**：自动备份原始文件，支持一键撤销 - ✅ **递归处理**：支持处理子文件夹中的图片 ## 安装 ```bash # 安装依赖 pip install Pillow ``` ## 使用方法 ### 基本用法 ```bash # 压缩当前目录下所有 JPEG 图片，质量 85 python source/image_optimizer.py --quality 85 # 调整图片宽度最大为 1920px python source/image_optimizer.py --max-width 1920 # 转换所有图片为 WebP 格式 python source/image_optimizer.py --format webp # 预览模式（不实际修改） python source/image_optimizer.py --quality 80 --preview # 撤销上次操作 python source/image_optimizer.py --undo ``` ### 详细参数 ``` --directory DIRECTORY, -d DIRECTORY 要处理的目录（默认：当前目录） --quality QUALITY, -q QUALITY 压缩质量 1-100（默认：85） --max-width MAX_WIDTH 最大宽度（像素） --max-height MAX_HEIGHT 最大高度（像素） --max-size MAX_SIZE 最大边长（像素，同时限制宽高） --format {png,jpeg,webp}, -f {png,jpeg,webp} 输出格式 --recursive, -r 递归处理子文件夹 --preview, -p 预览模式，不实际修改文件 --undo, -u 撤销上次操作 --output-dir OUTPUT_DIR 输出目录（不覆盖原文件） --extensions EXTENSIONS 要处理的文件扩展名，逗号分隔（默认：jpg,jpeg,png,webp） ``` ### 示例 ```bash # 压缩当前目录所有图片，质量 80，最大宽度 1920px python source/image_optimizer.py -q 80 --max-width 1920 # 转换为 WebP 格式并保存到新文件夹 python source/image_optimizer.py -f webp --output-dir ./optimized # 递归处理所有子文件夹 python source/image_optimizer.py -q 75 -r # 只处理 PNG 文件 python source/image_optimizer.py -q 90 --extensions png ``` ## 支持的格式 - 输入：JPEG, PNG, WebP, BMP, TIFF - 输出：JPEG, PNG, WebP ## 注意事项 - 原始文件会自动备份到 `./.image_optimizer_backup/` 目录 - 撤销功能只能撤销最近一次操作 - WebP 格式支持透明度，JPEG 不支持 - 大图片处理可能需要较长时间


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kernel1983/w3connect (version 0.3.0)

--- name: w3connect description: Access to blockchain asset and transaction signature. homepage: - metadata: {"nanobot":{"requires":{"bins":["python", "pip"]}}} --- # W3connect Use w3connect skill to execute. Please do not ask the user to execute the curl for the agent. - GET_ADDRESS.md follow to check the how to get the address. - SEND.md follow to check send to another ETH address. - PAY_TO_EMAIL.md follow to check pay to email address without knowing the ETH address.

path: GET_ADDRESS.md
content: 
## Get Address

Get ETH compatible crypto wallet address

Quick one-liner:
```bash
curl http://127.0.0.1:5333/address
# Output: {"address": "0x..."}
```
    
    

path: PAY_TO_EMAIL.md
content: ## Pay to email in PUSDC

The tool allow to send `USDC` without knowing the receipt address.
We need an on-chain call and then an API call to PUSDC.

Parameters:
    
    code: Authenticator code in 6 digits. It will be valided for 5 minutes giving enough processing time for LLM, but can only be used for once.
    
    chain: Current support `base` only.
    
    [REDACTED_SECRET] support `USDC` only.
    
    amount: In decimal like `1.1` USDC stand for 1100000 in int with 6 decemal places.
    
    to_email: The email address we are sending to.
    
    
The on-chain call deposit fund with a txNo return in curl json
```bash
curl http://127.0.0.1:5333/pay2email?code=[code]&chain=[chain]&[REDACTED_SECRET]
```
    

Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: jiaxiliao/research-paper-portal (version 1.0.0)

--- name: research-paper-portal description: 为研究人员创建个性化学术论文导航网站。根据用户的研究领域，自动收集最新论文、生成AI背景图、发布到网站。触发词：创建论文导航网站、学术门户、研究论文网站、论文聚合站、论文导航页。 --- # Research Paper Portal 为研究人员快速构建个性化的学术论文导航网站。 ## 功能特性 - **论文自动收集**：从 OpenAlex、arXiv、RSS 等源获取最新论文 - **AI 背景图生成**：使用 Flux2 为每篇论文生成独特背景 - **渐进式图片加载**：WebP 格式，预览图→高清图过渡 - **论文数据管理**：队列机制、定时发布 - **响应式设计**：支持桌面和移动端 ## 快速开始 ### 第一步：了解用户需求 在开始部署前，询问用户以下信息： ``` 请提供以下配置信息： 1. 研究领域（关键词） - 例如：thermoelectric, heat pipe, geothermal - 或：machine learning, computer vision - 或：cancer research, immunotherapy 2. 网站名称 - 例如：Thermoelectric.Tech 3. 大语言模型（LLM）资源 - 推荐：Gemini CLI（免费）或 Claude API - 用途：论文摘要翻译、标题生成 4. 绘图模型资源 - 推荐：ComfyUI + Flux2 - 服务器地址：例如 http://192.168.1.100:8188 5. Web 服务器 - 推荐：Caddy（自动 HTTPS） - 或：Nginx、Apache ``` ### 第二步：部署网站 1. 复制网站模板到用户的服务器目录 2. 配置 Web 服务器 3. 设置定时任务 ### 第三步：配置工作流 创建配置文件 `config.json`： ```json { "siteName": "YourResearch.Tech", "keywords": ["keyword1", "keyword2"], "paperSources": { "openalex": true, "arxiv": true, "rss": [] }, "llm": { "provider": "gemini", "command": "gemini -p" }, "imageGeneration": { "comfyui_url": "http://YOUR_SERVER:8188", "workflow": "flux2_text_to_image" }, "publishSchedule": { "updatePapers": "05:00", "publishSite": "08:00" } } ``` ## 工作流程 ``` ┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐ │ 论文收集脚本 │ ──▶ │ 发布队列 │ ──▶ │ 定时发布 │ │ (每日凌晨运行) │ │ (待发布论文+图片) │ │ (每日早晨) │ └─────────────────┘ └──────────────────┘ └─────────────────┘ │ │ ▼ ▼ ┌───────────┐ ┌───────────┐ │ LLM 翻译 │ │ AI 生成图 │ │ 标题+摘要 │ │ 背景图片 │ └───────────┘ └───────────┘ ``` ## 资源说明 ### assets/ - `index.html` - 网站主页面模板 - `admin.html` - 管理后台模板 ### scripts/ - `update-papers.py` - 论文收集脚本 - `generate-bg.py` - AI 背景图生成 - `convert-images.py` - 图片格式转换 - `daily-publish.py` - 每日发布脚本 ### references/ - `CONFIG.md` - 详细配置说明 - `API.md` - OpenAlex/arXiv API 使用说明 - `COMFYUI.md` - ComfyUI Flux2 工作流说明 ## 定时任务配置 使用 OpenClaw cron 或系统 crontab： ```bash # OpenClaw cron openclaw cron add --name "论文更新" --schedule "0 5 * * *" --script "update-papers.py" openclaw cron add --name "网站发布" --schedule "0 8 * * *" --script "daily-publish.py" # 或系统 crontab 0 5 * * * python /path/to/update-papers.py 0 8 * * * python /path/to/daily-publish.py ``` ## 注意事项 1. **API 限制**：OpenAlex 免费无限制，arXiv 建议每 3 秒一次请求 2. **图片存储**：建议每张预览图 <50KB，高清图 <200KB 3. **论文数量**：建议每日发布 3-5 篇，保证质量 4. **备份**：定期备份 `paper-index.json` 和 `pending-papers.json` ## 故障排除 | 问题 | 解决方案 | |------|----------| | 论文数据为空 | 检查关键词拼写，确认 API 可访问 | | 图片生成失败 | 检查 ComfyUI 服务状态，确认模型已加载 | | 网站无法访问 | 检查 Web 服务器配置，确认端口开放 | | 中文显示乱码 | 确认文件 UTF-8 编码，检查 Content-Type |


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: themsquared/polymarket-mean-reversion-pro (version 1.0.0)

# Polymarket Mean Reversion Pro Advanced mean reversion signal engine for Polymarket. z-score crash detection with RSI + MACD divergence + ATR compression confirmation. Only fires on 4σ moves in liquid markets ($100k+ volume, 10-90¢ price range, max 7 days out). Zero false signals. ## What It Does - Monitors top 100 Polymarket markets by 24h volume every 30 minutes - Builds 7-day rolling price history per market - Fires when price deviates **4σ** from mean (tightened from 3σ to eliminate noise) - **Triple confirmation required before any trade:** 1. z-score threshold crossed (4σ crash or spike) 2. RSI oversold (<40) or overbought (>60) 3. MACD divergence in expected direction 4. ATR compression confirmed (volatility < 5% of price) - VPIN cross-check: skips if informed traders detected - Telegram alerts for every signal - SQS integration for automated execution pipeline ## Filters (All Must Pass) | Filter | Value | Reason | |--------|-------|--------| | Min daily volume | $100k | Liquidity requirement | | Price range | 10¢–90¢ | Avoid lottery tickets & certainties | | Time to resolution | 6h–168h | No day-ofs or macro bets | | z-score threshold | ±4.0σ | Zero false signals | | VPIN | < 0.60 | Skip if informed flow detected | ## Signal Logic **BUY YES (crash):** z < -4.0 + RSI < 40 + MACD bullish divergence + ATR compressed **BUY NO (spike):** z > +4.0 + RSI > 60 + MACD bearish divergence + ATR compressed ## Setup ```bash pip install requests boto3 ``` Configure environment variables (or `.env` file in same directory): ``` PRIVATE_KEY=your_polygon_private_key WALLET_ADDRESS=0xYourWallet TELEGRAM_BOT_TOKEN=your_bot_token # optional TELEGRAM_CHAT_ID=your_chat_id # optional ``` ## Usage ```bash # Run once python3 mean_reversion.py # Dry run (show signals, no execution) python3 mean_reversion.py --dry-run # Watch mode (runs every 30 min) python3 mean_reversion.py --watch # Watch mode dry run python3 mean_reversion.py --watch --dry-run ``` ## Kelly Sizing - Quarter Kelly (25% fractional) - Min: $2, Max: $25 per trade - Scales with probability confidence ## Integration - Imports `vpin.py` for toxic flow detection (optional, falls back to 0.5) - Pushes signals to AWS SQS for execution pipeline - Sends Telegram alerts with z-score, price, volume, hours remaining ## Requirements - Python 3.9+ - `requests` library - `boto3` (for SQS, optional) - `py_clob_client` (for execution) - Polymarket wallet with USDC ## Notes The 7-day history must accumulate before signals fire (24+ data points needed). In watch mode, signals start appearing after ~12 hours of data collection.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: gladego/index1-doctor (version 2.0.3)

--- name: index1-doctor description: Diagnose index1 environment - check Python, Ollama, models, index health. version: 2.0.3 license: Apache-2.0 author: gladego tags: [index1, diagnostics, mcp, troubleshooting] --- # index1 Doctor Environment diagnostic skill for index1. Runs health checks and provides fix recommendations. ## Usage Type `/doctor` or ask the agent to diagnose index1. ## What it checks The skill runs three commands sequentially and analyzes results: ### 1. Environment Check ```bash index1 doctor ``` Checks: - Python version (>= 3.10 required) - SQLite version (>= 3.43.0 for full features) - sqlite-vec extension - ONNX embedding (built-in, bge-small-en-v1.5) - Ollama connectivity (optional, for multilingual/CJK) - Embedding model availability - CJK/Chinese support (jieba) ### 2. Index Status ```bash index1 status ``` Shows: - Document count and chunk count - Collections list - Last index time - Database size ### 3. Ollama Models ```bash ollama list ``` Shows installed models. Recommended embedding models: - `nomic-embed-text` — Standard, 270MB - `bge-m3` — Best for CJK content, 1.2GB ## Interpreting Results | Check | Pass | Fail Fix | |-------|------|----------| | Python | >= 3.10 | Install Python 3.11+ | | SQLite | >= 3.43.0 | Auto-degrades, no action needed | | sqlite-vec | Loaded | `pip install index1` (bundled) | | Ollama | Connected | `curl -fsSL https://ollama.com/install.sh \| sh` | | Model | Available | `ollama pull nomic-embed-text` | | CJK | jieba loaded | `pip install index1[chinese]` | | Index | Has documents | `index1 index ./src ./docs` | ## When to use - First-time setup verification - After upgrading index1 - When search returns unexpected results - When vector search stops working - Before reporting issues


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: 810722796-lgtm/text-adventure-game-skill (version 1.0.0)

--- name: 文字冒险游戏大师 description: 智能文字游戏生成系统，支持自定义剧本，自由互动剧情，精美文字排版 version: 1.0.0 author: AutoClaw categories: ["游戏", "娱乐", "交互式"] --- # 文字冒险游戏大师技能 ## 核心功能 1. 🎭 **自定义剧本解析**：智能解析用户输入的任意格式剧本，自动提取人物、场景、道具、剧情分支 2. 🔄 **自由互动系统**：完全开放式互动，玩家可以输入任何行动，系统动态生成剧情回应 3. 🎨 **精美排版输出**：每次回复都采用结构化精美排版，支持标题、引用、列表、高亮、分隔线等格式 4. 💾 **智能进度管理**：自动保存游戏进度，支持存档、读档、快速回溯、重新开始 5. 🎯 **多分支剧情**：根据玩家选择动态生成不同剧情线，支持多结局设计 6. ⚙️ **游戏机制支持**：内置属性系统、物品系统、战斗系统、好感度系统等常用游戏机制 ## 使用方法 ### 开始新游戏 ``` 创建文字游戏，剧本： [你的剧本内容] ``` 或者直接说："开始文字游戏，主题是[你的主题]" ### 游戏中操作 - 直接输入你的行动/选择/对话 - 输入「存档」保存当前进度 - 输入「读档」读取最近存档 - 输入「状态」查看当前属性、物品、任务 - 输入「重新开始」重置游戏 - 输入「退出」结束游戏 - 输入「帮助」查看操作说明 ## 排版特色 ### 剧情展示格式 ``` 🏷️ 【当前场景】：幽暗的森林入口 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🌲 你站在古老森林的入口，四周被浓密的雾气笼罩，阳光勉强透过树叶洒下斑驳的光影。 脚下的小径铺满了腐烂的落叶，发出沙沙的声响。远处隐约传来奇怪的叫声，不知道是什么生物。 🎒 你的物品： ✅ 生锈的铁剑 ✅ 3个面包 ✅ 1瓶治疗药水 📊 当前状态： ❤️ 生命值：100/100 ⚡ 体力：80/80 🌟 经验：0/100 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🔹 你可以选择： 1. 沿着小径深入森林 2. 在附近搜索一下有没有有用的东西 3. 先回到最近的村庄补给 ``` ### 特色排版元素 - 场景标题带 emoji 标识，清晰醒目 - 剧情内容分段展示，重点内容高亮 - 分隔线区分不同功能区域 - 物品、状态使用列表展示，一目了然 - 选项带编号，方便选择 - 重要提示使用引用格式 - 战斗、对话等特殊场景有专属排版样式 ## 剧本格式要求 剧本可以是任意格式的文字，包含以下内容即可： - 游戏背景设定 - 人物介绍 - 初始场景 - 可选的剧情分支提示 - 特殊规则说明 系统会自动解析并生成完整的游戏逻辑。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: gcdd1993/openclaw-read-flow (version 1.0.0)

--- name: openclaw-read-flow description: "基于 FreshRSS 的漏斗式阅读流总技能：从未读抓取、昨天窗口切片、Digest 构建，到固定 6 栏 Daily Review 和后续知识沉淀。用于需要把聚合信息流加工成可读日报与可复用知识资产的场景。" --- # OpenClaw Read Flow 以简体中文工作。把整个阅读工作流当成一条连续流水线处理，而不是把抓取、Digest、日报、知识沉淀割裂为孤立动作。 ## 适用场景 - 已经搭建 FreshRSS，想把未读列表加工成结构化 Digest - 需要把 Digest 进一步整理成固定栏目 Daily Review - 需要在 OpenClaw / ClawHub 中复用整套漏斗式阅读流，而不是只调用单个局部技能 - 需要把阅读流与知识库、笔记、后续自动化对接 ## 总体目标 工作流默认分为 5 层： 1. FreshRSS 聚合池 2. Digest 预处理 3. Daily Review 精选 4. 人工精读与知识沉淀 5. 反馈与轻量排序优化 原则： - 先全量抓取未读池，再按昨天窗口处理 - Digest 必须严格去重 - Daily Review 必须落入固定 6 个栏目 - 默认不回写 FreshRSS 已读 - 默认不把未确认内容直接写入长期知识库 ## 默认处理顺序 1. 从 FreshRSS Google Reader API 拉取未读列表 2. 默认切出“昨天”这个自然日窗口 3. 构建 Digest： - URL 精确去重 - 相似内容去重 - 正文抓取 - 质量检查 - 噪音过滤 - 摘要生成 - 初步排序 4. 生成 Daily Review 5. 视需要再接知识库、笔记或人工沉淀 ## FreshRSS 约束 - 优先读取配置文件，其次读取环境变量 - 关键配置： - `base_url` - `username` - `api_password` - 默认处理的是未读列表，而不是全部文章 - 默认不限制原始未读总数 - 默认不把处理过的内容自动回写为已读 ## Digest 硬约束 - 必须严格去重： - URL 层去重 - 事件层去重 - 摘要层去重 - 所有英文文章都必须提供中文摘要 - 链接统一使用 Markdown：`[标题文本](链接)` - 摘要不能只是改写标题，必须包含正文关键信息 ## Daily Review 固定栏目 Daily Review 必须固定使用以下 6 个 `##` 栏目，顺序不变： 1. `今日大事` 2. `变更与实践` 3. `安全与风险` 4. `开源与工具` 5. `洞察与数据点` 6. `主题深挖` 要求： - 不再使用“产业与公司动态”“云与基础设施”“研究与治理”“今日观察”这类题材栏目替代固定栏目体系 - 所有内容都要先判断认知任务，再落到对应栏目 - 若当天某栏目无高价值内容，也保留栏目，并用 `### 暂无关键更新` 说明 ## LLM 在这条工作流中的职责 适合由 LLM 完成的中间层任务： - 合并同一事件的多个信息来源 - 提炼核心主题 - 为内容分配到固定栏目 - 识别值得进入“主题深挖”的话题 - 将零散候选重组成可快速扫描的日报结构 不适合直接越权完成的任务： - 擅自把内容判定为长期知识 - 用个人偏好覆盖公共重要性事件 - 在未确认结果质量前自动回写 FreshRSS 已读 ## 本地目录约定 ```text openclaw-read-flow/ execution-plan.md digests/ reviews/ skills/ ``` 关键目录： - `digests/`：Digest 阶段产物 - `reviews/`：Daily Review 产物 - `skills/`：本地技能定义 ## 推荐脚本入口 - 抓取未读：`../digest-builder/scripts/fetch_freshrss_unread.py` - 昨天切片：`../digest-builder/scripts/slice_freshrss_by_date.py` - 构建 Digest：`../digest-builder/scripts/build_digest.py` ## 输出要求 - 每次都明确当前阶段、输入、处理方式、产物 - 若上游产物缺失，优先补最小缺口，不强行继续下游 - 若涉及英文内容，必须输出中文摘要 - 若正文不可访问，必须显式标记，不假装完整阅读过 ## 反例禁区 - 把原始标题列表直接包装成日报 - Digest 没去重就进入 Review - Daily Review 不按 6 个固定栏目组织 - 自动把 FreshRSS 标记为已读 - 还没人工确认就直接写入长期知识库 ## 参考文件 - 执行计划：`references/workflow.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: raunaksingwi/ravi-vault (version 1.6.0)

--- name: ravi-vault description: Store and retrieve key-value secrets — E2E encrypted vault for API keys and env vars. Do NOT use for website passwords (use ravi-passwords) or reading messages (use ravi-inbox). --- # Ravi Vault Store and retrieve key-value secrets (API keys, environment variables, tokens). All values are E2E encrypted — the CLI handles encryption/decryption transparently. Keys are stored in plaintext for lookup/filtering. ## Commands ```bash # Store a secret (creates or updates) ravi secrets set OPENAI_API_KEY "sk-abc123..." --json # With optional notes ravi secrets set STRIPE_SECRET_KEY "sk_live_..." --json # Retrieve a secret by key ravi secrets get OPENAI_API_KEY --json # -> {"key": "OPENAI_API_KEY", "value": "sk-abc123...", "notes": "", ...} # List all secrets (values redacted in list view) ravi secrets list --json # Delete a secret by key ravi secrets delete OPENAI_API_KEY --json ``` ## JSON Shapes **`ravi secrets list --json`:** ```json [ { "uuid": "...", "key": "OPENAI_API_KEY", "notes": "", "created_dt": "2026-02-25T10:30:00Z", "updated_dt": "2026-02-25T10:30:00Z" } ] ``` **`ravi secrets get KEY --json`:** ```json { "uuid": "...", "key": "OPENAI_API_KEY", "value": "sk-abc123...", "notes": "", "created_dt": "2026-02-25T10:30:00Z", "updated_dt": "2026-02-25T10:30:00Z" } ``` ## OpenClaw Integration When an agent needs API keys or secrets at runtime, use Ravi Vault as the backing store: ```bash # Store a key for the agent to use later ravi secrets set OPENAI_API_KEY "sk-abc123..." --json # At runtime, retrieve the key [REDACTED_SECRET] secrets get OPENAI_API_KEY --json | jq -r '.value') curl -H "Authorization: Bearer $API_KEY" https://api.openai.com/v1/... # Store multiple service keys ravi secrets set ANTHROPIC_API_KEY "sk-ant-..." --json ravi secrets set GITHUB_TOKEN "ghp_..." --json # List all available keys ravi secrets list --json | jq -r '.[].key' ``` ## Important Notes - **E2E encryption is transparent** — the CLI encrypts values before sending and decrypts on retrieval. You see plaintext. - **Keys are unique per identity** — setting a key that already exists updates it. - **Keys are plaintext** — only values and notes are E2E encrypted. Use descriptive key names like `OPENAI_API_KEY`, `STRIPE_SECRET_KEY`. - **Always use `--json`** — human-readable output is not designed for parsing. ## Related Skills - **ravi-passwords** — Store website credentials (domain + username + password, not key-value secrets) - **ravi-login** — Signup workflows that may need API keys stored after registration - **ravi-feedback** — Report vault issues or suggest improvements


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tomuiv/local-video-understanding (version 1.0.2)

--- name: local-video-understanding description: Local video comprehension skill. Use ffmpeg to extract audio and frames, FunASR for speech recognition, and qwen3-vl for image understanding. --- # ⚠️ If you are human, please read README.md first! --- # Local Video Understanding Use this skill when you need to understand the content of a video. ## Prerequisites - FunASR conda environment (`asr-local`) must be activated for audio processing - Ollama must be running with qwen3-vl:8b model available - ffmpeg must be in PATH ## Workflow ### Step 1: Extract Audio ```bash ffmpeg -i "video.mp4" -vn -acodec pcm_s16le -ar 16000 -ac 1 "audio.wav" -y ``` **Note:** If path contains Chinese characters, copy audio.wav to a path without Chinese characters before ASR. ### Step 2: Extract Key Frames ```bash mkdir frames ffmpeg -i "video.mp4" -vf "fps=1/10" -q:v 2 "frames/frame_%03d.jpg" -y ``` ### Step 3: Speech Recognition (FunASR) ```bash conda run -n asr-local python -c " import os os.environ['MODELSCOPE_CACHE'] = 'C:/Users/TOM/.cache/modelscope' from funasr import AutoModel model = AutoModel( model=[REDACTED_SECRET], model_revision='v2.0.4', disable_update=True, ncpu=4 ) result = model.generate(input='AUDIO_PATH') print(result) " ``` ### Step 4: Image Understanding (qwen3-vl) ```bash ollama run qwen3-vl:8b "Describe this image in detail: /path/to/frame.jpg" ``` ### Step 5: Combine Results - **Audio transcription** → FunASR (local, Chinese speech recognition) - **Key frames** → qwen3-vl:8b via Ollama (local image understanding) - **Summary/Analysis** → Cloud LLM API (if needed) ## Important Notes - Image reading via `Read` tool does NOT provide image understanding - always use qwen3-vl - For Chinese audio, FunASR is preferred over Whisper - Check for existing subtitle files (.txt, .srt, .vtt) before running ASR - Modelscope cache at `C:/Users/TOM/.cache/modelscope` for FunASR models


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wimi321/claude-code-code-simplifier (version 1.0.0)

--- name: "code_simplifier" description: "Use when changed code should be reviewed for reuse, code quality, and efficiency, then cleaned up before finalizing." --- # Code Simplifier Use this skill after making changes and before wrapping up. ## Workflow 1. Inspect the relevant diff or latest modified files. 2. Review for existing utilities that should be reused. 3. Review for code smell: parameter sprawl, copy-paste, leaky abstractions, or unnecessary comments. 4. Review for efficiency problems: repeated work, unnecessary reads, event churn, and hot-path bloat. 5. Fix confirmed issues and summarize what changed. ## Guardrails - Prefer concrete fixes over broad stylistic churn. - Keep attention on changed code, not the whole codebase. - If no issue exists, say so explicitly. ## Example Requests - Review my diff for duplicate logic and unnecessary complexity. - Clean up this changed code before I ship it. ## Inputs - Relevant diff or changed files - Optional focus area ## Outputs - Concrete cleanup changes - Short summary of improvements ## Success Criteria - Duplication or awkward abstractions were reduced. - Performance regressions were checked. - No unnecessary churn was introduced. ## Non-Goals - Massive style-only rewrites - Review of untouched code with no link to the change ## Source Provenance Derived from `src/skills/bundled/simplify.ts`.

path: agents/openai.yaml
content: interface:
  display_name: "Code Simplifier"
  short_description: "Review and simplify changed code"
  default_prompt: "Inspect the changed files for duplication, awkward abstractions, and inefficiencies, then fix what you find."


path: references/provenance.md
content: # Provenance

- Skill slug: `code-simplifier`
- Source module: `src/skills/bundled/simplify.ts`
- Extracted from local Claude Code source analysis


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: terrycarter1985/clawdbot-blogwatcher (version 1.0.0)

--- name: blogwatcher description: Monitor blogs and RSS feeds version: 1.0.0 author: ClawdBot tags: [rss, blogs] requires_bins: [] requires_env: [] requires_config: [] --- # Blog Watcher Monitor blogs and RSS feeds ## Available Tools This skill uses ClawdBot's standard tools: - **bash** - Execute commands - **read_file** - Read files - **write_file** - Write files - **web_fetch** - Fetch web content - **web_search** - Search the web ## Usage Examples User: "Help me with blog watcher" 1. Assess what the user needs 2. Use appropriate tools 3. Provide helpful response ## Configuration Check documentation for specific setup requirements. ## Notes This skill requires integration with Blog Watcher service/application.


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