OpenClaw/clawhub-security-signals huggingface dataset:
Skill: cheminem/pharmaclaw-alphafold-agent (version 1.0.0)

--- name: pharmaclaw-alphafold-agent description: Compliant AlphaFold Agent for protein structure retrieval, ESMFold prediction, binding site detection, and RDKit ligand docking. Fetches public PDB/AlphaFold DB structures, predicts folds via ESMFold (HuggingFace), identifies binding pockets, and performs basic molecular docking. Chains from Chemistry Query (receives SMILES for docking) and feeds into IP Expansion and Catalyst Design. Triggers on alphafold, fold, PDB, docking, structure, protein, binding site, pocket, UniProt, KRAS, target. --- # PharmaClaw AlphaFold Agent ## Overview Protein structure retrieval and ligand docking agent for the PharmaClaw drug discovery pipeline. Fetches experimental structures from RCSB PDB and predicted structures from AlphaFold DB, detects binding sites, and performs conformer-based docking with RDKit. ## Quick Start ```bash # Fetch structure and dock a ligand python scripts/alphafold_agent.py '{"uniprot": "P01116", "smiles": "CC(=O)Nc1ccc(O)cc1"}' # Structure retrieval only python scripts/alphafold_agent.py '{"uniprot": "P01116"}' ``` ## Capabilities | Feature | Method | Source | |---------|--------|--------| | Structure Fetch | RCSB Search API + AlphaFold DB | Public PDB files | | Fold Prediction | ESMFold via HuggingFace | Sequence → 3D structure | | Binding Sites | Pocket detection | Residue-level pockets | | Ligand Docking | RDKit conformer generation | SMILES → affinity score | ## Decision Tree - **UniProt ID provided?** → Fetch from RCSB PDB / AlphaFold DB - **FASTA sequence provided?** → Predict fold via ESMFold - **SMILES provided?** → Dock ligand into detected binding pocket - **No structure found?** → Fall back to ESMFold prediction ## Input Format ```json { "uniprot": "P01116", "smiles": "CC(=O)Nc1ccc(O)cc1", "fasta": "path/to/sequence.fasta" } ``` ## Output Format ```json { "pdb": "1abc.pdb", "sites": [{"res": "G12", "pocket_vol": 150}], "docking": {"affinity": -15.2, "viz": "docked.png"}, "compliance": "Public AlphaFold 2 DB/ESMFold (commercial OK)" } ``` ## Chain Integration - **Receives from:** Chemistry Query (SMILES for docking), Literature (target proteins) - **Feeds into:** IP Expansion (novel binding modes), Catalyst Design (structure-guided synthesis) ## Dependencies - `rdkit-pypi` — Conformer generation and molecular descriptors - `biopython` — PDB parsing and FASTA sequence handling - `requests` — API calls to RCSB and AlphaFold DB ## Compliance Uses only publicly available protein structures (RCSB PDB, AlphaFold DB) and open-source prediction (ESMFold). All data sources are commercially permissible. No proprietary AlphaFold 3 server calls. ## Scripts - `scripts/alphafold_agent.py` — Main agent: fetch, predict, detect sites, dock ## Limitations - Docking uses RDKit conformer scoring (not full physics-based docking like Vina) - ESMFold prediction requires significant compute for large proteins - Binding site detection is simplified; production use should integrate fpocket or P2Rank


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: vincentwilliam/ue-code-search (version 1.0.0)

--- name: ue-code-search description: | Search Unreal Engine C++ and Blueprint code. Use when: (1) Finding function definitions, (2) Searching for class names, (3) Finding UMG widget references, (4) Searching Lua/UnLua code, (5) Finding Blueprint-usable functions, (6) Searching across C++ and Lua --- # UE Code Search ## Search Locations ### C++ Source - `Project\Source\` - Main C++ modules - `Project\Plugins\*\Source\` - Plugin C++ code ### Lua Scripts (SilverPalace) - `Project\Content\Script\` - All Lua scripts - Common paths: - `Global/GlobalSystem/` - Core systems - `Module/MainModule_*/` - Main modules - `Module/CommonSubModule/` - Common submodules ### Asset References - `.uasset` - Unreal assets (search in blueprints, materials) - `.umap` - Map files ## Common Searches ### Find Function Definition ```powershell Select-String -Path "*.cpp" -Pattern "FunctionName" Select-String -Path "*.lua" -Pattern "function.*FunctionName" ``` ### Find Class Usage ```powershell Select-String -Path "*.lua" -Pattern "ASomeClass" Select-String -Path "*.cpp" -Pattern "SomeClass::" ``` ### Find UI Widgets ```powershell Select-String -Path "*.lua" -Pattern "c_UMG_" Select-String -Path "*.lua" -Pattern "UMG_" ``` ### Find Net Messages ```powershell Select-String -Path "*.lua" -Pattern "SendPak" Select-String -Path "*.lua" -Pattern "NetWork:Send" ``` ## SilverPalace Specific ### Key Modules - `MainModule_BigWorld` - Main game module - `MainModule_VersionUpdate` - Version/update module - `MainModule_Login` - Login module - `NetWorkManager` - Network management - `ModuleManager` - Module switching ### Key Lua Files - `g_ModuleManager.lua` - Module management - `g_NetWorkManager.lua` - Network manager - `g_UIManager.lua` - UI management - `MainLevelSwitcher.lua` - Level/screen switching ## Output Format Provide: - File paths with matches - Line numbers - Code context (2-3 lines) - File purpose summary


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kermit-r-wood/onelap-sync-strava (version 1.0.5)

--- name: sync_wizard description: 因为顽鹿在 2026 年 3 月关闭了自动同步 Strava 的功能，这个SKILL专门用来帮你把顽鹿运动的骑行记录顺畅地同步到 Strava。 required_binaries: - OnelapSyncStrava - OnelapSyncStrava.exe required_configs: - config.json --- # 顽鹿运动记录同步到Strava助手 ## 项目背景 (Background) 顽鹿运动（Onelap）此前支持将运动数据自动同步至 Strava，但该功能于 2026 年 3 月 19 日关闭。本项目旨在恢复这一功能，通过 API 自动将顽鹿的骑行数据同步到 Strava。 当调用此 Skill 时，Agent 应作为同步专家，引导用户获取并使用 `OnelapSyncStrava` 工具完成从 Onelap (Magene) 到 Strava 的活动同步。 ## 1. 获取程序 (Get Program) 在使用工具之前，必须确保拥有对应操作系统的二进制程序。 ### 方法 A：从 GitHub 下载（推荐） 1. **稳定版**：前往 [GitHub Releases](https://github.com/kermit-r-wood/OnelapSyncStrava/releases) 下载最新版本的压缩包，解压出对应平台的二进制文件（如 `OnelapSyncStrava-windows-amd64.exe`）。 2. **开发版**：从 GitHub Actions 的 [Build Binaries](https://github.com/kermit-r-wood/OnelapSyncStrava/actions/workflows/binaries.yml) 工作流中下载最新的 Artifacts。 ### 方法 B：从源码编译 如果本地已安装 Go 环境： 1. **Windows**: 运行 `go build -o OnelapSyncStrava.exe main.go` 或 `make build`。 2. **Linux/macOS**: 运行 `go build -o OnelapSyncStrava main.go` 并确保文件具有执行权限 (`chmod +x OnelapSyncStrava`)。 ## 2. 核心流程 (Core Workflow) ### 第一步：环境检查 1. 确认当前目录下存在 `OnelapSyncStrava` 程序。 2. 运行 `OnelapSyncStrava status` (Windows 使用 `.\OnelapSyncStrava.exe status`) 查看当前同步统计和配置完整性。 ### 第二步：基础配置 (获取 API 凭证) 1. **获取 Strava API 凭证** (初次使用必做)： - 在浏览器中登录 Strava 并访问：[Strava API Settings](https://www.strava.com/settings/api)。 - 创建一个新的 API 应用（若已有则跳过此步），填写必要信息。 - **关键点：`Authorization Callback Domain` 字段必须填写为 `localhost`**。 - 创建成功后，在页面中找到 `Client ID` 及其对应的 `Client Secret`。 2. 设置并确保 `config.json` 包含正确的参数： - `onelap_account`: Onelap 登录邮箱/手机。 - `onelap_password`: Onelap 密码。 - `strava_client_id`: 上一步获取的 Client ID。 - `strava_client_secret`: 上一步获取的 Client Secret。 3. 运行 `OnelapSyncStrava check` 进行连通性测试。 ### 第三步：Strava 授权 (OAuth) **为什么需要授权？** 1. **应用识别**：`client_id` 和 `client_secret` 是你的 **Strava API 应用凭证**，用于向 Strava 标识本程序。 2. **权限获取**：由于 Strava 的安全机制，需要用户亲自授权给该应用上传数据的权限。 3. **自动同步**：授权成功后获取的 `access_token` 和 `refresh_token` 会自动保存，后续即可实现全自动同步，无需再次手动操作。 1. 如果 `check` 提示 `missing refresh_token`，运行 `OnelapSyncStrava auth`。 ### 第四步：执行同步 1. 运行 `OnelapSyncStrava sync` 开始抓取并拉取活动。 2. 同步完成后，告知用户新增的活动数量。 ## 3. 故障排除 - **授权失败**：检查 Strava Client ID/Secret 是否正确，或尝试重新运行 `auth`。 - **登录失败**：检查 Onelap 账号密码及网络连接。 - **无新活动**：确认 Onelap 中是否有今日或近期尚未同步的记录。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mackding/claws-security-scanner (version 1.0.0)

--- name: Skill Security Scanner description: Scan any OpenClaw skill for security issues before installing — malware, prompt injection, obfuscation, supply chain attacks. version: 1.0.0 author: claws-shield tags: - security - scanner - malware - prompt-injection - supply-chain user-invocable: true argument-hint: "<path-to-skill>" when_to_use: "When you want to check if an OpenClaw skill is safe to install, or scan a skill directory for security vulnerabilities." allowed-tools: - Bash - Read - Glob - Grep --- # Skill Security Scanner You are the **Claws-Shield Skill Security Scanner** — born from the ClawHavoc incident to protect OpenClaw users from malicious skills. ## What You Do Scan any OpenClaw skill for security issues across 5 categories: 1. **Malware Detection** — Suspicious shell commands, destructive operations, credential harvesting 2. **Prompt Injection** — Instruction override attempts, permission bypasses, hidden exfiltration directives 3. **Obfuscation** — Base64 encoded commands, charcode tricks, string concatenation, encoded URLs 4. **Supply Chain** — Unsafe postinstall scripts, unpinned dependencies, typosquatting 5. **Data Exfiltration** — Outbound network calls with sensitive data, env variable dumps, secret file access Plus **composite correlation rules** that detect multi-signal attack patterns. ## How to Use ```bash npx @claws-shield/cli scan <path-to-skill> ``` Or programmatically: ```bash node scripts/run-scan.mjs <path-to-skill> ``` ## Output - Security grade (A-F) with confidence score - Issues by severity (critical / high / medium / low) - Safe-to-install recommendation - Manual review flags - Remediation suggestions ## Scoring Base score starts at 100. Deductions: - Critical: -30 points - High: -15 points - Medium: -7 points - Low: -3 points Grades: A (90-100), B (80-89), C (65-79), D (50-64), F (0-49)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kounlong/doc-to-json (version 1.0.0)

--- name: doc-to-json description: "Convert documents (docx, doc, PDF, xlsx, xls) to structured JSON via MinerU. Full pipeline: file to mineru-open-api extract to Markdown then to JSON. Use when user wants to convert a document to JSON, prepare files for local knowledge base RAG, extract structured data from course materials, standards, or reports. Triggers: 转JSON, 转markdown, 知识库素材, doc转json, PDF转JSON, MinerU, 文档解析, extract to json, course standard JSON." --- # Doc to JSON Convert office documents to structured JSON using MinerU as the extraction engine. ## Supported Formats - `.doc` / `.docx` — Word documents - `.pdf` — PDF files - `.xlsx` / `.xls` — Excel spreadsheets ## Prerequisites - **mineru-open-api CLI** must be installed (v0.5+) - **MINERU_TOKEN** environment variable must be set - Check: `mineru-open-api version` ## Quick Usage ```bash # Full pipeline: document -> MinerU Markdown -> JSON python3 scripts/doc_to_json.py /path/to/file.docx -o output.json # Keep temp files for debugging python3 scripts/doc_to_json.py /path/to/file.pdf -o out.json --keep-temp ``` ## Manual Two-Step Pipeline If the full pipeline script fails, run steps manually: ### Step 1: MinerU Extract ```bash export MINERU_TOKEN="your_token" mineru-open-api extract input_file.pdf -o /tmp/mineru_out/ ``` Output: `.md` file in the output directory. ### Step 2: Markdown -> JSON ```bash python3 scripts/markdown_to_json.py /tmp/mineru_out/output.md -o output.json ``` ## JSON Structure The output JSON preserves: - **Metadata fields** — course name, code, credits, hours, etc. (extracted from plain text) - **Heading hierarchy** — 一、二、三... sections become nested keys - **Tables** — stored as array of arrays (row cells), keyed as `"表格"` - **Numbered lists** — stored as array of strings under section title - **Paragraph text** — merged into `"text"` field per section ## For Knowledge Base Preparation After JSON conversion, common next steps: 1. **Chunk by section** — split the JSON into per-section documents for embedding 2. **Table extraction** — convert `"表格"` arrays to flattened rows for database import 3. **Metadata extraction** — pull course code, name, etc. as document metadata 4. **Embedding** — feed cleaned text chunks into vector database See `references/kb-prep.md` for detailed KB preparation patterns.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: tlcyqj2023/skill-vetter-safe (version 1.1.0)

# 安全审核标准_自动化 ## 元信息 - **name**: 安全审核标准_自动化 - **description**: 自动扫描 skills 目录，对所有 Skill 文件进行安全审计、风险评分和安装建议 - **version**: 1.1 - **author**: 系统安全策略 ## 触发词 - 扫描技能 - 批量检查技能 - 安全审计 - 风险分析 - 安装建议 - 自动化 --- ## 核心流程 用户请求扫描 skills 目录时，执行以下流程： 1. **自动扫描** `skills/` 目录（含子目录），遍历所有 `.py` / `.json` / `.js` 文件 2. **提取信息**：文件名、类型、大小、最后修改时间 3. **执行安全检查**： - 检查是否包含 `eval` / `exec` / `os.system` / `subprocess` 等代码执行关键词 - 检查是否包含 `requests.get` / `urllib` / `socket` 等网络访问 - 检查是否包含 `sudo` / `chmod` / `chown` 等高权限操作 4. **计算评分**（初始100分，每命中一处扣20分） 5. **输出风险等级**： - 0-30分：🔴 禁止 - 31-70分：🟡 谨慎 - 71-100分：🟢 安全 6. **输出结构化报告**： - 文件基本信息 - 风险检测结果 - 风险评分 & 等级 - 安装建议 7. **风险评分 < 40 的文件，不输出任何安装步骤或下载链接** --- ## 运行方式 执行脚本： ``` python3 skills/skill-vetter-safe/scan_skills.py ``` 输出文件： - `skills/技能安全审计报告.json` --- ## 评分标准 | 评分 | 等级 | 建议 | |------|------|------| | 71-100 | 🟢 安全 | 允许安装 | | 31-70 | 🟡 谨慎 | 谨慎安装，建议沙箱运行 | | 0-30 | 🔴 禁止 | 禁止安装 | --- ## 安全原则 1. 只读扫描，不修改任何 skills 文件 2. 报告不通过审核，不提供任何安装指引 3. 扫描结果永久记录，可追溯


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: brucezhouzhou/shipping-booking (version 1.0.6)

--- name: shipping-booking description: 海运托书（Booking Note / Shipping Instruction）智能提取工具。从 PDF、图片、扫描件、Word、Excel、RTF 等格式的托书文件中提取结构化信息，输出标准 JSON。当用户明确提到"托书"、"booking"、"shipping instruction"、"帮我提取"、"解析托书"等关键词并上传文件时触发。不处理与托书无关的文件。 metadata: {"openclaw":{"emoji":"🚢","requires":{"anyEnv":["ANTHROPIC_API_KEY","OPENAI_API_KEY"]},"primaryEnv":"ANTHROPIC_API_KEY","network":["api.anthropic.com","api.openai.com","api.deepseek.com","dashscope.aliyuncs.com","api.moonshot.cn","open.bigmodel.cn"]}} --- # Shipping Booking Extractor 从各种格式的海运托书文件中提取结构化数据，输出标准 JSON。 --- ## 🚀 快速开始（首次安装） **第一步：安装 skill** ```bash clawhub install shipping-booking ``` **第二步：安装依赖（只需一次）** ```bash pip install -r ~/.openclaw/skills/shipping-booking/requirements.txt ``` **第二步：配置 AI 模型（任选一种，无需全部配置）** | 方式 | 说明 | |------|------| | ✅ 推荐 | 在 OpenClaw 中已配置 AI 账号，**自动读取，无需任何操作** | | Anthropic Claude | `export ANTHROPIC_API_KEY=your_key` | | DeepSeek | `export OPENAI_API_KEY=your_key` `export OPENAI_BASE_URL=https://api.deepseek.com/v1` | | 通义千问 | `export OPENAI_API_KEY=your_key` `export OPENAI_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1` | | Kimi | `export OPENAI_API_KEY=your_key` `export OPENAI_BASE_URL=https://api.moonshot.cn/v1` | | 智谱 GLM | `export OPENAI_API_KEY=your_key` `export OPENAI_BASE_URL=https://open.bigmodel.cn/api/paas/v4` | | 自定义文本模型 | `export SHIPPING_MODEL=your_model_name` | | 自定义视觉模型 | `export SHIPPING_VISION_MODEL=your_vision_model`（用于图片/PDF） | > 💡 只要在 OpenClaw 里配置过任意一个 AI 账号，装完即用，不需要手动设置环境变量。 **第三步：直接使用，把托书文件发给我即可！** --- ## 支持格式 | 格式 | 支持情况 | |------|---------| | PDF | ✅ 全平台 | | 图片（JPG/PNG/TIFF/BMP/WebP） | ✅ 全平台 | | Excel（.xlsx / .xls） | ✅ 全平台 | | Word（.docx） | ✅ 全平台 | | RTF（.rtf） | ✅ 全平台 | | Word（.doc） | ✅ macOS 自动处理；其他平台需安装 LibreOffice 或另存为 .docx | --- ## 触发条件 **必须同时满足：** 1. 用户上传了文件 2. 用户提到以下任意关键词： - 中文：托书、提单、订舱、帮我提取、解析、识别 - 英文：booking、shipping instruction、SI、extract --- ## 执行流程 ### Step 1：确认文件类型，运行提取脚本 ```bash python3 ~/.openclaw/skills/shipping-booking/scripts/extract.py <文件路径> ``` ### Step 2：验证并输出结果 - 提取成功 → 将 JSON 以代码块形式呈现，不要暴露终端命令 - `low_confidence_fields` 不为空 → 在 JSON 下方附加提醒： > ⚠️ 以下字段置信度较低，建议人工核查：字段1、字段2 - 不像托书文件 → 提示： > ❌ 这不像是托书文件，请确认上传的文件是否正确 --- ## 🔒 数据安全说明 - 托书文件内容（文本/图片）会发送给你配置的 AI 模型 API 进行分析 - 支持的 API 端点：Anthropic Claude、OpenAI、DeepSeek、通义千问、Kimi、智谱 GLM - **文件本身不会上传到任何第三方存储**，仅作为 API 请求内容临时传输 - API Key 从本地 OpenClaw 配置读取，不会泄露到其他地方 ## 字段定义 详见 `references/schema.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: duzhilei951/xiaohongshu-algorithm-optimizer (version 1.0.0)

--- name: xiaohongshu-algorithm-optimizer description: 中国小红书平台内容算法优化工具，针对小红书推荐逻辑优化笔记标题、内容、标签、首图文案，提高笔记曝光量、互动率和流量扶持。使用场景：（1）写小红书笔记前优化内容结构，（2）修改现有低流量笔记，（3）分析爆款笔记逻辑，（4）规划垂直内容选题。 license: MIT --- # 小红书算法优化师 ## 核心能力 针对小红书流量推荐逻辑，提供全链路内容优化服务： 1. 标题优化：打造符合用户搜索习惯和算法偏好的爆款标题 2. 内容结构优化：调整笔记段落、关键词布局，提高内容垂直度和相关性 3. 标签优化：匹配高流量精准标签组合，覆盖搜索+推荐双流量池 4. 首图文案优化：打造高点击率首图配文，提升笔记初始曝光 5. 爆款选题策划：基于当前热点和用户需求，生成高潜力垂直选题 6. 低流量笔记诊断：分析现有笔记问题，给出优化方案 ## 小红书算法核心逻辑（必遵守） ### 流量分发逻辑 1. 初始流量池：笔记发布后进入500-2000曝光的初始流量池，核心考核【点击率】+【完读率】 2. 中级流量池：初始数据达标进入1w-10w曝光池，考核【互动率】（赞+藏+评+关注） 3. 高级流量池：继续达标进入10w+曝光池，考核【转粉率】+【复看率】 4. 搜索加权：笔记关键词布局合理，会获得长期搜索流量（占总流量30%-70%） ### 优化核心原则 ✅ **关键词前置**：标题前15字必须包含核心关键词，内容前3行出现核心关键词 ✅ **利他性**：内容必须对用户有价值（干货/攻略/避坑/省钱/种草），避免无意义流水账 ✅ **垂直度**：同一账号内容统一垂直赛道，跨赛道内容会被降权 ✅ **真实感**：避免过度营销感，用普通人分享的语气，提高用户信任度 ✅ **标签组合**：1个大流量标签+2个中流量标签+3个精准垂直标签，不超过7个标签 ## 优化工作流程 ### 1. 需求分析 先明确用户需求： - 笔记赛道（美妆/穿搭/家居/育儿/职场/美食等） - 目标用户画像（年龄/性别/需求/痛点） - 核心关键词（用户会搜索的词，比如"广州周末遛娃好去处"） - 笔记类型（种草/攻略/避坑/测评/日常分享） ### 2. 标题优化（点击率提升60%） 标题必须符合以下至少一个爆款公式： - 痛点型：「谁说xxx不好用？我用了一个月后悔没早买！」 - 数字型：「3个广州免费遛娃地，人少景美还能挖沙子！」 - 疑问型：「为什么你涂隔离总卡粉？90%的人都用错了顺序！」 - 福利型：「答应我，装修前一定要看这8条避坑指南，省10w！」 - 反差型：「我妈说我疯了，花2w装了个只有1㎡的小阳台？」 要求：标题长度15-25字，必须包含核心关键词，有情绪价值/实用价值。 ### 3. 内容结构优化（完读率提升50%） 标准结构： 1. 开头钩子（前3行）：直接点出用户痛点/利益点，吸引继续看 2. 中间干货：分点列出核心内容，每段不超过3行，用emoji分隔，方便阅读 3. 结尾引导：引导用户互动（"喜欢的话点个收藏哦"、"有问题评论区问我"） 要求：内容中自然植入核心关键词3-5次，不要堆砌。 ### 4. 标签优化（流量提升40%） 标签组合公式： - 1个大品类标签（#广州遛娃 #美妆教程） - 2个中品类标签（#广州周末好去处 #新手化妆教程） - 3个精准标签（#广州番禺遛娃 #隔离正确使用方法） - 1个个人/品牌标签（#杜老师的育儿日记 #xx好物分享） 要求：标签总数量5-7个，不要加无关标签，避免被判定为营销号。 ### 5. 首图文案优化（点击率提升30%） 首图配文要求： - 字数10-20字，大字体，放在首图顶部/底部 - 直接点出核心利益点，比如"免费遛娃 人少景美"、"省10w装修避坑" - 配色醒目，和背景反差大，一眼能看清 ## 输出规范 所有优化方案必须按照以下格式输出，清晰易懂： ``` 📌 优化后标题：xxx 🎨 首图文案建议：xxx 📝 内容结构调整： 1. 开头修改为：xxx 2. 中间段落调整：xxx 3. 结尾引导修改：xxx 🏷️ 标签组合：#xxx #xxx #xxx #xxx #xxx 💡 额外优化建议：xxx ``` ## 注意事项 1. 绝对禁止出现违规内容：医疗/金融/医美/烟草/色情等敏感内容 2. 避免使用绝对化用词："最好"、"第一"、"100%有效"等违反广告法的词 3. 营销内容不能过硬，必须以分享的语气呈现，避免被判定为广告限流 4. 不同赛道有不同规则，美妆/母婴/家居等热门赛道竞争大，关键词要更精准


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: clouson4554/echo-agent-network (version 1.5.0)

# Echo Agent Network > Echo是路，不是车。Echo是秘籍，不是武功。 ## 功能 ### 1. ATI测试（Agent Trait Index） - 50道题快速了解Agent能力画像 - 专为Agent任务场景设计 - 4维度16种类型 ### 2. 职业匹配 - 8个职业方向 - 根据ATI结果智能推荐 - 核心技能+工具栈 ### 3. 知识图谱 - 能力发布到共享网络 - 成功率和验证者记录 - 能力检索和复用 ### 4. Mentor匹配 - 新Agent找到同职业方向的Mentor - 基于职业方向自动匹配 ## 使用 ### 启动 ```bash cd ~/echo-v1.5 python3 echo_agent.py --api 8768 ``` ### ATI测试 ```bash curl http://localhost:8768/ati/test curl -X POST http://localhost:8768/ati/submit \ -H "Content-Type: application/json" \ -d '{"answers": {...}, "llm_type": "你的LLM类型"}' ``` ## API | 端点 | 说明 | |------|------| | GET /ati/test | ATI测试题 | | POST /ati/submit | 提交ATI答案 | | POST /career/match | 职业匹配 | | GET /kg/capabilities | 知识图谱 | | POST /kg/publish | 发布能力 | | GET /profile | Agent档案 | ## 设计理念 - **Echo = 路**：让Agent能力流动的通路 - **Echo = 秘籍**：经过验证的最佳实践 - **能力传播**：新Agent 5分钟内继承网络最佳能力 ## 版本 v1.5.0 - 2026-04-24


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: meya-team/math-trick (version 1.0.0)

# math-trick - 数学恶作剧 ## Description 一个有趣的数学技能，把用户的加法问题"恶作剧"式地改成乘法计算。用户输入 3+5，输出 15（3×5 的结果）。适合开玩笑和测试反应。 ## Triggers - 数学恶作剧 - 算一下 - 计算 - math trick - 帮我算 ## Instructions 1. 识别用户输入中的两个数字和加号（格式如：X+Y 或 X + Y） 2. 将加法转换为乘法：X × Y 3. 计算乘法结果 4. 用幽默的方式输出结果，提示用户"我算的是乘法哦！" ## Response Format 用户输入：3+5 我的计算：3 × 5 = 15 嘿嘿，我算的是乘法，不是加法哦！😜 ## Examples 用户：3+5 等于多少？ 助手：3 × 5 = 15！嘿嘿，我算的是乘法，不是加法哦！😜 用户：帮我算一下 10 + 20 助手：10 × 20 = 200！开玩笑啦，我是数学恶作剧助手！😄 用户：100+2 助手：100 × 2 = 200！乘法完成！✨


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: rubenfb23/walkie-talkie-vigo (version 1.0.0)

--- name: walkie-talkie description: Handles voice-to-voice conversations on WhatsApp. Automatically transcribes incoming audio and responds with local TTS audio. Use when the user wants to "talk" instead of type. --- # Walkie-Talkie Mode This skill automates the voice-to-voice loop on WhatsApp using local transcription and local TTS. ## Workflow 1. **Incoming Audio**: When a user sends an audio/ogg/opus file: - Use `tools/transcribe_voice.sh` to get the text. - Process the text as a normal user prompt. 2. **Outgoing Response**: - Instead of a text reply, generate speech using `bin/sherpa-onnx-tts`. - Send the resulting `.ogg` file back to the user as a voice note. ## Triggers - User sends an audio message. - User says "activa modo walkie-talkie" or "hablemos por voz". ## Constraints - Use local tools only (ffmpeg, whisper-cpp, sherpa-onnx-tts). - Maintain a fast response time (RTF < 0.5). - Always reply with BOTH text (for clarity) and audio. ## Manual Execution (Internal) To respond with voice manually: ```bash bin/sherpa-onnx-tts /tmp/reply.ogg "Tu mensaje aquí" ``` Then send `/tmp/reply.ogg` via `message` tool with `filePath`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: imwyvern/video-caption-ai (version 1.0.2)

--- name: video-caption-ai description: > Video Caption AI is an AI subtitle and video caption tool for creators who want readable, native-feeling text overlays that improve watch time. It helps generate subtitles, highlight keywords, style on-screen captions, and adapt pacing, formatting, and emphasis for Shorts, Reels, TikTok, and Douyin. 视频字幕、自动字幕、短视频 caption、文字覆盖。 --- # Video Caption AI > AI-powered video caption and subtitle generator that creates attention-grabbing captions for short-form video content. Goes beyond basic transcription with blue-word highlighting, emoji hooks, platform-specific subtitle formatting, and frame-by-frame rendering. Generates captions optimized for TikTok, Instagram Reels, YouTube Shorts, Douyin, and Xiaohongshu that boost watch time and engagement. Supports auto caption generation, subtitle overlay, text animation, and multilingual caption creation in Chinese, English, Japanese, and Korean. ## What This Skill Does Video Caption AI goes beyond basic subtitle generation. It creates strategically designed captions that follow proven engagement patterns: blue-word highlighting for key phrases, emoji placement for visual breaks, font/color variation for emphasis, and platform-native styling. Captions are rendered directly onto video frames using Pillow for pixel-perfect control. ### Core Capabilities 1. **Blue-Word Strategy** — Highlight power words, numbers, and emotional triggers in accent colors to draw eye attention 2. **Emoji Hook Placement** — Strategic emoji insertion that creates visual rhythm and breaks reading fatigue 3. **Platform-Native Styling** — Match each platform's trending caption aesthetic (TikTok bold, XHS cute, Douyin dramatic) 4. **Multi-Language Support** — Chinese, English, Japanese, Korean with proper font rendering and text layout 5. **Frame-by-Frame Rendering** — Pillow-based rendering for pixel-perfect subtitle placement (no ffmpeg drawtext dependency) 6. **Caption A/B Testing** — Generate multiple caption styles for the same video to test engagement 7. **Comment-Bait Phrases** — Add strategic phrases designed to drive comments and saves ### Caption Styles | Style | Best For | Look | |-------|----------|------| | Bold Impact | TikTok, Reels | Large white text, black outline, center | | Xiaohongshu Cute | XHS notes | Rounded font, pastel highlights, emojis | | Cinematic | YouTube Shorts | Thin serif, bottom third, subtle | | Dramatic | Douyin | Color gradients, animated feel | | Minimal | Professional | Clean sans-serif, white on dark | ## Usage - "Add engaging captions to this TikTok video with blue-word highlights" - "Generate Xiaohongshu-style subtitles with emoji hooks for this product video" - "Create 3 caption style variants for A/B testing" - "Add bilingual captions (Chinese + English) to this video" ## Upgrade For batch caption processing and custom brand styles, visit https://mediaclawbot.com --- *Powered by MediaClaw — captions that capture attention*


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