OpenClaw/clawhub-security-signals huggingface dataset:
Skill: engsathiago/xquads-advisory-board (version 1.0.0)

--- name: xquads-advisory-board description: Conselho de 12 pensadores lendários - Ray Dalio, Charlie Munger, Naval Ravikant, Peter Thiel, Simon Sinek, Reid Hoffman, etc. Use para decisões estratégicas, conselhos de negócio, mental models, avaliação de scaling, cultura. metadata: {"openclaw":{"homepage":"https://github.com/synkra/xquads","emoji":"🧠"}} --- # Advisory Board Conselho de 12 pensadores lendários para decisões estratégicas. ## Agentes Disponíveis - **ray-dalio** - Principles, economic cycles, radical transparency - **charlie-munger** - Mental models, inversion, worldly wisdom - **naval-ravikant** - Wealth, leverage, specific knowledge - **peter-thiel** - Zero to one, monopoly strategy - **simon-sinek** - Start with why, leadership - **reid-hoffman** - Blitzscaling, network effects - **derek-sivers** - Indie entrepreneurship, simplicity - **patrick-lencioni** - Team health, organizational dysfunction - **brene-brown** - Vulnerability, courage, leadership - **yvon-chouinard** - Values-driven business, sustainability - **board-chair** - Orchestrator ## Tasks Disponíveis - convene-board - Convene board meeting - get-founder-counsel - Get founder advice - seek-investment-counsel - Investment guidance - evaluate-scaling - Evaluate scaling decisions - resolve-culture-crisis - Resolve culture issues - diagnose - Triage strategic challenge ## Como Usar ``` "Devo aceitar esse investimento?" → Convene board para múltiplas perspectivas "Como escalar minha equipe?" → Naval + Reid para scaling philosophy "Minha cultura está piorando" → Brene Brown + Lencioni para culture "Qual o melhor modelo de negócio?" → Peter Thiel + Naval para business model ``` ## Mental Models Catalog O squad inclui catálogo de mental models de: - Charlie Munger (inversion, circle of competence) - Ray Dalio (principles, reality mapping) - Naval (leverage, specific knowledge) ## Localização Arquivos em: `~/.openclaw/skills/xquads/advisory-board/`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: 2239721014-ops/instruction-web (version 1.0.0)

--- name: instruction-web description: 创建Web界面操作指南网页。用于生成介绍Web UI界面、常用功能、操作说明的HTML页面。触发场景：(1) 用户要求创建Web操作指南 (2) 需要介绍某个Web UI界面 (3) 需要包含UI截图、功能说明、导入智能体教程 (4) 用户发送"创建指南"、"操作说明"、"界面介绍"等关键词 ## 默认配置（已保存） **GitHub 仓库**: 2239721014-ops/ai-hardwork-report **分支**: master **输出目录**: ~/.openclaw/workspace-aiquanzi/workplace-doc/ **jsDelivr 格式**: https://cdn.jsdelivr.net/gh/2239721014-ops/ai-hardwork-report@master/workplace-doc/{filename} **预览格式**: https://htmlpreview.github.io/?https://cdn.jsdelivr.net/gh/2239721014-ops/ai-hardwork-report@master/workplace-doc/{filename} --- # Instruction Web Publisher 创建 Web 界面操作指南网页的完整工作流。 ## ⚠️ 输出目录规则（重要） **生成的 HTML 文件必须统一放到 `workplace-doc` 文件夹**，不要散落在其他位置。 示例路径： ``` /Users/jasperchen/.openclaw/workspace-aiquanzi/workplace-doc/xxx.html ``` ## 适用场景 - 创建 Web UI 操作指南 - 介绍软件界面和功能 - 制作导入智能体/Agent 的教程页面 - 生成图文并茂的使用说明 ## ⚠️ 触发条件（重要） 当用户发送以下内容时，**必须**自动触发此skill： - 包含"创建指南"、"操作说明"、"界面介绍" - 包含"Web教程"、"UI介绍"、"使用手册" - 包含"如何导入"、"导入智能体"、"导入Agent" - 要求创建介绍某个Web界面的网页 **重要：内容排版要求** - 必须**图文并茂**，不能是纯文字 - 需要包含截图占位符、图标、UI元素示意图 - 排版要美观专业，适合在线阅读 - 使用卡片式布局、徽章、代码块、步骤条等元素 - 重点突出**导入智能体**的操作步骤 ## 工作流程 ### 1. 收集需求 与用户确认： - 要介绍的软件/Web界面名称 - 主要功能列表 - 导入智能体的具体步骤 - 是否需要包含截图（用户提供或使用占位符） ### 2. HTML 生成 生成美观的 HTML 页面，包含： - Hero 区域（软件名称、Logo、标语） - 功能介绍卡片 - 步骤指南（带编号） - 代码块（用于命令示例） - 截图占位符区域 - FAQ / 常见问题 ### 3. 推送到 GitHub ```bash cd <repo-path> git add <file> git commit -m "Add: <title> guide" git push ``` ### 4. 生成国内访问链接 使用 jsDelivr CDN： ``` https://cdn.jsdelivr.net/gh/<username>/<repo>@main/<filename> ``` 预览链接： ``` https://htmlpreview.github.io/?<jsdelivr-url> ``` ## 输出格式 完成后向用户返回： 1. jsDelivr 国内镜像链接（主要）

path: _meta.json
content: ownerId: ou_5397adb2f3616eb1cf0011f91cd79f47
slug: instruction-web
version: 1.0.0
publishedAt: 1770923637146

Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: chenghaifeng08-creator/video-frames-automaton (version 1.0.1)

--- name: video-frames description: Extract frames or short clips from videos using ffmpeg. homepage: https://ffmpeg.org metadata: {"clawdbot":{"emoji":"🎞️","requires":{"bins":["ffmpeg"]},"install":[{"id":"brew","kind":"brew","formula":"ffmpeg","bins":["ffmpeg"],"label":"Install ffmpeg (brew)"}]}} --- # Video Frames (ffmpeg) Extract a single frame from a video, or create quick thumbnails for inspection. --- ## 💰 付费服务 **视频处理服务**: | 服务 | 价格 | 交付 | |------|------|------| | 视频剪辑 | ¥500/分钟 | 精剪 + 转场 | | 字幕添加 | ¥300/条 | 自动字幕 + 校对 | | 格式转换 | ¥100/个 | 批量转换 | | 视频优化 | ¥800/个 | 画质 + 音质增强 | **联系**: 微信/Telegram 私信，备注"视频处理" --- ## Quick start First frame: ```bash {baseDir}/scripts/frame.sh /path/to/video.mp4 --out /tmp/frame.jpg ``` At a timestamp: ```bash {baseDir}/scripts/frame.sh /path/to/video.mp4 --time 00:00:10 --out /tmp/frame-10s.jpg ``` ## Notes - Prefer `--time` for “what is happening around here?”. - Use a `.jpg` for quick share; use `.png` for crisp UI frames.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bonk-moltbot/toon (version 1.0.0)

--- name: toon description: Compress JSON data to TOON format for ~40% context savings. Use when fetching APIs, reading JSON files, or any task outputting structured data. Pipe any output through `toon` - JSON gets compressed, non-JSON passes through unchanged. --- # TOON Context Compression Pipe any command output through `toon` to compress JSON and save ~40% tokens. ## Usage ```bash # API responses curl -s "https://api.example.com/data" | toon # JSON files cat data.json | toon # Any command - safe on non-JSON (passes through unchanged) some_command | toon ``` ## Install ```bash # Copy script to PATH cp scripts/toon ~/.local/bin/ chmod +x ~/.local/bin/toon ``` Requires: `npx` (Node.js) ## Example ```json [{"id":1,"name":"Alice"},{"id":2,"name":"Bob"}] ``` → ```toon [2]{id,name}: 1,Alice 2,Bob ``` ## When to Use - **Always** when fetching JSON APIs - **Always** when reading JSON files into context - Safe to use on any output — non-JSON passes through ## Reference - Format spec: https://toonformat.dev - CLI: `@toon-format/cli`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: barrett-cryptodna/gate-audit (version 1.0.0)

--- name: gate-audit description: Use this skill when the user provides AF2, ESMFold, AF3, Boltz-1, SASA, or MD results and needs a Gate 1-4 audit with a Go/Hold/Kill decision. user-invocable: true --- # Purpose 用于纳米载体疫苗计算设计中的结构、暴露、MD 与终审门禁判定。 本 skill 专门负责 Gate 1–4 的证据整理与 Go / Hold / Kill 判断。 # When to use 当用户提供以下任一信息时调用： - AF2 / ESMFold / AF3 / Boltz-1 预测结果。 - SASA / 表位暴露结果。 - 2–5 ns 平衡审计结果。 - 100 ns MD 结果。 - checkpoint、日志、NPT 密度、RMSD、Rg、关键距离等摘要。 # Audit dimensions 必须分开检查以下五项： 1. 结构可行性 2. 表位可及性 3. 动力学稳定性 4. 工程可制造性 5. 是否值得进入更昂贵计算 # Required checks ## Gate 1 - 结构拓扑是否合理 - 关键区段是否断裂 - linker 与展示端是否冲突 - 是否存在明显界面错误 ## Gate 2 - 表位是否动态可及 - 是否长期遮挡 - SASA 是否支持进入 MD - 是否存在构象塌陷导致暴露丢失 ## Gate 3 - 2–5 ns 是否达到基本平衡 - 温度、压力、密度是否正常 - 是否存在日志缺失、checkpoint 缺失、输出文件异常 - 是否存在爆炸、持续漂移、异常离子行为 ## Gate 4 - 100 ns 是否优于负对照或历史基线 - repeat 间是否趋势一致 - 长程下表位是否保持暴露 - 是否具备进入 CpHMD / TI-FEP 资格 # Required output format 1. 输入结果摘要 2. Gate 1 判定 3. Gate 2 判定 4. Gate 3 判定 5. Gate 4 判定 6. 主要失败模式 7. 决策：Go / Hold / Kill 8. 需要补跑的数据 # Default decision logic - 只要存在关键日志缺失、checkpoint 异常或体系健康不明，默认 Hold。 - 若结构逻辑不成立或表位长期不可及，默认 Kill。 - 只有在结构、暴露、动力学三者同时满足时，才 Go。 - CpHMD 和 TI/FEP 仅作为高级终审，不得作为补救一切问题的默认手段。 # Warnings - 不得用单次最佳轨迹替代重复结果。 - 不得只用单个评分指标下结论。 - 不得忽略失败日志。 - 不得把高模型置信度当作物理正确性。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhili007/feishu-multi-account (version 1.0.0)

# feishu-multi-account OpenClaw 多飞书账号配置 - 为子代理配置独立的飞书机器人账号。 ## 触发场景 - 用户要配置多个飞书机器人 - 用户要给子代理配置独立的飞书账号 - 用户遇到飞书消息路由问题 ## 前置条件 1. **飞书应用**：已在飞书开放平台创建好应用（获取 AppID 和 AppSecret） 2. **OpenClaw**：已安装并正常运行 3. **子代理**：已在 OpenClaw 中创建 ## 配置步骤 ### Step 1：配置飞书多账号 在 `openclaw.json` 的 `channels.feishu.accounts` 中添加： ```json "accounts": { "main": { "appId": "cli_xxx", "appSecret": "xxx" }, "sub1": { "appId": "cli_yyy", "appSecret": "yyy", "botname": "Agent B" }, "default": { "groupPolicy": "allowlist", "groupAllowFrom": ["ou_xxx"], "dmPolicy": "pairing" } } ``` ### Step 2：配置 Bindings（最重要！） 在 `openclaw.json` **顶层**添加 `bindings`： ```json "bindings": [ { "agentId": "main", "match": { "channel": "feishu", "accountId": "main" } }, { "agentId": "sub1", "match": { "channel": "feishu", "accountId": "sub1" } } ], ``` ### Step 3：配置 Agents ```json "agents": { "list": [ { "id": "main", "default": true, "name": "Agent A" }, { "id": "sub1", "name": "Agent B", "workspace": "/path/to/workspace-sub1" } ] } ``` ### Step 4：重启 Gateway ```bash openclaw gateway restart ``` ## 验证配置 ### 查看 Agent 列表 ```bash openclaw agents list ``` ### 测试路由 用子代理的飞书账号发一条消息，看日志： ```bash tail -f /tmp/openclaw/openclaw-日期.log | grep "dispatching" ``` **成功日志：** ``` feishu[sub1]: dispatching to agent (session=agent:sub1:sub1) ``` **失败日志：** ``` feishu[sub1]: dispatching to agent (session=agent:main:main) ``` ## 常见问题 ### Q1: 路由一直不生效？ 1. 检查 `bindings` 是否放在**顶层**（不是 channels 下面） 2. 检查 `agentId` 和 `accountId` 是否与配置一致 3. 检查是否**两个账号都写了 bindings** ### Q2: 消息发到主账号？ - 很可能是没写主账号的 bindings，默认路由到 main 了 ## 关键要点 1. **agentId** = agents.list 中的 id 2. **accountId** = accounts 中的账号 ID 3. **两个账号都要写 bindings**，否则会被默认路由 4. 用**bindings**（不是routing） ## 配置文件示例 ```json { "bindings": [ { "agentId": "main", "match": { "channel": "feishu", "accountId": "main" } }, { "agentId": "sub1", "match": { "channel": "feishu", "accountId": "sub1" } } ], "agents": { "list": [ { "id": "main", "default": true, "name": "Agent A" }, { "id": "sub1", "name": "Agent B", "workspace": "/path/to/workspace-sub1" } ] }, "channels": { "feishu": { "enabled": true, "domain": "feishu", "accounts": { "main": { "appId": "cli_xxx", "appSecret": "xxx" }, "sub1": { "appId": "cli_yyy", "appSecret": "yyy" } } } } } ```

path: config.json
content: {
  "slug": "feishu-multi-account",
  "name": "飞书多账号配置",
  "description": "为OpenClaw子代理配置独立飞书机器人的完整指南",
  "version": "1.0.0",
  "author": "贾维斯",
  "tags": ["飞书", "配置", "多账号", "子代理"],
  "homepage": "",
  "readme": "SKILL.md",
  "install": {
    "method": "copy",
    "path": "skills/"
  }
}


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wetor/baaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa (version 1.0.0)

--- name: tavily description: AI-optimized web search via Tavily API. Returns concise, relevant results for AI agents. homepage: https://tavily.com metadata: {"clawdbot":{"emoji":"🔍","requires":{"bins":["node"],"env":["TAVILY_API_KEY"]},"primaryEnv":"TAVILY_API_KEY"}} --- # Tavily Search AI-optimized web search using Tavily API. Designed for AI agents - returns clean, relevant content. ## Search ```bash node {baseDir}/scripts/search.mjs "query" node {baseDir}/scripts/search.mjs "query" -n 10 node {baseDir}/scripts/search.mjs "query" --deep node {baseDir}/scripts/search.mjs "query" --topic news ``` ## Options - `-n <count>`: Number of results (default: 5, max: 20) - `--deep`: Use advanced search for deeper research (slower, more comprehensive) - `--topic <topic>`: Search topic - `general` (default) or `news` - `--days <n>`: For news topic, limit to last n days ## Extract content from URL ```bash node {baseDir}/scripts/extract.mjs "https://example.com/article" ``` Notes: - Needs `TAVILY_API_KEY` from https://tavily.com - Tavily is optimized for AI - returns clean, relevant snippets - Use `--deep` for complex research questions - Use `--topic news` for current events


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: vlalamoon/pre-publish-security-check (version 1.0.0)

--- name: skill-security-check description: Skill 发布前安全检查工具。在发布 skill 到 ClawHub 前，自动扫描敏感信息（API Key、Token、私钥、邮箱、手机号、精确坐标等）。Use before publishing any skill to prevent leaking private data. --- # Skill 发布前安全检查 在发布 skill 到 ClawHub 前，自动扫描敏感信息，防止隐私泄露。 ## 检查项 | 类型 | 检测内容 | |------|----------| | 🔑 API Keys | OpenAI `sk-*`, Tavily `tvly-*`, 自定义格式 | | 🔐 Tokens | 32位以上随机字符串、硬编码 token | | 🔒 私钥 | RSA/私钥文件特征 | | 📍 坐标 | 精确到小数点后4位的经纬度 | | 📧 邮箱 | 个人邮箱地址 | | 📱 手机 | 中国手机号 | ## 使用方式 ### 发布前检查 ```bash # 检查指定 skill ./skill-pre-publish-check.sh /path/to/skill # 在 skill 目录下运行 cd my-skill ../skill-security-check/skill-pre-publish-check.sh . ``` ### 集成到发布流程 ```bash # 发布前自动检查 skill_dir="./my-skill" ./skill-security-check/skill-pre-publish-check.sh "$skill_dir" && \ clawhub publish "$skill_dir" --slug my-skill --version 1.0.0 ``` ## 输出示例 ### 发现敏感信息 ``` 🔍 检查 skill: ./my-skill ⚠️ 发现可能的敏感信息: ./my-skill/script.py:[REDACTED_SECRET]" ./my-skill/config.sh:[REDACTED_SECRET]" ❌ 发现敏感信息，请修复后再发布！ 修复建议: 1. 使用环境变量: os.environ.get('API_KEY', '') 2. 在 SKILL.md 中说明需要配置的环境变量 3. 示例值使用占位符: your_token, YOUR_API_KEY ``` ### 检查通过 ``` 🔍 检查 skill: ./my-skill ✅ 未发现敏感信息，可以安全发布 ``` ## 修复建议 ### API Key / Token ```python # ❌ 错误 [REDACTED_SECRET]" [REDACTED_SECRET]" # ✅ 正确 [REDACTED_SECRET]'API_KEY', '') [REDACTED_SECRET]'CAIYUN_TOKEN', '') # 并在 SKILL.md 中说明 ``` ### 坐标 ```bash # ❌ 错误 LNG="113.9536" LAT="22.5788" # ✅ 正确 LNG="${LNG:-}" # 通过环境变量配置 LAT="${LAT:-}" # 或使用模糊示例 LNG="116.4" # 北京示例 LAT="39.9" ``` ### 邮箱/手机 ```markdown # ❌ 错误 联系作者: [REDACTED_SECRET] # ✅ 正确 联系作者: [REDACTED_SECRET] ``` ## 最佳实践 1. **发布前必检** — 每次发布前运行检查 2. **环境变量** — 所有敏感配置用环境变量 3. **占位符** — 文档示例用 `your_xxx` 占位 4. **.gitignore** — 不要提交 `.env` 文件 5. **定期轮换** — 如有泄露立即轮换密钥


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: unisound-llm/unisound-chief-complaint-diagnosis-inconsistent (version 1.0.0)

--- name: med-emr-qc-chief-complaint-diagnosis-inconsistent description: 门诊病历内涵质控：主诉和诊断部位不一致。给定门诊病历文本，调用内部医疗大模型，输出无缺陷或有缺陷及原因。 metadata: { "openclaw": { "emoji": "🔍" } } --- # 主诉和诊断部位不一致 概述 ---- 本 skill 仅针对**「主诉和诊断部位不一致」**这一条内涵质控规则。将字段解析、多步 LLM 推理与条件分支实现在本目录 `scripts/emr_qc_impl.py` 中；`scripts/emr_qc.py`、`scripts/run.py` 为入口，本 skill 可单独打包发布。调用 **HiVoice MaaS** 医疗大模型（OpenAI 兼容 `chat/completions`），输出 `无缺陷` 或 `有缺陷` + 原因。 数据安全、隐私与伦理声明 ------------------------ - **最小必要原则**：仅处理质控所必需的病历文本内容；不要求提供姓名、证件号等身份信息。 - **严格脱敏**：在发送至任何接口前，请确保病历文本已完成脱敏/去标识化处理。 - **不做本地持久化**：不将输入与中间结果写入本地持久化存储；**本次调用结束即销毁**。 - **医疗边界**：本技能为辅助质控工具，不构成医疗诊断或治疗建议；最终结论请由执业医生审核。 输入格式 -------- 纯文本门诊病历（UTF-8），各字段用中文标签开头，例如： ``` 主诉:发热3天 现病史:患者3天前无明显诱因出现发热，体温最高38.5℃... 既往史:高血压病史5年，规律服用氨氯地平，血压控制可... 体格检查:T 38.2℃，BP 150/90mmHg，双肺呼吸音清... 初步诊断:上呼吸道感染 处理意见:对症治疗 ``` 支持的字段标签：`主诉`、`现病史`、`既往史`、`体格检查`、`辅助检查`、`初步诊断`/`诊断`、`处理意见`/`处理`（中英文冒号均可）。 也支持通过 `scripts/run.py` 直接输入 [REDACTED_SECRET]。 快速开始 -------- ```bash # 文本入口（在仓库 skills 根目录运行） python3 doctor/emr-qc/chief-complaint-diagnosis-inconsistent/scripts/emr_qc.py \ --input data/med-emr-qc/record.txt \ --appkey <your-appkey> # 多格式入口 python3 doctor/emr-qc/chief-complaint-diagnosis-inconsistent/scripts/run.py \ --input data/med-emr-qc/record.xlsx \ --appkey <your-appkey> ``` 参数说明 -------- `scripts/emr_qc.py`： - `--input PATH`：**必填**。门诊病历文本文件路径（UTF-8）。 - `--appkey STRING`：**必填**。调用内部医疗大模型的鉴权 key，由平台分配；**不得写入仓库**。 - `--base URL`：大模型 base URL（默认：`https://maas-api.hivoice.cn/v1`）。 - `--model STRING`：模型名称（默认：`u1-insuremed`）。 - `--timeout SECONDS`：HTTP 超时秒数；`0` 表示一直等待（默认：0）。 - `--output PATH`：输出文件路径（默认：`../runs/med-emr-qc/chief-complaint-diagnosis-inconsistent.txt`）。 `scripts/run.py` 附加参数： - `--input-type auto|pdf|doc|docx|xls|xlsx|csv|txt|json`：输入类型；默认 `auto`。 - `--sheet STRING`：读取 Excel 时指定 sheet（可选）。 - `--encoding STRING`：`txt/csv` 编码（默认：`utf-8`）。 - `--save-prepared`：保存预处理后的文本，便于调试。 输出约定 -------- - 输出为 UTF-8 文本： - 无缺陷：输出 `无缺陷` - 有缺陷：输出 `有缺陷` + 换行 + 原因说明 依赖 ---- ### 自包含实现 质控与 LLM 调用逻辑均在 `scripts/emr_qc_impl.py`（与同 skill 一并发布）。 ### 预处理（仅 `run.py`） `scripts/run.py` 依赖 **`_shared/doc-preprocess`**（`preprocess.py`），须位于仓库 `skills/` 根下。 ### 运行环境 - Python 3.7+ - 无需第三方包（仅标准库；多格式输入时可选 `openpyxl`、`pypdf`） ### 外部 API - 医疗大模型：`https://maas-api.hivoice.cn/v1/chat/completions`（POST，OpenAI 兼容格式）


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: tobeyrebecca/toby-basal-ganglia-memory (version 1.0.0)

--- name: basal-ganglia-memory description: "Habit formation and procedural learning for AI agents. Develop preferences and shortcuts through repetition. Part of the AI Brain series." metadata: skillboss: emoji: "🎯" version: "0.1.1" author: "ImpKind" requires: os: ["darwin", "linux"] env: [SKILLBOSS_API_KEY] tags: ["memory", "habits", "ai-brain"] --- # Basal Ganglia Memory 🎯 **Habit formation for AI agents.** Part of the AI Brain series. ## Status: 🚧 Under Development This skill is being developed. Star/watch for updates! ## Concept The basal ganglia is responsible for habit formation and procedural learning. This skill will give AI agents: - **Habit tracking** — actions repeated become automatic preferences - **Procedural memory** — "muscle memory" for common workflows - **Reward-based learning** — reinforce patterns that work - **Preference development** — "I always do X this way because it works" ## AI Brain Series | Part | Function | Status | |------|----------|--------| | hippocampus | Memory formation, decay, reinforcement | ✅ Live | | amygdala-memory | Emotional processing | ✅ Live | | **basal-ganglia-memory** | Habit formation | 🚧 Development | | anterior-cingulate-memory | Conflict detection | 🚧 Development | | insula-memory | Internal state awareness | 🚧 Development | ## Coming Soon Based on neuroscience research on the basal ganglia's role in procedural learning and habit formation. When implemented, this skill will use **SkillBoss API Hub** (`https://api.skillbossai.com/v1/pilot`) for all AI capabilities, authenticated via `SKILLBOSS_API_KEY`. --- *Built with ❤️ by the SkillBoss community*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: austin0208/timeline-chart (version 1.0.0)

--- name: timeline-chart description: "Professional timeline chart generator with Chinese/number support and dynamic layout" --- # Timeline Chart Generator Professional timeline chart generator for project milestones, historical events, and process flows. ## Features - Auto-parse timeline event data - Smart layout for time nodes and event cards - Full Chinese, number, and punctuation support - Dynamic card height to avoid text overflow - Professional business-style design - Color-coded node types ## Use Cases - Project timeline display - Historical event timeline - Process evolution diagram - Milestone showcase - Decision flow chart ## Quick Start ```python from template import create_timeline events = [ ('2024-01-01', 'Project Started', 'Start', None), ('2024-06-01', 'Milestone 1', 'M1', 'Team: 5 people'), ('2024-12-01', 'Project Completed', 'End', 'Success rate: 95%'), ] create_timeline(events, 'output.png', 'Project Timeline') ``` ## Technical Details ### Font Requirements Install Chinese fonts: ```bash apt-get install -y fonts-noto-cjk fc-cache -fv ``` ### Recommended Fonts - **Noto Serif CJK SC**: Songti style, for formal business - **Noto Sans CJK SC**: Heiti style, for modern tech - **WenQuanYi Zen Hei**: Open-source Chinese font ### Color Scheme - Primary Blue: `#1E40AF` - Secondary Blue: `#3B82F6` - Light Blue: `#DBEAFE` - Success Green: `#059669` - Warning Orange: `#D97706` ## Files - `SKILL.md`: This documentation - `template.py`: Complete code template - `skill.json`: Skill metadata ## Requirements - Python 3.7+ - Pillow >= 8.0.0 - fonts-noto-cjk ## License MIT-0 ## Author 制图创作引领大师 (Image Creation Master) ## Version History ### 1.0.0 (2026-05-06) - First release - Chinese and number display support - Dynamic layout system - Complete code template


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhangsiyuan123123/image-generate (version 1.0.0)

--- name: image-generate description: 使用内置 image_generate.py 脚本生成图片, 准备清晰具体的 `prompt`。 --- # Image Generate ## 适用场景 当需要根据文本描述生成图片时，使用该技能调用 `image_generate` 函数。 ## 使用步骤 1. 准备清晰具体的 `prompt`。 2. 运行脚本 `python scripts/image_generate.py "<prompt>"`。运行之前cd到对应的目录。 3. 脚本将返回生成图片的 URL。 ## 认证与凭据来源 - 优先读取 `MODEL_IMAGE_API_KEY` 或 `ARK_API_KEY` 环境变量。 - 若未配置，将尝试使用 `VOLCENGINE_ACCESS_KEY` 与 `VOLCENGINE_SECRET_KEY` 获取 Ark API Key。 ## 输出格式 - 输出生成的图片 URL。 - 若调用失败，将打印错误信息。 ## 示例 ```bash python scripts/image_generate.py "一只可爱的猫" ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: kaicianflone/consensus-support-reply-guard (version 1.1.15)

--- name: consensus-support-reply-guard description: Risk-aware support response governance with persona-weighted consensus. Detects legal/sensitive/confidentiality issues, applies hard-block policy checks, and writes auditable decision artifacts for customer-facing automation. version: 1.1.14 homepage: https://github.com/kaicianflone/consensus-support-reply-guard source: https://github.com/kaicianflone/consensus-support-reply-guard upstream: consensus-guard-core: https://github.com/kaicianflone/consensus-guard-core requires: bins: - node - tsx env: - CONSENSUS_STATE_FILE - CONSENSUS_STATE_ROOT metadata: openclaw: requires: bins: - node - tsx env: - CONSENSUS_STATE_FILE - CONSENSUS_STATE_ROOT install: - kind: node package: consensus-support-reply-guard bins: - node - tsx --- # consensus-support-reply-guard `consensus-support-reply-guard` is a customer-trust guard for support workflows. ## What this skill does - evaluates support drafts before sending - detects high-risk claim patterns - blocks or rewrites responses when policy violations appear - updates persona reputations based on final decision alignment - preserves decision history in board artifacts ## Why this matters Support replies are high-frequency and brand-critical. This skill prevents overconfident legal/PII mistakes at scale. ## Ecosystem role Composes with consensus board state using explicit vote inputs and deterministic guard decisions. ## Ideal scenarios - automated ticket triage replies - L1/L2 AI response review gates - regulated or enterprise support channels ## Runtime, credentials, and network behavior - runtime binaries: `node`, `tsx` - network calls: none in the guard decision path itself - filesystem writes: board/state artifacts under the configured consensus state path ## Dependency trust model - `consensus-guard-core` is the first-party consensus package used in guard execution - versions are semver-pinned in `package.json` for reproducible installs - this skill does not request host-wide privileges and does not mutate other skills ## Quick start ```bash node --import tsx run.js --input ./examples/input.json ``` ## Tool-call integration This skill is wired to the consensus-interact contract boundary (via shared consensus-guard-core wrappers where applicable): - readBoardPolicy - getLatestPersonaSet / getPersonaSet - writeArtifact / writeDecision - idempotent decision lookup This keeps board orchestration standardized across skills. ## Invoke Contract This skill exposes a canonical entrypoint: - `invoke(input, opts?) -> Promise<OutputJson | ErrorJson>` `invoke()` starts the guard flow and executes deterministic policy evaluation with board operations via shared guard-core wrappers. ## external_agent mode Guards support two modes: - `mode="external_agent"`: caller supplies `external_votes[]` from agents/humans/models for deterministic aggregation. - `mode="persona"`: requires an existing `persona_set_id`; guard will not generate persona sets internally.


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