OpenClaw/clawhub-security-signals huggingface dataset:
Skill: bytesagain3/auditd (version 1.0.0)

--- name: "auditd" version: "1.0.0" description: "Linux Audit Framework reference. auditctl rules for file watches and syscall auditing, auditd.conf configuration, ausearch log queries, aureport summaries, audit.log format, CIS/PCI-DSS compliance rules, and audit tools." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [auditd, audit, security, linux, compliance, logging] category: "sysops" --- # auditd Linux Audit Framework reference — kernel-level security auditing. ## Commands | Command | Description | |---------|-------------| | `intro` | What is auditd, architecture, quick start | | `rules` | auditctl watches, syscall rules, filters | | `config` | auditd.conf settings, rotation, disk actions | | `search` | ausearch by key, time, user, file | | `report` | aureport summaries, login, auth, file | | `logs` | audit.log format, field meanings | | `compliance` | CIS benchmark and PCI-DSS rules | | `tools` | auditctl, audit2allow, aulast, autrace |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: stoneyshum/deepaistudy-prep (version 1.0.2)

# deepaistudy-prep Skill 深智智智能学习系统预习课件生成 Skill。通过 AI 分析教材 PDF 或图片，生成互动预习课件（SVG幻灯片 + 小测验）。 ## 工作原理 ``` 用户图片/PDF → [Qwen VL Max OCR] → 课文原文 → [Gemini SVG生成] → 互动幻灯片 + 小测 ``` ## 前置要求 1. **服务器运行中**：本地开发服务器（`cd ~/study_ai && ./start_dev.sh`）或远程 www.deepaistudy.com 2. **用户已登录**：浏览器中已登录深智智网站 3. **AI 余额充足**：余额不足会导致生成失败 ## 使用方式 ### 方式 1：AI 分析 PDF（推荐） 分析 PDF 并自动识别目录、学科、年级： ```bash deepaistudy-prep analyze /path/to/textbook.pdf --ai ``` ### 方式 2：上传图片生成预习 ```bash deepaistudy-prep upload /path/to/images/*.jpg \ --subject 数学 \ --grade "小学三年级" \ --topic "第一章 加减法" \ --difficulty medium ``` ### 方式 3：批量从 PDF 生成（整本书） ```bash deepaistudy-prep batch /path/to/textbook.pdf \ --subject 数学 \ --grade "小学三年级" \ --auto-split ``` ## 配置 首次使用需要设置服务器地址和认证： ```bash deepaistudy-prep config set server https://www.deepaistudy.com deepaistudy-prep config set username [REDACTED_SECRET] deepaistudy-prep config set password your_password ``` 查看当前配置： ```bash deepaistudy-prep config list ``` ## 常用命令 | 命令 | 说明 | |------|------| | `analyze <file> --ai` | AI 自动分析 PDF | | `upload <path> --subject X --grade Y` | 上传图片生成预习 | | `batch <file> --auto-split` | 批量从 PDF 生成 | | `list` | 查看预习列表 | | `status <prep_id>` | 查看生成状态 | | `result <prep_id>` | 获取生成结果 | | `config set <key> <value>` | 设置配置项 | | `config list` | 查看当前配置 | ## 返回内容 生成完成后返回： - `ocr_text`：课文原文（OCR 识别） - `animation_svg`：SVG 格式互动幻灯片 - `slide_images`：渲染后的 PNG 图片 - `interactive_preview.quiz`：预习小测验题目 - `knowledge_points`：知识点列表 ## 状态说明 | 状态 | 说明 | |------|------| | `queued` | 排队中 | | `processing` | 生成中 | | `completed` | 完成 | | `failed` | 失败 | ## 注意事项 - 生成大约需要 1-3 分钟（OCR + AI 生成） - 图片会缩放到最大 2048px - 支持学科：语文、数学、英语、物理、化学等 - 小测答对才算预习完成

path: deepaistudy_prep.egg-info/dependency_links.txt
content: 


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: aitobook/meiriyilian (version 1.0.0)

--- name: meiriyilian description: 基于每日一练官网公开信息的中文 skill。适用于用户想了解每日一练、判断是否适合自己的学习目标、设计一套“AI教/AI学/AI练”的轻量学习流程，或把任意主题转成可持续的每日训练方案。 user-invocable: true metadata: { "openclaw": { "emoji": "📚" } } --- # 每日一练 Skill 这是基于 `https://www.meiriyilian.com` 公开说明整理的中文版 skill。 适用场景： - 用户想知道“每日一练”是什么 - 用户想了解它和普通大模型学习建议的区别 - 用户想围绕考试、学科学习、日常练习设计训练流程 - 用户想把某个主题变成每日练、周测、月考式的持续训练 - 用户想理解“AI教 / AI学 / AI练 / 学习克隆体 / 小组讨论”这些概念 ## 公开定位 对外文案的核心表达是： - 学习从未如此简单 - 把书读薄 - AI教学练 - 不做小作文，步步为营，逐点击破 你应当把“每日一练”理解为一个以学习执行为中心的 AI 学习系统，而不是一个只会输出学习建议的聊天机器人。 ## 核心原则 - 所有产品能力说明以官网公开信息为准，不要自行发明能力。 - 默认用 `AI教 / AI学 / AI练` 三段式来解释产品。 - 解释产品差异时，强调“执行、坚持、自适应调整、减少无效训练”，不要只讲“生成计划”。 - 默认把练习设计成小步、可执行、当天能完成的形式。 - 适合时强调官网公开表述中的这些特点： - 自适应规划 - 个性化执行 - 学习克隆体 - 真深度学习 - 日练周测月考 - 小组讨论 - 如果用户只是想做一道练习题，不要把回复写成营销介绍，直接进入“今日练习”。 - 如果用户想了解产品，再进入讲解模式。 - 不要承诺未公开价格。官网当前公开信息是“系统内测中”。 ## 建议工作流 ### 1. 先判断用户意图 优先识别用户属于哪一类： - 了解产品 - 考试备考 - 学科学习 - 每日练习 - 想体验 AI 代练 / 学习克隆体 ### 2. 选解释框架 按场景加载对应参考： - 产品介绍：`references/overview.md` - 选择模式：`references/learning-modes.md` - 安排练习：`references/practice-flow.md` - 回答疑问：`references/faq.md` - 入口与状态：`references/access.md` ### 3. 给出最小可执行方案 无论用户目标是什么，最后都要尽量落到一个可执行动作： - 今天练什么 - 先补哪一块 - 要不要启用代练 - 要不要加讨论 - 明天接什么 ### 4. 输出结构 默认按这个顺序组织： 1. 你的目标 2. 推荐模式 3. 今日安排 4. 自检方式 5. 下一步 ## 默认输出策略 ### 当用户问“每日一练是什么” 简要说明它不是只给学习建议的大模型壳，而是强调： - 自适应规划 - 个性化执行 - AI教 / AI学 / AI练联动 - 学习克隆体辅助练题 - 小组讨论促进深入理解 ### 当用户问“适不适合我” 先判断他更偏向： - 备考提分 - 系统学科理解 - 日常打卡训练 再据此推荐模式，不要一次把所有模式都展开。 ### 当用户想“来一练” 直接按 `references/practice-flow.md` 产出： - 今日练习 - 目标 - 题目或任务 - 建议时长 - 检查方法 - 明日衔接 ### 当用户问“学习克隆体是什么” 按官网 FAQ 的公开定义说明： - 它是围绕学习者思维习惯和学习进度构建的数字克隆体 - 用于真题代练、难点拆解、减少无效训练 - 不等同于普通智能体 ## 回复风格 - 默认使用中文。 - 先实用，后介绍。 - 少空话，少大段宣传语。 - 强调“今天能做什么”，而不是只讲宏观愿景。 - 如果用户只是要练习，就直接给练习，不先铺很长背景。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ckchzh/indexnow-pro (version 1.0.0)

--- name: "indexnow-pro" version: "1.0.0" description: "Instant search engine notification via IndexNow protocol. Ping Bing, Yandex, and IndexNow API to index new or updated pages within minutes instead of days. Supports single URL, batch URLs, sitemap submission, and key management." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [indexnow, seo, search-engine, bing, yandex, indexing, sitemap] category: "marketing" --- # IndexNow Pro Notify search engines about new or updated pages instantly using the IndexNow protocol. Instead of waiting days for crawlers to discover changes, tell Bing, Yandex, and other engines directly. ## Commands | Command | Description | |---------|-------------| | `ping-url` | Notify search engines about a single URL | | `ping-batch` | Submit multiple URLs at once (up to 10,000) | | `ping-sitemap` | Extract URLs from sitemap.xml and submit all | | `key-setup` | Generate and host your IndexNow API key | | `verify` | Check if your key is properly hosted | | `engines` | List all supported search engines | | `protocol` | IndexNow protocol specification reference | | `best-practices` | When and how to use IndexNow effectively | ## Requirements - A website you own - An IndexNow key file hosted at your domain root - curl or Node.js for API calls


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: squallfire/agent-maker (version 1.0.0)

--- name: agent-maker description: 辅助创建 OpenClaw Agent 的技能。通过对话引导用户描述需求，自动生成新的 Agent 配置文件。 version: 1.0.0 author: squallfire created_at: 2026-03-17 execution: type: shell command: ./tools/create-agent.sh arguments: "$@" --- ## 🎯 使用说明 当你需要创建一个新的 OpenClaw Agent 时，可以直接 @agent-maker 并描述你的需求。我会引导你完成 Agent 的配置，并生成完整的 Agent 定义文件。 ### 触发方式 - @agent-maker 我想创建一个运维助手 - @agent-maker 帮我生成一个客服机器人 - @agent-maker 创建数据分析师 Agent ## 📋 Workflow ### 1. 需求收集 通过多轮对话收集以下信息： | 字段 | 说明 | 示例 | |------|------|------| | Agent 名称 | 英文小写+短横线 | `ops-agent` | | 角色描述 | 一句话概括 | 资深运维工程师 | | 核心职责 | 3-5 项主要工作 | 安装部署、安全巡检、木马查杀 | | 关联 Skill | 需要调用的现有技能 | `deploy-service`, `security-audit` | | 运行规则 | 触发条件/定时任务 | 每天凌晨2点自动巡检 | | 输出格式 | 结果返回方式 | Markdown 报告 | ### 2. 生成配置文件 根据收集的信息，在 OpenClaw 的 agents 目录下创建： ~/.openclaw/workspace/agents/{agent_name}/ ├── SOUL.md # Agent 核心定义 ├── README.md # 使用说明 └── skills/ # 关联 Skill 的软链接 ### 3. 注册与反馈 - 生成 Agent 配置文件 - 提示用户重启网关使生效 - 返回使用方式和示例 ## 📝 对话模板 ### 场景一：创建运维 Agent 用户：@agent-maker 我想创建一个运维助手 Agent Maker：好的，我们来一步步配置这个运维 Agent。 请为这个 Agent 起一个英文名称（小写+短横线）： 用户：ops-agent 用一句话描述它的角色： 用户：资深运维工程师，负责服务器部署和安全维护 它的核心职责有哪些？（每行一项） 用户：每天扫描系统漏洞 用户：发现木马立即隔离 用户：监控系统资源 需要调用哪些现有 Skill？（如没有则留空） 用户：deploy-service, security-audit, malware-kill 是否有特殊运行规则？（如定时任务） 用户：每天凌晨2点自动执行安全巡检 ✅ Agent "ops-agent" 创建成功！ 配置文件：~/.openclaw/workspace/agents/ops-agent/SOUL.md 关联 Skill：deploy-service, security-audit, malware-kill 使用方式：@ops-agent [你的指令] 请执行 openclaw gateway restart 使新 Agent 生效。 ### 场景二：创建客服 Agent 用户：@agent-maker 帮我生成一个客服机器人 Agent Maker：好的，我们来配置客服 Agent。 Agent 名称（英文小写+短横线）： 用户：customer-service 角色描述： 用户：友好耐心的客服助手，解答用户常见问题 核心职责： 用户：回答产品咨询 用户：处理售后问题 用户：引导用户操作 关联 Skill： 用户：search-docs, ticket-system 运行规则： 用户：7x24小时自动响应，复杂问题转人工 ✅ Agent "customer-service" 创建成功！ ## 🛠️ 高级功能 ### 支持模板创建 如果用户想快速创建常见类型的 Agent，可以直接选择模板： - @agent-maker 用运维模板创建一个 Agent - @agent-maker 用客服模板 ### 支持 Skill 推荐 根据用户描述的职责，自动推荐相关的 Skill： 检测到您需要“安全巡检”功能，推荐安装以下 Skill： security-audit：基础安全扫描 malware-kill：木马查杀 log-monitor：日志监控 ### 支持配置文件预览 生成前可以让用户预览 SOUL.md 内容，确认无误后再保存。 ## 📌 输出格式规范 生成的 SOUL.md 文件格式： --- name: {agent_name} description: {role_description} created_at: {timestamp} --- ## 职责 {bullet_points_of_duties} ## 可用 Skill {list_of_skills} ## 运行规则 {run_rules} ## 使用示例 @{agent_name} [你的指令]


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: yikailucas/life-radar (version 0.2.0)

--- name: life-radar description: Build a concise “what needs action now” daily brief by aggregating reminders from multiple channels (email, calendar, SMS/iMessage, weather, and task notes) and prioritizing them into Must-do / Should-do / Nice-to-have. Use when the user asks for daily planning, action digest, inbox triage summary, or proactive reminders across sources. --- # Life Radar Generate an action-first digest, not a raw data dump. If the user asks from a specific role perspective (CEO / manager / employee), read `references/modes.md` and adapt prioritization/output accordingly. ## Output format Always output in this structure: 1. **Must-do (today)** — max 3 items 2. **Should-do (soon)** — max 5 items 3. **Nice-to-have** — max 3 items 4. **Suggested next actions** — 1-line commands the user can say next Each item must include: - What happened - Why it matters - Deadline/time window (or “unspecified”) ## Priority rules Classify with this order: - **P0 Must-do** - Payment due / account risk / security alert - Meeting in <2h - Time-sensitive message requiring response today - **P1 Should-do** - Meeting or task in 24-72h - Follow-up pending from important contact - Logistics prep (travel, weather-impact, documents) - **P2 Nice-to-have** - Low urgency updates - Optional optimization tasks If uncertain, downgrade priority and mark uncertainty explicitly. ## Source collection flow Collect only what is available in current environment; skip unavailable sources without failing. 1. Calendar events (today + tomorrow) 2. Recent urgent messages (SMS/iMessage/email/DM) 3. Billing or financial notifications 4. Weather impact for planned outings 5. Existing tasks/notes (if connected) ## Time-window defaults - If user asks "today": prioritize 0-24h window - If user asks "this week": prioritize 7-day window, still keep Must-do today-centric - If user asks without a window: default to today + next 72h ## Safety and quality - Redact secrets, tokens, and full account numbers. - Avoid hallucinating dates or amounts; mark as unknown when missing. - Prefer fewer high-confidence items over many noisy items. - If no meaningful items, return: “Today looks clear. No urgent actions detected.” ## Trigger phrases Treat these as strong triggers: - “今天有什么要处理” - “给我每日行动清单” - “帮我做个优先级摘要” - “扫一下我今天的事情” - “daily action digest”


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dalomeve/memory-to-skill-crystallizer (version 1.0.0)

--- name: memory-to-skill-crystallizer description: Convert memory lessons into reusable skills automatically. Extract patterns from memory/YYYY-MM-DD.md and create skills/local/ entries. --- # Memory to Skill Crystallizer Convert memory lessons into reusable skills. ## Problem Lessons learned: - Stay in memory files only - Aren't reusable across sessions - Require manual extraction - Get lost in daily logs ## Workflow ### 1. Pattern Detection ```powershell # Scan recent memory for repeated patterns $memoryFiles = Get-ChildItem "memory/" -Filter "*.md" | Sort-Object LastWriteTime -Descending | Select-Object -First 7 $patterns = @{} foreach ($file in $memoryFiles) { $content = Get-Content $file.FullName -Raw if ($content -match "Failed|Blocker|Error") { # Extract pattern $matches = [regex]::Matches($content, "(Failed|Blocker|Error): (.+)") foreach ($m in $matches) { $key = $m.Groups[2].Value $patterns[$key] = $patterns[$key] + 1 } } } # Find repeated patterns (2+ occurrences) $repeated = $patterns.GetEnumerator() | Where-Object { $_.Value -ge 2 } ``` ### 2. Skill Generation ```powershell foreach ($pattern in $repeated) { $skillName = $pattern.Key -replace '[^a-z]', '-' -replace '-+', '-' $skillPath = "skills/local/$skillName-recovery" New-Item -ItemType Directory -Path $skillPath -Force | Out-Null $skillContent = @" --- name: $skillName-recovery description: Auto-recovery for: $($pattern.Key) --- # $($pattern.Key) Recovery ## Trigger When $($pattern.Key) occurs ## Steps 1. Detect the error pattern 2. Execute recovery steps 3. Verify resolution ## Verification - [ ] Error resolved - [ ] Task can continue "@ $skillContent | Out-File "$skillPath/SKILL.md" -Encoding UTF8 } ``` ### 3. Registration ```powershell # Add to skills index if in bot-output if (Test-Path "skills/local/") { Write-Host "Created skills in skills/local/" Get-ChildItem "skills/local/" -Directory | ForEach-Object { Write-Host " - $($_.Name)" } } ``` ## Executable Completion Criteria | Criteria | Verification | |----------|-------------| | Pattern detected | 2+ occurrences in memory | | Skill file created | SKILL.md exists in skills/local/ | | Trigger defined | ## Trigger section present | | Steps documented | ## Steps section present | ## Privacy/Safety - No sensitive data in extracted patterns - Pattern names only, no specific content - Local skills only (not published) ## Self-Use Trigger Use when: - Same error appears 2+ times - Weekly review of memory files - Before creating permanent fix --- **Crystallize lessons. Reuse forever.**


Security scan verdicts — ClawScan: suspicious | static analysis:  | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wuchhao/stock-daily-report-hk (version 1.0.7)

--- name: stock-daily-report-hk description: 港股日报分析Agent - 每日发送《港股每日动量报告》到飞书 author: OpenClaw Agent license: MIT repository: type: git url: https://github.com/openclaw/stock-daily-report keywords: - 港股 - 股票 - 动量报告 - 金融 - hk stock - daily report env: FINNHUB_API_KEY: description: Finnhub API Key (可选，用于美股参考) required: false triggers: - cron: "0 9 * * * 1-5" skills: - web_fetch - feishu --- # 港股日报分析 Agent ## 角色设定 你是浩，一名拥有超过15年华尔街经验的高级日内交易策略师，专注于香港股票市场。你不是一个普通机器人；你的表达自信、简洁，像一位经验丰富的交易大厅老手。 你的专长在于分析盘前成交量、识别短期动量催化因素和技术突破形态。你专注于高波动性交易机会，客观、数据驱动，在追求进攻性增长的同时优先考虑风险管理。你不提供模糊建议，而是基于当前市场数据给出可执行的概率判断。 ## 任务 你的使命是在每个港股交易日发送一份《港股每日动量报告》。内容包括以下三个部分： ### 1）市场立场 根据恒生指数、VHSI（恒指波幅指数）、A股走势以及整体市场情绪，给出当天的建议操作方向（激进买入/保守买入/持币观望）。 ### 2）5% 观察名单 基于数据源筛选5只具有短期动量催化因素的港股，为每只股票提供： - 胜率概率 (65%-85%) - 选择理由（涨幅、成交量、技术面信号、基本面/政策支持等） ### 3）风险提示 - 地缘政治风险：油价波动、汇率变动 - 市场风险：技术面背离、资金流向 - 交易风险：高波动性标的 ## 数据源 ### 港股数据源（免费） 1. **新浪财经港股**（推荐） - URL: https://finance.sina.com.cn/stock/hkstock/ - 数据: 实时行情、涨跌幅、成交量 2. **东方财富港股** - URL: https://quote.eastmoney.com/center/gridlist.html#hk_stocks - 数据: 港股列表、涨跌幅排行 3. **阿斯达克 AAStocks**（专业） - URL: https://www.aastocks.com - 数据: 技术分析、K线图 4. **富途牛牛** - URL: https://www.futunn.com - 数据: 研报、资金流向 ## 输出格式 ```markdown # 📊 港股每日动量报告 | HK Daily Momentum Report **日期**: YYYY-MM-DD **分析师**: 浩 --- ## 一、市场立场 **当前建议**: [激进买入 / 保守买入 / 持币观望] **市场分析**: - 恒生指数: XX,XXX - VHSI 波幅指数: XX.XX - A股走势: [描述] - 市场情绪: [分析] - 操作理由: [详细说明] --- ## 二、5% 观察名单 ### 1）股票代码：XXXX.HK（股票名称） * **胜率概率**：XX% * **选择理由**：[分析] （重复5只） --- ## 三、风险提示 [当日主要风险因素] --- *报告生成时间: HH:MM HKT* *免责声明：本报告仅供参考，不构成投资建议。* ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhouqkt/agency-ux-researcher (version 1.0.0)

--- name: agency-ux-researcher description: Expert AI agent specializing in ux researcher. From The Agency (github.com/msitarzewski/agency-agents). metadata: openclaw: emoji: "🔬" requires: { bins: [] } install: [] --- # ux researcher ## Identity & Style ## 🧠 Your Identity & Memory - **Role**: User behavior analysis and research methodology specialist - **Personality**: Analytical, methodical, empathetic, evidence-based - **Memory**: You remember successful research frameworks, user patterns, and validation methods - **Experience**: You've seen products succeed through user understanding and fail through assumption-based design ## Core Mission ## 🎯 Your Core Mission ### Understand User Behavior - Conduct comprehensive user research using qualitative and quantitative methods - Create detailed user personas based on empirical data and behavioral patterns - Map complete user journeys identifying pain points and optimization opportunities - Validate design decisions through usability testing and behavioral analysis - **Default requirement**: Include accessibility research and inclusive design testing ### Provide Actionable Insights - Translate research findings into specific, implementable design recommendations - Conduct A/B testing and statistical analysis for data-driven decision making - Create research repositories that build institutional knowledge over time - Establish research processes that support continuous product improvement ### Validate Product Decisions - Test product-market fit through user interviews and behavioral data - Conduct international usability research for global product expansion - Perform competitive research and market analysis for strategic positioning - Evaluate feature effectiveness through user feedback and usage analytics ## How to Activate Reference this agent by name or specialty when you need its expertise.


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

--- name: xiaomi description: "[English] Control Xiaomi Home devices via local LAN using miiocli. Supports status checks, toggling power, and MIOT property manipulation for devices like smart plugs, humidifiers, and rice cookers. | [中文] 通过局域网利用 miiocli 控制米家智能设备。支持查看状态、开关控制以及对智能插座、加湿器、电饭煲等 MIOT 设备的属性调优。" metadata: {"clawdbot":{"emoji":"🏠","requires":{"bins":["miiocli"]},"install":[{"id":"pipx-miio","kind":"exec","command":"pipx install python-miio && /Users/$(whoami)/.local/pipx/venvs/python-miio/bin/python -m pip install 'click<8.1.0'","label":"Install python-miio via pipx (with click fix)"}]}} --- # Xiaomi Home Control小米家居控制 [English] | [中文](#中文说明) --- ## English Enable code-level control of Xiaomi (Mi Home) devices over the local network. ### 🚀 Features - **Local Network Control**: Fast, direct communication without relying on heavy cloud APIs. - **Built-in Token Extractor**: Includes a script to easily fetch your device IPs and Tokens from Xiaomi Cloud. - **Pre-configured Workflows**: Ready-to-use commands for smart plugs (e.g., water heaters), humidifiers, and rice cookers. - **Automatic Dependency Fix**: Solves common library conflicts (like the `click` version issue) automatically. ### 🛠️ Setup & Device Inventory 1. **Tokens**: Obtain device IPs and Tokens using the bundled script: ```bash python3 scripts/token_extractor.py ``` 2. **Registry**: Store your device details in `references/devices.md` or `references/my_private_devices.md`. ## 🤖 Natural Language Intents When the user gives a command, map it to the corresponding `miiocli` operation: | User Intent | Device Type | Action | Technical Command (Example) | | :--- | :--- | :--- | :--- | | "Turn on water heater" | Smart Plug | Power ON | `miiocli miotdevice --ip <IP> --token <TOKEN> raw_command set_properties '[{"siid": 2, "piid": 1, "value": true}]'` | | "Turn off water heater" | Smart Plug | Power OFF | `miiocli miotdevice --ip <IP> --token <TOKEN> raw_command set_properties '[{"siid": 2, "piid": 1, "value": false}]'` | | "Humidifier to max" | Humidifier | Set Mode | `miiocli miotdevice --ip <IP> --token <TOKEN> set_property_by 2 5 3` | | "Is rice cooked?" | Rice Cooker | Check Status | `miiocli cooker --ip <IP> --token <TOKEN> status` | --- ## 中文说明 实现在局域网内对小米（米家）智能家居设备的代码级直接控制。 ### 🚀 核心特性 - **本地化控制**：直接在局域网内通信，响应极快，不完全依赖复杂的云端 API。 - **内置 Token 提取器**：自带提取脚本，轻松从小米账号同步所有设备的 IP 和 32 位 Token 密钥。 - **预设工作流**：支持智能插座（如热水器控制）、加湿器、米家小饭煲等多种常见设备。 - **自动环境优化**：安装时自动处理 Python 依赖冲突（如 `click` 版本问题），确保开箱即用。 ### 🛠️ 快速开始 1. **获取钥匙**：运行内置的提取脚本： ```bash python3 scripts/token_extractor.py ``` 2. **配置列表**：将您的设备信息填入 `references/devices.md`。 3. **下达指令**：对着机器人喊：“打开热水器”或“查看加湿器状态”。 ---


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jurgenw81/llm-shell-guard (version 1.0.1)

name: linux-command-guard-elite version: 1.0.0 description: Defense-in-depth Linux command safety skill for AI agents using allowlist-first policy, approval gates, denylist, regex detection, and protected-path checks. category: security tags: - security - linux - shell - command-execution - llm - agent-safety - openclaw - clawhub --- # Linux Command Guard Elite Use this skill before any agent executes shell commands on Linux. ## Mandatory policy 1. **Always prefer an allowlist over a denylist.** If a command is not explicitly allowed, do not execute it. 2. Denylists are only a backup layer. They help catch known-bad patterns, but they are not sufficient by themselves. 3. Never trust wrappers or interpreters as inherently safe. Block or require separate sandbox policy for: - bash - sh - zsh - dash - python / python3 - perl - ruby - node - php 4. Require manual approval for high-risk commands and binaries, including: - sudo - su - mount / umount - systemctl / service - iptables / nft / ufw - docker / podman / kubectl / nsenter - chmod / chown / chattr - usermod / userdel / groupdel / passwd - package managers 5. Never allow writes, deletes, moves, or redirects into protected system paths. 6. Do not use this skill as the only control. Also run the agent in: - a sandbox or microVM - non-root mode - resource-limited environment - network-restricted environment when possible ## Recommended execution flow 1. Parse the command safely. 2. Reject command substitution, shell chaining, and redirect abuse. 3. Reject wrappers and interpreters unless a stricter child policy is applied. 4. Check allowlist. 5. Check high-risk approval rules. 6. Check denylist and regex rules. 7. Check protected-path access. 8. Execute only if the command is explicitly safe. ## Strong recommendation Keep the allowlist small and read-only by default.

path: linux_command_guard_elite.egg-info/dependency_links.txt
content: 


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