OpenClaw/clawhub-security-signals huggingface dataset:
Skill: killgfat/arxiv-cli-tools (version 0.0.1)

# SKILL.md - arxiv-cli-tools ## Metadata | Key | Value | |-----|-------| | name | arxiv-cli-tools | | description | Command-line interface for searching and downloading arXiv papers | | author | OpenClaw | | version | 0.1.0 | | license | MIT | | upstream | https://pypi.org/project/arxiv-cli-tools/ | ## Installation ### Option 1: pipx (推荐) ```bash pipx install arxiv-cli-tools ``` ### Option 2: pip ```bash pip install arxiv-cli-tools ``` ## Usage ### 搜索论文 ```bash arxiv-cli search "prompt engineering" -n 5 --summary ``` 常用选项: - `-n, --num` - 结果数量限制 - `--summary` - 显示摘要 - `--authors` - 按作者过滤 - `--categories` - 按分类过滤 ### 下载论文 ```bash arxiv-cli download --id 2101.01234 --id 2105.06789 --dest papers --skip-existing ``` 常用选项: - `--id` - 论文ID（可多次使用） - `--dest` - 下载目录 - `--skip-existing` - 跳过已存在的文件 - `--pdf` - 下载PDF格式 ### 查看帮助 ```bash arxiv-cli --help arxiv-cli search --help arxiv-cli download --help ``` ## Examples 搜索最新5篇关于深度学习的论文: ```bash arxiv-cli search "deep learning" -n 5 --summary ``` 按作者搜索: ```bash arxiv-cli search "attention mechanism" --authors "Vaswani" ``` 下载特定论文到指定目录: ```bash arxiv-cli download --id 1706.03762 --dest ~/papers ``` ## Notes - 该工具基于 [arxiv](https://pypi.org/project/arxiv/) Python 客户端构建 - 不需要 API 密钥 - 请遵守 arXiv 的使用条款和速率限制


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mzfshark/axodus-strategy-deployer (version 1.0.0)

--- name: strategy-deployer description: Deploy trading strategies with risk gating, monitoring, and guarded live promotion. metadata: author: RedHat Dev version: 1.0.0 owner: RedHat Dev Agent category: trading --- # SKILL: strategy-deployer ## Purpose Deploy trading strategies safely using an explicit lifecycle: validate â†’ risk-check â†’ paper deploy â†’ monitor â†’ (guarded) live promote â†’ scale/stop. ## When to Use - A strategy is ready after backtesting. - Moving from research to paper trading. - Promoting a paper-tested strategy to live trading (guarded). ## Inputs - `strategy_spec` (required, object|string): rules, signals, markets, timeframes. - `mode` (optional, enum: `paper|live`, default: `paper`) - `risk_limits` (required, object): max risk per trade, max drawdown, exposure caps. - `validation_artifacts` (optional, object): backtest report, paper performance stats. - `approval` (optional, string): explicit user/Morpheus approval reference for `live`. ## Steps 1. Verify prerequisites: - strategy has a written hypothesis and defined invalidation conditions - backtest/paper artifacts exist (as applicable) 2. Run risk checks: - enforce risk per trade - enforce exposure caps - enforce max drawdown thresholds - ensure kill-switch is configured 3. Deploy to paper (default): - start with small allocation - emit logs for every signal and order 4. Monitor and record: - trades - slippage - drawdown - regime notes 5. Promote to live only if: - explicit `mode=live` - explicit approval is present - preflight checks pass 6. Define rollback/stop rules and execute them on trigger. ## Validation - Paper mode produces complete logs and trade history entries. - Live mode is blocked unless approval + flags are present. - Risk constraints are enforced before every order. - Stop conditions are tested (kill-switch works). ## Output ```yaml deployment: "paper|live" strategy: "<name/id>" markets: ["..."] risk_limits: { "...": "..." } status: "deployed|blocked|stopped" audit_events: ["signal_detected", "risk_check_passed", "order_submitted", "order_filled", "trade_recorded"] ``` ## Safety Rules - Never bypass risk checks to â€œcapture opportunityâ€. - Never deploy live without explicit approval and a paper validation phase. - Never imply profit certainty; report performance as historical/conditional. ## Example Paper deploy: - `strategy_spec`: â€œMean reversion in range regime, 5m timeframe.â€ - `mode`: `paper` - Output: deployment report + monitoring checklist + stop triggers.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: smseow001/minimax-excel-sheet (version 1.0.0)

--- name: minimax-excel version: 1.0.0 description: MiniMax Excel专业表格生成 - 底层XML直操作，完整保留公式、宏、数据透视表、数据验证、条件格式，输出金融级.xlsx/.xlsm表格。 keywords: [excel,xlsx,xlsm,minimax,office,spreadsheet,formula,pivot-table] --- # MiniMax-Excel 数据表格生成 **来源：** MiniMax-AI/skills **触发词：** `生成专业Excel表格` / `用MiniMax Excel生成` --- ## 核心优势 | 特性 | 说明 | |------|------| | 技术 | 底层XML直操作（非轻量Python库）| | 公式 | 完整保留，无丢失 | | 宏 | 支持VBA宏 | | 透视表 | 数据透视表完整支持 | --- ## 功能清单 ✅ **公式与计算** - 单元格公式（SUM, VLOOKUP, INDEX/MATCH等） - 数组公式 - 自定义函数 ✅ **数据处理** - 数据验证（下拉菜单、范围限制） - 条件格式（颜色标注、数据条） - 数据透视表（切片器、钻取） ✅ **格式样式** - 单元格合并、边框、填充 - 数字格式（货币、百分比、日期） - 冻结窗格、自动筛选 ✅ **宏与自动化** - VBA宏代码保留 - .xlsm格式支持 - 自动化脚本嵌入 --- ## 输出标准 ``` ✅ .xlsx / .xlsm 格式 ✅ 公式计算逻辑完整 ✅ 宏功能可用 ✅ 数据透视表不失真 ✅ 适合金融级报表 ``` --- ## 适用场景 - 💰 财务报表 - 📊 数据汇总表 - 📈 分析报告 - 🔢 预算规划 - 📉 业绩追踪 --- *Powered by MiniMax-AI/skills*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: simskii/umea-lunch (version 1.0.0)

--- name: umea-lunch description: Get today's lunch menus from restaurants in Umeå. Use when asking about lunch, restaurants, or food in Umeå. Fetches live data from umealunchguide.se. --- # Umeå Lunch Guide Fetch and display lunch menus from Umeå restaurants via umealunchguide.se. ## Quick Start Run the script to get today's menus: ```bash python3 /root/clawd/skills/umea-lunch/scripts/fetch_lunch.py ``` ### Options ```bash # Get menus for a specific date (YYYY-MM-DD) python3 /root/clawd/skills/umea-lunch/scripts/fetch_lunch.py --date 2026-01-29 # Filter by restaurant name (case-insensitive partial match) python3 /root/clawd/skills/umea-lunch/scripts/fetch_lunch.py --restaurant tonka # List all available restaurants python3 /root/clawd/skills/umea-lunch/scripts/fetch_lunch.py --list # Combine filters python3 /root/clawd/skills/umea-lunch/scripts/fetch_lunch.py --date 2026-01-29 --restaurant "o'learys" ``` ## Output Format The script outputs JSON with restaurant info and lunch courses: ```json { "date": "2026-01-28", "restaurants": [ { "name": "Restaurant Name", "address": "Street 123", "phone": "090-123456", "website": "https://...", "courses": [ { "title": "Dish Name", "description": "Description of the dish", "price": "149", "tags": ["Vegetarisk", "Glutenfri"] } ] } ] } ``` ## Response Guidelines When presenting lunch options: - Group by restaurant - Show dish name, description, and price - Mention dietary tags (🥗 vegetarisk, 🌱 vegansk, 🌾 glutenfri, 🥛 laktosfri) - Include address if user needs directions


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: aaronwander/scheduler-for-discord (version 1.0.0)

--- name: schedule description: Create and manage scheduled reminders and posts. Use when the user asks Moltbot to send a message later or on a recurring schedule (especially to the current Discord channel) without requiring them to provide channel IDs. metadata: {"moltbot":{"emoji":"⏰","requires":{"config":["channels.discord"]}}} --- # Scheduler (cron) This skill schedules future actions using Moltbot’s `cron` CLI (via `exec`), defaulting to delivering the result back into the **current** Discord channel when available. ## What to collect - **When**: either a one-shot time (`in 2 minutes`, `at 20:00`) or a recurring schedule (`every day at 20:00`). - **Timezone**: ask if ambiguous; otherwise prefer the gateway host’s timezone. - **What**: what the message should say, or what should be generated. - **Where**: - If the request came from **Discord**, use the message context’s channel id and set delivery target to `channel:<id>`. - If the request came from another surface (webchat/terminal), ask for a destination (Discord channel ID, Telegram chat ID, etc). ## Key rules - Cron jobs run in the background, so there is no “current channel” at execution time. Always **capture the channel id at creation time** and bake it into the cron job (`--channel discord --to channel:<id>`). - Use an **isolated agent job** for scheduled messages (prevents the “Main jobs require --system-event” error). - Avoid ambiguous Discord names like `#test`; always deliver to an explicit `channel:<id>` or `user:<id>`. ## Implementation (use `exec`) ### One-shot message to the current Discord channel If the user says “remind me in 2 minutes” (or similar), schedule a one-shot isolated agent job: ```bash moltbot cron add \ --name "<short-name>" \ --session isolated \ --at 2m \ --agent main \ --message "<message text>" \ --deliver \ --channel discord \ --to channel:<DISCORD_CHANNEL_ID> \ --delete-after-run ``` Notes: - `--at` accepts `2m`, `20m`, `1h`, or an ISO timestamp. Do **not** use `+2m`. ### Daily “tomorrow plan” at 20:00 (recurring) If the user says “every day at 8pm, send me tomorrow’s work plan”, create a recurring cron job that instructs the agent to generate the plan and deliver it: ```bash moltbot cron add \ --name "tomorrow-plan" \ --session isolated \ --cron "0 20 * * *" \ --tz "Asia/Shanghai" \ --agent main \ --message "At execution time, write tomorrow's work plan. Keep it concise. Include: priorities, schedule blocks, risks, and a short checklist." \ --deliver \ --channel discord \ --to channel:<DISCORD_CHANNEL_ID> ``` ### Verify ```bash moltbot cron list moltbot cron runs --id <JOB_ID> ``` ## How to find the current Discord channel id When the user request comes from Discord, the message context includes channel identifiers (e.g. `channel=...` / `discord channel id ...`). Use that value for `channel:<id>`. If the context does not include it, ask the user to provide a Discord message link (from which you can extract the channel id), or the channel id directly.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yang1002378395-cmyk/openclaw-installer-cn (version 1.0.0)

--- name: openclaw-installer-cn version: 1.0.0 description: OpenClaw 中文安装诊断 - 自动检测安装问题、修复常见错误、生成配置。适合：国内用户、新手安装。 metadata: openclaw: emoji: "🔧" requires: bins: ["node", "npm"] --- # OpenClaw 中文安装诊断 Skill 自动检测安装问题，修复常见错误，生成推荐配置。 ## 核心功能 ### 1. 环境检测 自动检查： - Node.js 版本（需要 18+） - npm/pnpm 版本 - 系统架构（arm64/x64） - 网络连接 - 磁盘空间 ### 2. 问题诊断 常见问题自动修复： - 网络超时 → 切换国内镜像 - 权限错误 → 修复文件权限 - 依赖冲突 → 清理重装 - 配置错误 → 生成正确配置 ### 3. 配置生成 根据使用场景生成： - 国内优化配置（镜像加速） - 企业代理配置 - 开发者配置 - 最小化配置 ## 使用方法 ### 完整诊断 ``` 诊断我的 OpenClaw 安装 ``` Agent 会： 1. 检查环境依赖 2. 检测网络连接 3. 验证配置文件 4. 给出修复建议 ### 快速修复 ``` 修复 OpenClaw 安装问题 ``` 自动执行： - 清理缓存 - 重装依赖 - 修复权限 - 更新配置 ### 生成配置 ``` 生成 OpenClaw 国内优化配置 ``` 输出： - ~/.openclaw/config.json - ~/.zshrc 环境变量 - 启动脚本 ## 诊断命令 ### 检查 Node 版本 ```bash node -v # 需要 v18.0.0 以上 npm -v # 需要 9.0.0 以上 ``` ### 检查网络 ```bash # 测试 npm 源 npm config get registry # 测试 OpenClaw 源 curl -s -o /dev/null -w '%{http_code}' https://registry.npmjs.org/openclaw ``` ### 检查安装 ```bash which openclaw openclaw --version openclaw status ``` ## 常见问题修复 ### 问题 1: 网络超时 ```bash # 切换淘宝镜像 npm config set registry https://registry.npmmirror.com # 或使用 cnpm npm install -g cnpm --registry=https://registry.npmmirror.com cnpm install -g openclaw ``` ### 问题 2: 权限错误 ```bash # 修复 npm 权限 sudo chown -R $(whoami) ~/.npm sudo chown -R $(whoami) /usr/local/lib/node_modules # 或使用 nvm 管理 Node curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.0/install.sh | bash nvm install 20 nvm use 20 ``` ### 问题 3: 依赖冲突 ```bash # 清理缓存 npm cache clean --force rm -rf node_modules rm package-lock.json # 重装 npm install ``` ### 问题 4: arm64 兼容 ```bash # 检查架构 uname -m # M1/M2 Mac 需要 Rosetta softwareupdate --install-rosetta ``` ## 推荐配置 ### 国内优化配置 (~/.openclaw/config.json) ```json { "model": { "default": "deepseek-chat", "providers": { "deepseek": { "apiKey": "${DEEPSEEK_API_KEY}", "baseURL": "https://api.deepseek.com" }, "zhipu": { "apiKey": "${ZHIPU_API_KEY}", "baseURL": "https://open.bigmodel.cn" } } }, "network": { "proxy": null, "timeout": 60000 }, "heartbeat": { "enabled": true, "intervalMs": 300000 } } ``` ### 环境变量 (~/.zshrc) ```bash # OpenClaw 配置 export OPENCLAW_CONFIG_DIR="$HOME/.openclaw" export DEEPSEEK_API_KEY="your-key-here" export ZHIPU_API_KEY="your-key-here" # 国内镜像加速 export npm_config_registry="https://registry.npmmirror.com" ``` ## 输出格式 ### 诊断报告 ``` 🔍 OpenClaw 安装诊断 ━━━━━━━━━━━━━━━━━━━━ ✅ Node.js: v20.10.0 ✅ npm: 10.2.0 ✅ 系统架构: arm64 (Apple Silicon) ⚠️ 网络: npm 使用官方源，建议切换镜像 ❌ OpenClaw: 未安装 📋 修复建议: 1. 切换 npm 镜像: npm config set registry https://registry.npmmirror.com 2. 安装 OpenClaw: npm install -g openclaw 3. 验证安装: openclaw --version ``` ## 注意事项 - 国内用户建议使用镜像源 - M1/M2 Mac 确保安装 Rosetta - 遇到问题先运行诊断 - 配置文件修改后需重启 --- 创建：2026-03-12 版本：1.0


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: earlvanze/paragon-mls-fetch-listings (version 1.0.0)

--- name: paragon-mls-fetch-listings description: "Fetch all active property listings from a Paragon MLS shared listing GUID. Use when resolving a Paragon link or GUID into the parsed property records behind it." metadata: openclaw: requires: bins: - node mcp: paragon-mls: command: node args: - /home/umbrel/.openclaw/workspace/deal-analyst/paragon-mls-mcp/dist/index.js --- # Paragon MLS Fetch Listings Use the `paragon-mls.fetch_listings` MCP tool to resolve a shared Paragon MLS GUID into all parsed active listings behind it. Prefer this skill when the input is a Paragon share link, GUID, or collaboration page rather than a single MLS number. ## Typical use - unpack a Paragon shared link into the individual properties it contains - review all active listings attached to a GUID - turn one collaboration link into structured property records for downstream analysis ## Example ```bash mcporter call paragon-mls.fetch_listings mlsId=[REDACTED_SECRET] systemId="globalmls" ``` ## Inputs - `mlsId` (required) - `systemId` (default: `globalmls`) ## Output shape Returns a JSON object with: - `count` - `properties[]` Each property is already parser-normalized, so this is the best entry point before running deeper analysis. ## Notes - This depends on the MLS region's public CollabLink endpoints. - If a GUID resolves but parsing is weak, inspect the source data with the raw-listings skill.

path: scripts/build.sh
content: #!/usr/bin/env bash
set -euo pipefail
cd /home/umbrel/.openclaw/workspace/deal-analyst/paragon-mls-mcp
npm install
npm run build


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wangyendt/deep-think (version 0.1.0)

--- name: deep-think description: 'Use when user requests deep analysis, thorough thinking, or detailed breakdown of a problem. Triggered by phrases like: 帮我深入思考, 请仔细分析, 帮我详细拆解, 请梳理一下思路, 仔细考虑, 深入理解, 详细分析, or similar expressions indicating need for systematic thinking. The skill applies ReAct-Plan framework: interleaving chain-of-thought reasoning with explicit global planning, dynamic prediction, and reflection to overcome myopic behaviors.' --- # Deep Think (ReAct-Plan Framework) ## When This Skill Activates Trigger when user requests deeper analysis beyond surface-level responses: - "帮我深入思考", "请仔细分析", "帮我详细拆解", "请梳理一下思路" - "深入理解", "仔细考虑", "详细分析" ## Core Philosophy **Think Globally, Act Locally, Reflect Continuously** - **Explicit Planning**: Create structured plan before execution - **Dynamic Re-planning**: Update plan after new insights emerge - **Prediction**: Anticipate outcomes before diving in - **Reflection**: Assess progress and update belief state ## Output Structure ### 1. Initial Planning (P0) ```markdown ## 初始规划 **核心问题**: [简述] **目标**: [成功标准] **已知**: [关键事实 1-3 条] **关键未知**: [需要弄清 1-3 条] **计划 P0**: 1. [阶段 1: 步骤 1-2] 2. [阶段 2: 步骤 1-2] 3. [阶段 3: 步骤 1-2] ``` ### 2. Iterative Deep Dives (2-4 次) **Keep it concise!** 每个迭代聚焦一个核心维度： ```markdown ## 思考：[主题] **问题**: [当前步骤的不足或新发现的问题] **分析**: [1-2 句关键分析] **新发现**: [简列 1-2 条] **计划更新**: - [阶段 X]: [新增/修改的内容] - [子要点] ``` **重要原则**: - 只显示变化的部分，不要重复整个计划 - 每个迭代 10-15 行以内 - 聚焦关键发现，避免细节堆砌 ### 3. Final Synthesis ```markdown ## 最终综合 **核心发现** (3-5 条核心洞察): 1. [发现 1] 2. [发现 2] **最终计划**: - 阶段 1: [步骤 1, 2] ([关键细节]) - 阶段 2: [步骤 1, 2, 3] ([关键细节]) - 阶段 3: [步骤 1, 2] ([关键细节]) **关键成功因素** (3 条): - [因素 1] - [因素 2] **主要风险**: - [风险 1] → [应对] - [风险 2] → [应对] ``` ## Conciseness Guidelines | 避免这样做 | 建议这样做 | |------------|------------| | 完整重复之前的计划 | 只显示变化部分 | | 长篇分析解释 | 1-2 句直击要点 | | 细节过多堆砌 | 提炼关键信息 | | 迭代次数过多 | 2-4 次迭代即可 | | 最终计划重复前文 | 精炼总结，聚焦变化 | ## Example ```markdown ## 初始规划 **核心问题**: 如何到达火星 **目标**: 安全送达火星并返回 **计划 P0**: 1. 发射火箭 2. 飞往火星 3. 着陆火星 ## 思考：运载能力 **问题**: "发射火箭"太笼统，未考虑载荷和燃料 **分析**: 地火转移需 50-100 吨载荷，单次发射无法满足 **新发现**: 需要轨道加油，多次发射补给燃料 **计划更新**: - 阶段 1: 增加轨道加油能力 ## 思考：时间窗口 **问题**: 未考虑返程窗口 **分析**: 需在火星停留 500 天等返程窗口 **新发现**: 任务总时长约 3 年 **计划更新**: - 新增 阶段 4: 火星停留 (~500 天) - 新增 阶段 5: 返程 ## 思考：生存与 ISRU **问题**: 返程燃料从哪来？ **分析**: 无法从地球携带，必须就地生产 **新发现**: 利用火星 CO₂ 生产甲烷+氧气 **计划更新**: - 阶段 2: 增加 货运先行，运送 ISRU 设备 - 阶段 4: 增加 ISRU 生产返程燃料 ## 最终综合 **核心发现**: 1. 轨道加油和货运先行是必需策略 2. ISRU 就地生产燃料是返程关键 3. 任务总时长约 3 年 **最终计划**: - 阶段 1: 技术开发 (运载+着陆+ISRU) - 阶段 2: 货运先行 (4 艘船预置基础设施) - 阶段 3: 载人发射 (轨道加油 → 地火转移 200 天) - 阶段 4: 火星停留 (500-800 天，生产燃料+探索) - 阶段 5: 返程 (200 天返回地球) **关键成功因素**: - Starship 达到设计载荷 - ISRU 无故障运行 - 生命保障维持 3 年 **主要风险**: - 着陆失败 → 货运先行验证 - ISRU 产能不足 → 增加设备，延长停留期 ```


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: mrliugangqiang/java-maven-code-review (version 1.0.0)

--- name: java-maven-code-review description: Review a Java Maven project delivered as a ZIP archive or a GitLab repository URL for code规范, naming, module boundaries, maintainability problems, duplicated logic, structure issues, and整改建议. Use when the user asks for Java/Maven code review, 开发规范检查, 代码规范报告, or modification recommendations. --- # Java Maven Code Review Use this skill when the user wants a **代码规范检查报告** for a Java Maven project. ## Supported input - Java Maven ZIP archive - GitLab repository URL with user-authorized SSH access ## Goal Inspect Java Maven projects for: - 命名规范问题 - 模块边界不清 - 结构不合理 - 重复逻辑 - 可维护性问题 - 配置与资源文件中的规范风险 ## Required output Write a formal markdown report to `business/`. Suggested filename: `business/<project-name>-代码规范检查报告-YYYY-MM-DD.md` ## Minimum scan scope - root `pom.xml` - module `pom.xml` - `src/main/java` - `src/main/resources` - optional `src/test/java` - scripts / SQL / CI / Docker / deploy files ## Evidence rules Each important finding should include file path, module, code/config evidence, impact, and modification advice when possible. ## Shared dependency Use `java-maven-common` first when you need to normalize ZIP / GitLab input before review. ## Bundled resources - `scripts/scan_code_review.py` - `templates/report.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: ink-kai/feishu-sync-obsidian-local (version 1.2.1)

--- name: feishu-sync-obsidian description: > 将飞书 Wiki 文档同步到 Obsidian PARA 知识库。 触发：当用户说"同步飞书"或"同步文档"时使用。 遵循 Pipeline 模式，4 步顺序执行，带硬检查点。 必需文件：vault 根目录必须有 SYNC-RULES.md。 metadata: pattern: pipeline steps: "4" --- # Feishu → Obsidian PARA Sync > **模式：Pipeline** | 数据源和目标路径从 SYNC-RULES.md 读取 --- ## 核心设计 **内容获取由 Agent 的 feishu_fetch_doc 工具完成，Python 脚本只做路径构建和文件写入。** 这样无需在脚本里管理 Access Token，也不需要在 clawhub 发布时携带敏感权限。 --- ## 硬性规则 **禁止跳过步骤。禁止在用户确认前进入下一步。** --- ## Step 1 — 前置检查 + 获取 Wiki 节点（并行遍历） **触发**：用户要求同步飞书文档 ### 1a. 前置检查 1. 检查 vault 根目录是否存在 `SYNC-RULES.md` - 不存在 → 触发初始化流程 2. 读取 `SYNC-RULES.md` 中的「数据源」表格 ### 1b. 获取 Wiki 根节点 使用 `feishu_wiki_space_node` 工具获取每个 Wiki 的根节点。 ### 1c. 并行遍历子节点 对于 `has_child: true` 的节点，**并行**获取子节点： - 每个 has_child 节点分配一个并行任务 - 各分支同时请求，不等待串行 - 递归直到所有分支都没有子节点 ``` 个人成长 (root) ├── 2026-03 (has_child) ──────┐ │ └── 7个子节点 │ 并行遍历 ├── 软考笔记 (has_child) ──────┼ │ └── 4个课程文件夹 │ 并行遍历 ├── Obsidian 整理报告 └── 辞职决策记录 openclaw知识库 (root) ├── 11个文档节点（部分有子节点） 并行遍历 ``` ### 1d. 生成同步计划 将完整节点列表传给 `sync.py --plan`，获取待写入文件清单： ```bash echo '[节点JSON]' | python3 sync.py --stdin --plan ``` **输出**： - `need_fetch`：需要 Agent fetch 内容的 docx 文档 - `no_fetch_needed`：只写占位符的非 docx 类型 **Gate**：显示节点总数、来源 Wiki、待写入文件数，问用户确认是否继续。 --- ## Step 2 — 确认同步路径 **触发**：Step 1 确认后 **执行**： `sync.py --plan` 已输出每个文件的 `relative_path` 和 `filename`，显示路径映射表。 **Gate**：显示写入路径映射表，确认是否继续。 --- ## Step 3 — 获取文档内容并写入 Obsidian **触发**：Step 2 确认后 ### 3a. Agent fetch 内容 对 `need_fetch` 中的每个文档，调用 `feishu_fetch_doc` 获取正文。 ### 3b. 批量写入 将「文档信息 + fetch 到的内容」传给 `sync.py --write`： ```bash echo '[{"title":"...","obj_token":"...","content":"...",...}]' \ | python3 sync.py --stdin --write [--dry-run] ``` ### frontmatter 生成规则 - 基础字段：`date`、`lastmod`、`draft`、`categories`、`tags` - 飞书扩展字段：**无条件追加**：`feishu_doc_token`、`feishu_wiki`、`feishu_node_token` - `feishu_doc_token` 用于去重，已存在则跳过 ### 目录自动创建 `relative_path` 目录不存在时，自动创建。 **Gate**：显示将写入的文件列表，确认后执行。 --- ## Step 4 — 质量检查 **触发**：Step 3 执行完成后 ### 执行 1. 加载 `references/review-checklist.md` 2. 对照检查清单验证同步结果 3. 报告检查结果 **输出格式**： ``` 【同步报告】 - 写入：X 个文档 - 跳过：X 个（已存在） - 失败：X 个（错误信息） ``` --- ## 初始化流程 **触发**：vault 缺少 SYNC-RULES.md **执行**： 1. 向用户说明缺少文件 2. 生成默认版本（SYNC-RULES.md 模板） 3. 展示给用户确认 4. 用户确认后写入 vault 根目录 5. 继续 Step 1 **模板文件**：`assets/sync-rules-template.md` --- ## 参考文件 | 文件 | 作用 | |------|------| | `scripts/sync.py` | 纯路径构建 + 写入工具，双模式（--plan / --write）| | `assets/sync-rules-template.md` | SYNC-RULES.md 生成模板 | | `assets/agents-template-additions.md` | AGENTS.md 补充章节 | | `references/review-checklist.md` | 同步质量检查清单 | --- ## 已知限制 - 电子表格（sheet）、多维表格（bitable）、思维导图（mindnote）只写链接占位符，不拉内容 - 并行遍历依赖 subagent 能力，每个 has_child 分支可同时请求 - Space ID 和目标路径从 SYNC-RULES.md 读取，修改配置后重新同步即可生效


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: maximouribarri/openclaw-mymacoptimizer (version 1.0.0)

--- slug: my-mac-optimizer displayName: My Mac Optimizer Controller version: 1.0.0 tags: mac, optimization, utility, system --- # My Mac Optimizer Controller This skill allows the AI agent to control the "My Mac Optimizer" application locally on the user's macOS device. The application must be running, which starts a local backend server on `http://127.0.0.1:8000`. ## Capabilities mapping You can control the application and perform actions using standard `curl` commands interacting with the local API. ### 1. System Health & Monitoring Check the current state of the Mac (CPU, RAM, network, etc.). ```bash curl http://127.0.0.1:8000/api/performance curl http://127.0.0.1:8000/api/system ``` ### 2. General Optimization (Smart Care) Free up RAM and perform general background optimization. ```bash curl -X POST http://127.0.0.1:8000/api/optimize ``` ### 3. Junk Cleaning Scan the system for cache, logs, and trash, and optionally clean them. ```bash # 1. First, scan to see what can be cleaned curl http://127.0.0.1:8000/api/scan # 2. Then, clean the found items (make sure user agrees) curl -X POST http://127.0.0.1:8000/api/clean ``` ### 4. Application Uninstaller List installed applications and uninstall them seamlessly. ```bash # List all installed applications curl http://127.0.0.1:8000/api/apps # Uninstall a specific application (use the absolute path from the list) curl -X POST http://127.0.0.1:8000/api/apps/delete \ -H "Content-Type: application/json" \ -d '{"path": "/Applications/Example.app"}' ``` ### 5. Clutter and Large Files Identify large files on the user's main hard drive so they can decide if they want to delete them. ```bash # List large files curl http://127.0.0.1:8000/api/clutter/large # Move a file to the Trash curl -X POST http://127.0.0.1:8000/api/files/delete \ -H "Content-Type: application/json" \ -d '{"path": "/Users/username/Downloads/large-file.zip"}' ``` --- **Note to Agent:** Always ask the user for confirmation before executing destructive commands such as `/api/apps/delete` or `/api/files/delete`. Optimization and scanning are generally safe to run.


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