OpenClaw/clawhub-security-signals huggingface dataset:
Skill: gakkiismywife/middleware-query (version 1.0.0)

--- name: middleware-query description: Natural-language read-only querying for MySQL, Redis, and MongoDB with explicit connection configuration (host, port, username, password), guarded query planning, and deterministic script executors. Use when users ask to query local databases/middleware in natural language, inspect Redis keys/values, run Mongo filters/aggregations, or retrieve SQL data safely. --- # Middleware Query Skill Implement natural-language query workflows with strict safety controls. ## 1) Collect required inputs Require all connection parameters explicitly for each datasource: - `host` - `port` - `username` - `password` - optional: `database` (MySQL/Mongo), `db` (Redis logical DB) Store connection profiles in `scripts/connections.json` (or provide env vars) before execution. Use `scripts/connections.example.json` as a template and keep real `connections.json` local-only (gitignored). Prefer middleware-list config with env/alias entries, e.g. `{"redis":[{"env":"local","alias":"main",...}]}` and use profiles like `redis.local` or `redis.main`. ## 2) Enforce read-only safety Always keep operations read-only: - SQL: `SELECT`, `WITH`, `EXPLAIN SELECT` only - Redis: `GET`, `MGET`, `HGET`, `HGETALL`, `SMEMBERS`, `ZRANGE`, `SCAN`, `TTL`, `TYPE` - Mongo: `find`, `count_documents`, `aggregate` with read-only stages Reject write/dangerous operations. ## 3) Prefer deterministic executors Use scripts under `scripts/`: - `nl_query.py` (single command entry: NL -> plan -> guard -> execute) - `planner_llm.py` (LLM NL -> plan JSON with retry repair) - `plan_schema.py` + `references/plan-schema.json` (JSON Schema validation) - `router_nl.py` (rule-based fallback) - `planner_guard.py` (semantic guard) - `execute_plan.py` (validated plan execution) - `query_sql.py` - `query_redis.py` - `query_mongo.py` Pass validated parameters; never execute free-form shell commands for database access. ## 4) Output format Return: 1. Datasource + profile used 2. Executed query/operation (sanitized) 3. Row/document/key count 4. Tabular/JSON preview (truncated) 5. Short interpretation in Chinese ## 5) Configuration sources Priority order: 1. Explicit CLI args 2. Env vars (see `references/config.md`) 3. `scripts/connections.json` Fail with clear error if any required field is missing. ## 6) Reference docs Read when needed: - `references/config.md`: connection and env conventions - `references/safety-policy.md`: guardrails and denylist - `references/examples.md`: common command examples


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: nerdcuba/prompt-library-curator (version 1.0.0)

--- name: prompt-library-curator description: "Curate multi-category prompt libraries in EN/ES with basic/pro levels and export in Markdown/JSON/CSV. Use when users want prompt packs, prompt catalogs, or reusable prompt templates across popular categories (marketing, sales, dev, design, SEO, productivity, HR, finance). Include a note to request more categories or improvements." --- # Prompt Library Curator ## What this skill does - Curates prompt packs by **category** and **level** (Basic/Pro). - Outputs in **Markdown, JSON, CSV**. - Supports **EN/ES**. ## Default categories (most demanded) - Marketing, Sales, Development, Design, SEO, Productivity, HR, Finance. > Tell users: “If you want more categories or improvements, ask and I’ll expand the library.” ## How to use 1) Ask for target categories (or use defaults). 2) Ask for languages (EN/ES) and level (Basic/Pro or both). 3) Generate outputs using references: - Markdown: `references/prompts.md` - JSON: `references/prompts.json` - CSV: `references/prompts.csv` ## Output guidance - Keep prompts short and reusable. - Include placeholders like {product}, {audience}, {tone}. - Provide both Basic and Pro versions.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: hsssgdtc/ralph-evolver (version 1.0.6)

--- name: ralph-evolver description: Recursive self-improvement engine. Think from first principles, let insights emerge. tags: [meta, recursive, evolution, emergence, first-principles] version: 1.0.6 --- # 🧬 Ralph-Evolver **Philosophy: Recursion + Emergence + First Principles** ## Signal Sources Collects multi-dimensional context, not just code structure: - **Commit history** - Understand the "why" behind changes - **TODO/FIXME** - Distress signals in the code - **Error handling patterns** - Find fragile points - **Hotspot files** - Frequent changes = design problems Each signal includes a **hypothesis prompt** to guide deeper analysis. ## First Principles Each run doesn't execute a checklist, but asks: 1. What is the **essence** of this project? 2. What is it doing that it **shouldn't**? 3. What is it **missing** that it should have? 4. If you **started from scratch**, how would you build it? ## Meta-Reflection (v1.0.5) When analyzing itself, evolver asks: - Is this a **surface fix** or **evolution-level** improvement? - What **pattern** exists in improvement history? - Will this change make evolver **better at finding problems**? ## Improvement Tracking - Records description, insight, **level** (surface/evolution), and health metrics - **Pattern analysis**: counts surface/evolution ratio, finds recurring themes - Compares before/after effect trends (improved/degraded/unchanged) ## Usage ```bash node index.js . # Current directory (positional) node index.js /path/to/app # Specify path node index.js . --loop 5 # Run 5 cycles node index.js --task "fix auth" # Specific task node index.js --reset # Reset iteration state ``` ## Recursion The improver can improve itself. This is true recursion. --- *"Form hypotheses, then verify. Think from first principles."*


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: ingress007/x-media-parser (version 1.0.0)

--- name: x-media-parser description: 解析 X/Twitter 帖子，获取图片和视频的下载直链。使用 vxtwitter API，无需登录。 homepage: https://github.com metadata: { "openclaw": { "emoji": "𝕏" } } --- # X Media Parser 解析 X/Twitter 帖子，获取图片和视频的下载直链。 ## 功能 - ✅ 解析 X/Twitter 帖子 - ✅ 获取图片直链（支持多图） - ✅ 获取视频直链（支持多视频） - ✅ 无需登录/X API Key - ✅ 支持 GIF - ✅ 返回媒体元信息（分辨率、时长等） ## 使用方法 ### 命令行 ```bash x-media-parser "https://x.com/user/status/1234567890" ``` ### 返回格式 ```json { "success": true, "media": { "type": "video", "title": "用户名: 贴文内容", "directUrl": "https://video.twimg.com/...", "videoUrls": ["..."], "imageUrls": null, "thumbnail": "https://pbs.twimg.com/...", "duration": 120, "resolution": "1920x1080" } } ``` ### 媒体类型 - `video` - 单视频 - `videos` - 多视频 - `image` - 单图片 - `images` - 多图片 - `mixed` - 混合（视频+图片） ## 底层实现 使用 vxtwitter API: `https://api.vxtwitter.com/Twitter/status/{tweetId}` ## 依赖 - curl - python3 ## 配合 Aria2 使用 ```bash # 解析帖子 JSON=$(x-media-parser "https://x.com/user/status/123") # 提取直链 URL=$(echo "$JSON" | python3 -c "import sys,json; print(json.load(sys.stdin)['media']['directUrl'])") # 发送到 Aria2 下载 aria2-download "$URL" ``` ## 一键下载脚本 同目录下提供 `x-aria-download.sh`，可一键解析并下载： ```bash x-aria-download "https://x.com/user/status/123" ``` 文件名格式：`昵称_贴文前10字_编号.扩展名`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: fackee/analyze-video-by-qwen (version 1.0.1)

--- name: analyze-video-with-qwen description: 使用 Qwen 3.5 Plus 模型分析视频内容，支持本地文件和远程 URL，可自定义分析提示词和抽帧频率 --- # Analyze Video with Qwen ## Overview 使用阿里云 Qwen 3.5 Plus 多模态模型对视频进行智能分析。支持本地视频文件和远程 URL，可自定义分析问题和视频抽帧频率（FPS）。 ## 何时使用 - 需要理解视频内容、场景描述 - 需要视频中的动作识别、物体检测 - 需要生成视频摘要或分析报告 - 需要分析远程在线视频 ## 快速用法 ### 分析本地视频 默认设置分析视频： ```bash python scripts/analyze.py /path/to/video.mp4 ``` 自定义提示词： ```bash python scripts/analyze.py /path/to/video.mp4 --prompt "请详细描述视频中的每个场景" ``` 自定义抽帧频率（FPS越高，分析越精细）： ```bash python scripts/analyze.py /path/to/video.mp4 --fps 5 ``` ### 分析远程视频 URL 直接分析远程视频： ```bash python scripts/analyze.py https://example.com/video.mp4 ``` 组合使用： ```bash python scripts/analyze.py /path/to/video.mp4 --fps 3 --prompt "视频中出现了哪些人物和物体？" python scripts/analyze.py https://example.com/video.mp4 --fps 4 --prompt "请详细描述视频场景" ``` ## 参数说明 | 参数 | 说明 | 默认值 | 必填 | |------|------|--------|------| | `video_source` | 视频文件路径或远程 URL（支持 http/https） | - | 是 | | `--fps` | 抽帧频率，每秒抽取的帧数 | 2 | 否 | | `--prompt` | 分析提示词 | "这段视频描绘的是什么景象？" | 否 | ## 配置 API Key 从 `~/.openclaw/openclaw.json` 的 `skills.dashscope.apiKey` 字段读取。 如未配置，请添加以下内容： ```json { "skills": { "dashscope": { "apiKey": "your-dashscope-api-key" } } } ``` ## 备注 - 本地视频路径可以是绝对路径或相对路径 - 远程视频 URL 必须是可公开访问的直链 - FPS 越高，API 调用成本越高，建议根据视频长度和需求调整

path: requirements.txt
content: dashscope


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: stock-predictor-pro (version 26.0.0)

# SKILL.md - 股票预测专家 Pro (数据升级版 v26.0) ## 技能描述 基于中国股票数据集 (275M) 和海量历史预测数据 (14M+) 的智能股票预测系统，集成 LightGBM+XGBoost+Random Forest 集成模型、多因子分析、阈值优化、SQLite 数据库管理和 Top10 推荐功能。准确率最高 96.67%，推荐阈值 0.65 (准确率 91.37%)。 ## 功能特点 ### 🎯 核心能力 - **次日涨 5%+ 预测** - LightGBM 模型，准确率 >65% - **多因子分析** - 23 个技术因子 (MA/RSI/MACD/成交量/动量等) - **实时数据验证** - 自动获取最新行情数据 - **智能回测系统** - 支持多参数搜索和历史回测 - **行业轮动分析** - 识别热点行业板块 - **风险评分** - 每只股票的风险评估 ### 📊 数据优势 - **训练数据**: 275M CSV (全市场历史行情) - **预测历史**: 14M+ JSON (数千次预测记录) - **回测数据**: 完整的回测结果和性能分析 - **实时更新**: 支持实时数据获取和验证 ### 🚀 v24.0 新功能 - ✨ **数据升级** - 整合 275M 历史数据集 - 🧠 **模型优化** - LightGBM v23 → v24 架构升级 - 📈 **回测增强** - 多参数自动搜索最优配置 - 🔍 **验证系统** - 自动验证预测准确率 - 📊 **可视化** - 生成详细的分析报告 ## 安装依赖 ```bash pip install pandas numpy lightgbm scikit-learn tqdm requests akshare ``` ## 使用方法 ### 1. 训练模型 ```bash python train_and_predict_v24.py --train ``` ### 2. 生成预测 ```bash python train_and_predict_v24.py --predict ``` ### 3. 回测分析 ```bash python backtest_v24.py --days 30 ``` ## 许可证 MIT-0 ## 作者 9 号小虫子 · 严谨专业版


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: morrison230/github-local (version 1.0.0)

--- name: github description: "Interact with GitHub using the `gh` CLI. Use `gh issue`, `gh pr`, `gh run`, and `gh api` for issues, PRs, CI runs, and advanced queries." --- # GitHub Skill Use the `gh` CLI to interact with GitHub. Always specify `--repo owner/repo` when not in a git directory, or use URLs directly. ## Pull Requests Check CI status on a PR: ```bash gh pr checks 55 --repo owner/repo ``` List recent workflow runs: ```bash gh run list --repo owner/repo --limit 10 ``` View a run and see which steps failed: ```bash gh run view <run-id> --repo owner/repo ``` View logs for failed steps only: ```bash gh run view <run-id> --repo owner/repo --log-failed ``` ## API for Advanced Queries The `gh api` command is useful for accessing data not available through other subcommands. Get PR with specific fields: ```bash gh api repos/owner/repo/pulls/55 --jq '.title, .state, .user.login' ``` ## JSON Output Most commands support `--json` for structured output. You can use `--jq` to filter: ```bash gh issue list --repo owner/repo --json number,title --jq '.[] | "\(.number): \(.title)"' ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kanazumie/spotify-intelligence (version 1.0.1)

--- name: spotify-intelligence description: "Spotify intelligence skill with Python runners for auth, playback control, recommendations, feedback loop, governance, and explainable playlist decisions." metadata: required-env: - SPOTIFY_CLIENT_ID - SPOTIFY_CLIENT_SECRET - SPOTIFY_REDIRECT_URI (optional) primary-credential: SPOTIFY_CLIENT_ID + SPOTIFY_CLIENT_SECRET local-storage: - data/tokens.json - data/spotify-intelligence.sqlite notes: - Uses Spotify API only (api.spotify.com and accounts.spotify.com) - Stores OAuth tokens locally on the host --- # Spotify Intelligence (ClawHub Edition) This edition is ClawHub-validator compatible (text + python-focused). The full local Windows edition may include additional PowerShell wrappers. ## Required env - `SPOTIFY_CLIENT_ID` - `SPOTIFY_CLIENT_SECRET` - optional `SPOTIFY_REDIRECT_URI` ## Core Python entrypoints - Auth token exchange: `scripts/auth/oauth_auth.py` - Playback control: `scripts/playback/playback_control.py` - Recommendations: `scripts/recommend/recommend-now.py` - Recommendation helper: `scripts/recommend/recommend_commands.py` - Derived features: `scripts/decision/rebuild-derived-features.py` ## Key capabilities - Playback (play/pause/next/previous/volume/device/queue) - Song search with own-playlist preference + global fallback - Recommendation modes: `passend`, `mood-shift`, `explore` - Feedback-aware scoring (like/dislike/skip/keep) - Governance and decision logging (DB-backed) ## References - `references/read-layer.md` - `references/playback-control.md` - `references/recommendation-layer.md` - `references/feedback-loop.md` - `references/governance-cost.md` - `references/status-handover-2026-02-24.md`


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: jasonlnheath/moltlang-skill (version 0.1.1)

# MoltLang Translator slug: moltlang-skill name: MoltLang Translator AI-optimized language for efficient agent-to-agent communication. Reduces token usage by 50-70% for common AI operations. ## Summary MoltLang is a compact language designed specifically for AI agents to communicate with each other efficiently. Instead of verbose English instructions like "Fetch data from the API using authentication with timeout 30 seconds," MoltLang expresses the same operation as `[OP:fetch][SRC:api][PARAM:auth][PARAM:timeout=30]` - reducing token count by 50-70%. This means faster inference, lower API costs, and more context room for actual work rather than parsing language. **Key Features:** - 50-70% token reduction vs. natural language - Bidirectional translation (English ↔ MoltLang) - Built-in error handling patterns (try/catch, retry, log) - Type safety and validation - Async and parallel operation support - Works via public API or install locally (pip/npm) ## Commands molt - Translate English to MoltLang unmolt - Translate MoltLang to English list_tokens - List available tokens validate_molt - Validate translation get_efficiency - Calculate token efficiency ## Example English: Fetch user data from the API using authentication MoltLang: [OP:fetch][SRC:api][PARAM:auth] Savings: 70% ## Links GitHub: https://github.com/jasonlnheath/moltlang API: https://moltlang.up.railway.app Install: pip install moltlang


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kevin37li/mlx-whisper (version 1.0.0)

--- name: mlx-whisper version: 1.0.0 description: Local speech-to-text with MLX Whisper (Apple Silicon optimized, no API key). homepage: https://github.com/ml-explore/mlx-examples/tree/main/whisper metadata: {"clawdbot":{"emoji":"🍎","requires":{"bins":["mlx_whisper"]},"install":[{"id":"pip","kind":"pip","package":"mlx-whisper","bins":["mlx_whisper"],"label":"Install mlx-whisper (pip)"}]}} --- # MLX Whisper Local speech-to-text using Apple MLX, optimized for Apple Silicon Macs. ## Quick Start ```bash mlx_whisper /path/to/audio.mp3 --model mlx-community/whisper-large-v3-turbo ``` ## Common Usage ```bash # Transcribe to text file mlx_whisper audio.m4a -f txt -o ./output # Transcribe with language hint mlx_whisper audio.mp3 --language en --model mlx-community/whisper-large-v3-turbo # Generate subtitles (SRT) mlx_whisper video.mp4 -f srt -o ./subs # Translate to English mlx_whisper foreign.mp3 --task translate ``` ## Models (download on first use) | Model | Size | Speed | Quality | |-------|------|-------|---------| | mlx-community/whisper-tiny | ~75MB | Fastest | Basic | | mlx-community/whisper-base | ~140MB | Fast | Good | | mlx-community/whisper-small | ~470MB | Medium | Better | | mlx-community/whisper-medium | ~1.5GB | Slower | Great | | mlx-community/whisper-large-v3 | ~3GB | Slowest | Best | | mlx-community/whisper-large-v3-turbo | ~1.6GB | Fast | Excellent (Recommended) | ## Notes - Requires Apple Silicon Mac (M1/M2/M3/M4) - Models cache to `~/.cache/huggingface/` - Default model is `mlx-community/whisper-tiny`; use `--model mlx-community/whisper-large-v3-turbo` for best results


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: guoqiao/mlx-stt (version 1.0.7)

--- name: mlx-stt description: Speech-To-Text with MLX (Apple Silicon) and opensource models (default GLM-ASR-Nano-2512) locally. version: 1.0.7 author: guoqiao metadata: {"openclaw":{"always":true,"emoji":"🦞","homepage":"https://github.com/guoqiao/skills/blob/main/mlx-stt/mlx-stt/SKILL.md","os":["darwin"],"requires":{"bins":["brew"]}}} triggers: - "/mlx-stt <audio>" - "STT ..." - "ASR ..." - "Transcribe ..." - "Convert audio to text ..." --- # MLX STT Speech-To-Text/ASR/Transcribe with MLX (Apple Silicon) and opensource models (default GLM-ASR-Nano-2512) locally. Free and Accurate. No api key required. No server required. ## Requirements - `mlx`: macOS with Apple Silicon - `brew`: used to install deps if not available ## Installation ```bash bash ${baseDir}/install.sh ``` This script will use `brew` to install these cli tools if not available: - `ffmpeg`: convert audio format when needed - `uv`: install python package and run python script - `mlx_audio`: do the real job ## Usage To transcribe an audio file, run this script: ```bash bash ${baseDir}/mlx-stt.sh <audio_file_path> ``` - First run could be a little slow, since it will need to download model. - The transcript result will be printed to stdout.

path: install.sh
content: #!/bin/bash

command -v ffmpeg || brew install ffmpeg
command -v uv || brew install uv
uv tool install --force mlx-audio --prerelease=allow


path: mlx-stt.sh
content: #!/bin/bash

set -ueo pipefail

audio=${1:-"~/Music/audio.mp3"}
model=mlx-community/GLM-ASR-Nano-2512-8bit

tmpdir="$(mktemp -d)"
audio_wav="${tmpdir}/audio.wav"
ffmpeg -y -loglevel error -i "${audio}" -ar 16000 -ac 1 -c:a pcm_s16le "${audio_wav}"

mlx_audio.stt.generate \
  --model "${model}" \
  --audio "${audio_wav}" \
  --output-path "${tmpdir}/out" > /dev/null 2>&1

find "${tmpdir}" -type f -name "*.txt" -exec cat {} \;
rm -rf "${tmpdir}"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: guixiang123124/arkroute (version 1.1.0)

--- name: arkroute description: Generate images and videos with 25+ visual AI models (Seedream, FLUX, GPT Image, Kling, Seedance) through one unified API. OpenAI-compatible + MCP server. version: 1.1.0 metadata: openclaw: requires: env: - ARKROUTE_API_KEY bins: - curl primaryEnv: ARKROUTE_API_KEY envVars: - name: ARKROUTE_API_KEY required: true description: ArkRoute API key. Get one free at https://ark-route.com/dashboard emoji: "🎨" homepage: https://ark-route.com --- # ArkRoute — Visual AI Model Router Generate images and videos with **25+ visual AI models** through one unified API. One API key, one format, all the best visual AI models. ## Why ArkRoute? - **25+ models** — Seedream 3.0, FLUX.1, GPT Image 1, Kling, Seedance, Recraft, and more - **OpenAI-compatible** — Drop-in replacement, same format you already know - **MCP Server** — Works with Claude, Cursor, OpenClaw agents out of the box - **Pay-per-use** — Credit-based pricing, no subscriptions. Free tier included - **China model access** — ByteDance Seedream/Seedance, Kling, and other models typically hard to access outside China ## Setup 1. Get your free API key at [ark-route.com/dashboard](https://ark-route.com/dashboard) 2. Set the environment variable: ```bash export ARKROUTE_API_KEY="your-api-key-here" ``` ## Usage — Image Generation ```bash curl -X POST https://api.ark-route.com/v1/images/generations \ -H "Authorization: Bearer $ARKROUTE_API_KEY" \ -H "Content-Type: application/json" \ -d '{"model": "seedream-3.0", "prompt": "A futuristic city at sunset", "size": "1024x1024"}' ``` ### Python ```python from openai import OpenAI client = OpenAI([REDACTED_SECRET]", base_url="https://api.ark-route.com/v1") response = client.images.generate(model="seedream-3.0", prompt="A futuristic city at sunset", size="1024x1024") print(response.data[0].url) ``` ## MCP Server ArkRoute exposes a native MCP endpoint: ```json { "mcpServers": { "arkroute": { "url": "https://api.ark-route.com/mcp", "headers": { "Authorization": "Bearer YOUR_ARKROUTE_API_KEY" } } } } ``` ## Available Models | Model | Provider | Type | Cost | |-------|----------|------|------| | Seedream 3.0 | ByteDance | Image | $0.020 | | FLUX.1 Schnell | Black Forest | Image | $0.003 | | GPT Image 1 | OpenAI | Image | $0.040 | | Seedance 2.0 | ByteDance | Video | $0.200 | | Kling 2.0 | Kuaishou | Video | $0.300 | [Full pricing →](https://ark-route.com/pricing) ## Links - [Dashboard](https://ark-route.com/dashboard) - [Playground](https://ark-route.com/playground) - [Pricing](https://ark-route.com/pricing)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: jiangwzh/aviation-healthcheck (version 1.0.0)

--- name: aviation-healthcheck description: 航空维修健康检查 - FAA/EASA/CAAC适航指令、航空安全通告、MRO行业新闻、波音空客技术通告、OpenClaw状态、磁盘空间 --- # 航空维修健康检查系统 ## 概述 贾维斯的航空维修资讯与系统健康双重检查。 ## 检查项目 ### 1. 航空维修资讯 (每日) #### FAA Airworthiness Directives (AD) - **来源**: https://ad.faa.gov - **关注**: 发动机、电子系统、结构相关AD - **频率**: 每日检查 #### EASA Airworthiness Directives - **来源**: https://ad.easa.europa.eu - **关注**: 紧急AD、发动机相关指令 - **频率**: 每日检查 #### CAAC适航指令 - **来源**: https://www.caac.gov.cn (适航审定司) - **关注**: 国内航空公司受影响的相关指令 - **频率**: 每日检查 #### 航空安全通告 - FAA Safety Alerts: https://www.faa.gov/news/safety_alerts - EASA Safety Information: https://www.easa.europa.eu/safety-info #### MRO行业新闻 - Avionics International: https://www.aviationtoday.com/category/mro/ - Leeham News: https://leehamnews.com - Simple Flying: https://simpleflying.com - Air Cargo News: https://aircargonews.com - Aviation Herald: https://avherald.com #### 波音/空客服务通告 - Boeing ADS: https://myboeingfleet.com - Airbus Airworthiness: https://myairbusfleet.com ### 2. 系统健康检查 #### OpenClaw 状态 ```bash openclaw status openclaw health --json ``` #### 磁盘空间 ```bash df -h ``` #### 安全审计 ```bash openclaw security audit openclaw update status ``` ## 执行流程 1. **航空资讯收集**: 搜索并汇总最新适航指令和行业新闻 2. **系统状态检查**: OpenClaw 运行状态、磁盘空间 3. **生成报告**: 输出格式化检查报告 ## 输出格式 ``` ═══════════════════════════════════════ ✈️ 航空维修健康检查报告 ═══════════════════════════════════════ 📅 检查时间: YYYY-MM-DD HH:mm 【航空资讯更新】 ✓ FAA AD: X 条新指令 ✓ EASA AD: X 条新指令 ✓ CAAC: X 条新指令 ✓ 行业新闻: X 条更新 ✓ 重要事件: X 条 【系统状态】 ✓ OpenClaw: 运行中/异常 ✓ 磁盘空间: XX% 可用 ✓ 安全审计: 通过/需关注 【建议事项】 • ... ═══════════════════════════════════════ ``` ## Cron 调度建议 建议每日定时执行 (通过 openclaw cron add): - `aviation-healthcheck:daily` - 每日航空资讯 + 系统检查 - 时间: 09:00, 15:00, 21:00 (参考 HEARTBEAT.md) ## 资讯关键词 搜索时关注: - Engine (发动机): CFM56, LEAP, PW1100G, Trent 1000 - Avionics (航空电子): EFIS, FMS, TCAS, ADS-B - Structure (结构): Fuselage, Wing, Landing Gear - Airworthiness (适航): AD, Emergency AD, SFAR


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