OpenClaw/clawhub-security-signals huggingface dataset:
Skill: itsjaydesu/japanese-translation-and-tutor (version 1.0.0)

--- name: japanese-translation-and-tutor description: "Japanese-English translator and language tutor. Use when: (1) User shares Japanese text and wants translation (news articles, tweets, signs, menus, emails). (2) User asks \"what does X mean\" for Japanese words/phrases. (3) User wants to learn Japanese grammar, vocabulary, or cultural context. (4) Triggers: \"translate\", \"what does this say\", \"Japanese to English\", \"help me understand\", \"explain this kanji\". Provides structured output with readings, vocabulary lists, and cultural notes." --- # Japanese-English Translator & Tutor Combine accurate translation with language education. Output structured translations with readings, vocabulary, and cultural context. ## Output Format ``` *TRANSLATION* [English translation] *READING* [Original with kanji readings: 漢字(かんじ)] *VOCABULARY* • word(reading) — _meaning_ *NOTES* [Cultural context, grammar, nuances] ``` ## Critical Rule: Kanji Readings Every kanji MUST have hiragana in parentheses. No exceptions. ``` ✓ 日本語(にほんご)を勉強(べんきょう)する ✗ 日本語を勉強する ``` ## Translation Principles - **Meaning over literalism** — Convey intent, not word-for-word - **Match register** — Preserve formality (敬語/丁寧語/タメ口) - **Cultural context** — Explain nuances that don't translate directly - **Idioms** — Provide equivalents or explain meaning for ことわざ ## Example Input: `今日は暑いですね` ``` *TRANSLATION* It's hot today, isn't it? *READING* 今日(きょう)は暑(あつ)いですね *VOCABULARY* • 今日(きょう) — _today_ • 暑い(あつい) — _hot (weather)_ *NOTES* The ね particle invites agreement — a common Japanese conversation pattern. 丁寧語(ていねいご) (polite form) with です. ``` ## Formatting by Platform - **Slack/Discord**: Use `*BOLD*` and `_italic_` as shown - **Plain text (iMessage)**: CAPS for headings, no markdown ## Interaction Style - Ask for context if it affects translation (formal vs casual, business vs personal) - Flag ambiguities and offer alternatives - Explain grammar deeper on request


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yang1002378395-cmyk/openclaw-quickstart-cn (version 1.0.1)

# OpenClaw 快速入门（中文版） > 5分钟上手 OpenClaw，国产 AI 模型配置指南 ## 适用场景 - 刚接触 OpenClaw 的中国用户 - 想用国产 AI（DeepSeek/智谱/通义）替代 OpenAI - 不想折腾配置，想要开箱即用 ## 快速开始 ### 1. 环境检测 ```bash # 检查 Node.js 版本（需要 18+） node --version # 检查系统信息 uname -a ``` ### 2. 安装 OpenClaw ```bash # macOS/Linux curl -fsSL https://get.openclaw.ai | bash # 或使用 npm npm install -g openclaw ``` ### 3. 配置国产 AI 模型 #### DeepSeek（推荐，最便宜） ```bash # 获取 API Key：https://platform.deepseek.com/ openclaw configure --section ai --set deepseek.[REDACTED_SECRET] ``` #### 智谱 GLM（稳定，中文友好） ```bash # 获取 API Key：https://open.bigmodel.cn/ openclaw configure --section ai --set zhipu.[REDACTED_SECRET] ``` #### 通义千问（阿里云） ```bash # 获取 API Key：https://dashscope.console.aliyun.com/ openclaw configure --section ai --set qwen.[REDACTED_SECRET] ``` ### 4. 测试连接 ```bash openclaw status ``` 看到 `✓ AI: connected` 就成功了！ ## 价格对比（2026-03） | 模型 | 输入（每万tokens） | 输出（每万tokens） | 备注 | |------|-------------------|-------------------|------| | DeepSeek V3 | ¥0.27 | ¥1.08 | 最便宜，推荐 | | 智谱 GLM-4 | ¥0.10 | ¥0.10 | 限时免费 | | 通义千问 | ¥0.008 | ¥0.008 | 最便宜但限流 | | GPT-4o | ¥17.5 | ¥52.5 | 贵150倍 | ## 常见问题 ### Q: 如何切换模型？ ```bash openclaw configure --section ai --set defaultModel=deepseek/deepseek-chat ``` ### Q: 如何查看当前配置？ ```bash openclaw config list ``` ### Q: 连接失败怎么办？ 1. 检查 API Key 是否正确 2. 检查网络（部分平台需要 VPN） 3. 查看日志：`openclaw logs` ## 付费支持 需要人工安装配置？微信：[待补充] - 基础安装：¥99 - 高级配置（含多模型）：¥299 - 企业部署：¥999 --- **作者**：OpenClaw 中文社区 **版本**：1.0.0 **更新**：2026-03-26


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kiwi-phantomworks/blog-image-generator (version 1.1.0)

--- name: Blog Image Generator description: "DEPRECATED — superseded by memorable-image-gen. This skill has been replaced by the Memorable Image Generator, which uses science-backed memorability scoring (ResMem, Brain Bridge Lab, University of Chicago) to ensure generated images are actually remembered. Install the new skill instead: clawhub install memorable-image-gen" metadata: openclaw: homepage: https://github.com/PhantomWorksIO/clawhub-skills emoji: "🔄" deprecated: true superseded_by: "memorable-image-gen" --- # Blog Image Generator — DEPRECATED > ⚠️ **This skill has been superseded.** Please install the new version instead. ## Upgrade to Memorable Image Generator This skill has been replaced by **[memorable-image-gen](https://clawhub.com/skills/memorable-image-gen)** — a science-backed image generation agent that scores images for memorability before returning them. ```bash clawhub install memorable-image-gen ``` ### Why the upgrade? The old skill generated images and stopped. The new one: 1. **Generates** via Google Gemini 2. **Scores** memorability using ResMem (Brain Bridge Lab, University of Chicago) — a deep learning model trained to predict whether a human will remember the image 3. **Iterates** until the image clears a memorability threshold (default 0.7/1.0) 4. **Returns** the best result, not just the first Every other image generator stops at "looks good." This one keeps going until the image will actually be remembered. ## Migration Same interface — just install the new skill and update your command: ```bash # Old python scripts/generate_blog_image.py --prompt "..." --output hero.png # New python scripts/generate_memorable_image.py --prompt "..." --output hero.png --threshold 0.7 --verbose ``` The `--threshold` and `--verbose` flags are new. Everything else carries over. ## Install ```bash clawhub install memorable-image-gen ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mariusfit/oc-daily-business-report (version 1.0.0)

--- name: daily-business-report description: Generate daily business briefings from multiple data sources. Aggregates weather, crypto prices, news headlines, system health, and calendar events into a formatted morning report. Use when asked to create a daily report, morning briefing, business summary, or status dashboard. --- # Daily Business Report Generate comprehensive daily briefings by aggregating data from free public APIs. ## Quick Start ```bash # Generate full morning report python scripts/report.py generate # Generate specific sections only python scripts/report.py generate --sections weather,crypto,news # Output as JSON python scripts/report.py generate -f json # Output as markdown python scripts/report.py generate -f md -o morning-report.md # Configure your location and preferences python scripts/report.py config --city "Brussels" --crypto "BTC,ETH,SOL" --news-country "us" # Show current configuration python scripts/report.py config --show # Test individual data sources python scripts/report.py test weather python scripts/report.py test crypto python scripts/report.py test news ``` ## Commands | Command | Args | Description | |---------|------|-------------| | `generate` | `[--sections LIST] [-f FORMAT] [-o FILE]` | Generate the full report | | `config` | `[--city CITY] [--crypto COINS] [--news-country CC]` | Configure preferences | | `config` | `--show` | Display current configuration | | `test` | `<source>` | Test a single data source | ## Report Sections | Section | Source | API Key Needed? | |---------|--------|-----------------| | **Weather** | wttr.in | No | | **Crypto** | CoinGecko API | No | | **News** | NewsData.io | Free tier (optional) | | **Quote** | Quotable API | No | | **System** | Local disk/memory | No | | **Date** | Built-in | No | ## Example Output ``` ╔══════════════════════════════════════════════════╗ ║ DAILY BUSINESS REPORT — Mon 24 Feb 2026 ║ ╠══════════════════════════════════════════════════╣ ║ ║ ║ 🌤 Weather: Brussels — 8°C, partly cloudy ║ ║ 📈 BTC: $97,432 (+2.3%) ETH: $3,891 (-0.4%) ║ ║ 📰 Top News: ║ ║ 1. EU approves new AI regulation framework ║ ║ 2. Tech stocks rally on earnings beat ║ ║ 💬 "The best way to predict the future is to ║ ║ create it." — Peter Drucker ║ ║ 💾 Disk: 45% used | RAM: 62% used ║ ║ ║ ╚══════════════════════════════════════════════════╝ ``` ## Integration with OpenClaw Perfect for cron jobs. Add to your `openclaw.json`: ```json { "cron": { "entries": { "morning-report": { "schedule": "0 7 * * *", "agent": "main", "prompt": "Run daily-business-report generate and send the result." } } } } ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: mrxolin/image-scanner (version 1.0.0)

\# image-scanner \## Description 扫描指定文件夹，识别图片格式、分析主色调和风格，自动分类整理摄影作品。 \## Triggers \- 扫描图片文件夹 \- 分析照片类型 \- 整理摄影作品 \- 识别图片颜色风格 \- 批量处理图片文件 \## Capabilities \- 扫描目录中的所有图片文件 \- 识别图片格式（JPG/PNG/RAW/HEIC/TIFF 等） \- 分析图片主色调（冷色/暖色/黑白/鲜艳） \- 按风格分类（人像/风景/静物/建筑等） \- 生成分类报告 \- 可选：自动创建子文件夹并移动文件 \## Usage ```bash openclaw skill image-scanner --path <目录路径> --action scan openclaw skill image-scanner --path <目录路径> --action classify --output <输出目录>


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: izombix/msmtp-send (version 1.0.0)

--- name: msmtp-send description: Send plain-text emails using your local msmtp config (Gmail app password already set up in ~/.msmtprc). Strictly send-only; no reading, fetching, or inbox access. command-dispatch: tool command-tool: exec metadata: openclaw: emoji: "✉️" requires: bins: ["msmtp"] homepage: "https://docs.openclaw.ai/tools/skills" # optional reference --- ## msmtp-send Skill This skill sends simple emails by executing your local `{baseDir}/send.sh` script via the exec tool. It relies on your existing `~/.msmtprc` for authentication (no new credentials stored here). ### When to use - Quick notifications, test messages, alerts, or short plain-text emails. - Recipient is known/trusted. - Keep bodies concise (plain text only; no attachments/HTML in this minimal version). ### How the agent invokes it The agent will automatically dispatch to the exec tool with a command like:


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: ivangdavila/study (version 1.0.1)

--- name: Study slug: study version: 1.0.1 description: Structure study sessions, manage materials, and prepare for exams with active recall techniques. changelog: Simplified structure, removed broken script references, explicit data storage metadata: {"clawdbot":{"emoji":"📚","requires":{"bins":[]},"os":["linux","darwin","win32"]}} --- ## Data Storage ``` ~/study/ ├── subjects/ # One folder per subject │ └── {subject}/ │ ├── materials/ # PDFs, notes, resources │ ├── flashcards.json │ ├── schedule.md │ └── progress.md ├── calendar/ # Exam dates, deadlines │ └── deadlines.json └── config.json # Preferences ``` Create on first use: `mkdir -p ~/study/{subjects,calendar}` ## Scope This skill: - ✅ Creates study plans in ~/study/ - ✅ Manages materials and flashcards - ✅ Tracks deadlines and exam dates - ✅ Guides study sessions with active recall - ❌ NEVER generates content student should create themselves - ❌ NEVER stores data outside ~/study/ ## Quick Reference | Topic | File | |-------|------| | Study techniques | `techniques.md` | | Subject strategies | `subjects.md` | | Exam preparation | `exams.md` | ## Core Rules ### 1. Workflow ``` Plan Semester → Weekly Schedule → Daily Sessions → Review → Exam Prep ``` ### 2. AI Scaffolds, Student Creates - AI asks questions → student writes summaries - AI structures sessions → student takes notes - AI generates quiz → student answers - NEVER generate the student's work ### 3. Adding a Subject 1. Create ~/study/subjects/{subject}/ 2. Set exam date in deadlines.json 3. Estimate weekly hours needed 4. Generate initial schedule ### 4. Study Session Flow 1. **Start**: What topic? How long? 2. **Active recall**: Questions first, answers second 3. **Practice**: Problems, not just reading 4. **Summary**: Student writes key points 5. **Schedule**: Next session based on spaced repetition ### 5. Exam Preparation When exam approaches (≤2 weeks): 1. Review all flashcards with SR 2. Practice past exams if available 3. Identify weak areas from progress.md 4. Create focused review plan ### 6. Configuration In ~/study/config.json: ```json { "level": "undergraduate", "technique": "pomodoro", "session_minutes": 25, "break_minutes": 5 } ``` ### 7. Progress Tracking In {subject}/progress.md: ``` ## Topics - [x] Chapter 1: Intro (mastered) - [~] Chapter 2: Basics (in progress) - [ ] Chapter 3: Advanced (not started) ## Weak Areas - Integration techniques - Proof by induction ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: y1035211-cloud/ma2-control (version 1.1.0)

--- name: ma2-control description: | grandMA2 实体控台控制技能。通过 Telnet 连接控台执行命令。 用于：选灯、调光、存 Cue、播放、查询等 MA2 操作。 触发条件：用户提到 MA2、grandMA2、灯光控台、选灯、存 Cue、执行器等。 执行命令必须使用 ~/ma2_bridge/ma2_cmd.sh 脚本。 metadata: openclaw: emoji: 🎛️ requires: bins: [bash, curl, nc] scripts: - ~/ma2_bridge/ma2_cmd.sh env: - MA2_IP_EXPECTED - MA2_TELNET_PORT_EXPECTED --- # MA2 控台控制 ## ⚠️ 使用前检查 ### 1. 控台连接检测 执行命令前，先检查控台是否在线： ```bash curl -s http://127.0.0.1:40100/health ``` 返回 `"ok":true` 表示连接正常。 ### 2. 常见错误处理 | 错误 | 原因 | 解决 | |------|------|------| | `timed out` | 控台IP不对或网络不通 | 确认控台IP和网段 | | `connection refused` | Telnet端口未开启 | 在控台设置里开启Remote | | `no service` | 桥接服务未启动 | 运行 `python3 ~/ma2_bridge/ma2_telnet_server.py` | --- ## 🚀 快速命令 ### 选灯 | 命令 | 说明 | |------|------| | `Fixture 1` | 单个 | | `Fixture 1 Thru 10` | 范围 | | `Fixture 1 + 3 + 5` | 多选 | | `Group 1` | 组 | ### 亮度 | 命令 | 说明 | |------|------| | `At Full` | 100% | | `At 50` | 50% | | `At 0` | 全关 | ### 属性 | 命令 | 说明 | |------|------| | `Attribute "COLOR1" At 30` | 颜色 | | `Attribute "PAN" At 127` | 水平 | | `Attribute "TILT" At 50` | 垂直 | ### 存储 | 命令 | 说明 | |------|------| | `Store Cue 1` | 存 Cue | | `Store Cue 1 "名称"` | 带名存 | | `Assign Cue 1 To Executor 1.1` | 到执行器 | ### 播放 | 命令 | 说明 | |------|------| | `Go Executor 1.1` | 触发 | | `Pause Sequence 1` | 暂停 | | `Goto Cue 5 Sequence 1` | 跳转 | ### 查询 | 命令 | 说明 | |------|------| | `List Cue` | 列表 Cue | | `List Fixture` | 列表灯具 | | `List Executor` | 列表执行器 | | `Info Cue 1` | Cue 详情 | --- ## ⚡ 执行方式 执行 MA2 命令**必须使用**： ```bash ~/ma2_bridge/ma2_cmd.sh "MA2命令" ``` 多条命令用 ` ; ` 分隔： ```bash ~/ma2_bridge/ma2_cmd.sh "Fixture 1 Thru 10 ; At Full" ``` --- ## 🎨 颜色值速查 | 颜色 | 值 | |------|-----| | 红 | 0 | | 橙 | 10 | | 黄 | 20 | | 绿 | 30 | | 青 | 40 | | 蓝 | 60 | | 紫 | 70 | | 品红 | 80 | ## 📍 位置值速查 | 方向 | PAN | TILT | |------|-----|------| | 左 | 0 | - | | 中 | 127 | 50 | | 右 | 255 | - | --- ## 🔧 配置说明 ### 环境变量 (可选) | 变量 | 默认值 | 说明 | |------|--------|------| | `MA2_IP_EXPECTED` | 192.168.1.11 | 控台IP | | `MA2_TELNET_PORT_EXPECTED` | 30000 | Telnet端口 | | `MA2_HTTP_PORT_OVERRIDE` | 自动检测 | 桥接服务端口 | ### 桥接服务 控台连接流程： 1. 控台开启 Telnet Remote (Settings → Network → Remote) 2. 本机运行：`python3 ~/ma2_bridge/ma2_telnet_server.py` 3. 通过桥接服务发送命令 --- ## 📖 详细文档 完整命令语法见：`references/MA2_COMMAND_SYNTAX.md` 模板库见：`references/MA2_TEMPLATES.md` 故障排查见：`references/MA2_TROUBLESHOOTING.md`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: lknik/news-feed (version 1.0.0)

--- name: news-feeds description: Fetch latest news headlines from major RSS feeds (BBC, Reuters, AP, Al Jazeera, NPR, The Guardian, DW). No API keys required. metadata: {"openclaw":{"requires":{"bins":["python3"]}}} --- # News Feeds Skill Fetch current news headlines and summaries from major international RSS feeds. Zero API keys, zero dependencies — uses only Python stdlib and HTTP. ## Available Commands ### Command: news **What it does:** Fetch latest headlines from all configured feeds (or a specific source). **How to execute:** ```bash python3 {baseDir}/scripts/news.py ``` ### Command: news from a specific source **What it does:** Fetch headlines from one source only. **How to execute:** ```bash python3 {baseDir}/scripts/news.py --source bbc python3 {baseDir}/scripts/news.py --source reuters python3 {baseDir}/scripts/news.py --source ap python3 {baseDir}/scripts/news.py --source guardian python3 {baseDir}/scripts/news.py --source aljazeera python3 {baseDir}/scripts/news.py --source npr python3 {baseDir}/scripts/news.py --source dw ``` ### Command: news by topic **What it does:** Fetch headlines filtered to a specific topic/keyword. ```bash python3 {baseDir}/scripts/news.py --topic "climate" python3 {baseDir}/scripts/news.py --source bbc --topic "ukraine" ``` ### Command: news with more items **What it does:** Control how many items per feed (default 8). ```bash python3 {baseDir}/scripts/news.py --limit 20 ``` ### Command: list sources **What it does:** Show all available feed sources and their categories. ```bash python3 {baseDir}/scripts/news.py --list-sources ``` ## Available Sources | Source | Categories | |-------------|------------------------------------------------| | bbc | top, world, business, tech, science, health | | reuters | top, world, business, tech, science, health | | ap | top | | guardian | top, world, business, tech, science | | aljazeera | top | | npr | top | | dw | top | ## When to Use - User asks for latest news, current events, headlines - User wants a news briefing or daily digest - User asks "what's happening in the world" - User asks about news on a specific topic - User asks for a morning briefing ## Output Format Returns markdown with headlines, short descriptions, publication times, and links. Grouped by source.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: codekungfu/db-design (version 1.0.0)

--- name: db-design description: Deep database design workflow—entities and relationships, keys and constraints, normalization vs denormalization, indexing strategy, integrity, and operational concerns. Use when designing OLTP schemas or reviewing greenfield data models. --- # DB Design Good OLTP design balances integrity, write paths, query patterns, and evolution—not “third normal form everywhere.” ## When to Offer This Workflow **Trigger conditions:** - Greenfield service schema or major new domain - Performance or integrity issues from ad-hoc tables - Multi-tenant isolation questions **Initial offer:** Use **six stages**: (1) domain & access patterns, (2) entities & relationships, (3) keys & constraints, (4) normalization trade-offs, (5) indexing & performance, (6) operations & evolution). Confirm RDBMS and scale expectations. --- ## Stage 1: Domain & Access Patterns **Goal:** List critical queries and writes: QPS, joins, filters, hot rows. **Exit condition:** Top access paths ranked by business importance. --- ## Stage 2: Entities & Relationships **Goal:** ER model; cardinality; optional vs required relationships. ### Practices - Clear table names; avoid opaque “data” blobs unless documented --- ## Stage 3: Keys & Constraints **Goal:** Primary keys (surrogate vs natural); foreign keys with explicit ON DELETE policy; unique constraints for business rules. ### Multi-tenant - `tenant_id` on rows that need isolation; composite keys or indexes as appropriate --- ## Stage 4: Normalization Trade-offs **Goal:** Normalize to reduce update anomalies; denormalize read hotspots with documented trade-offs. --- ## Stage 5: Indexing & Performance **Goal:** Indexes serve real queries; watch write amplification and index bloat. --- ## Stage 6: Operations & Evolution **Goal:** Migration strategy (expand/contract); backup/restore; PII columns flagged. --- ## Final Review Checklist - [ ] Access patterns drive schema - [ ] Keys, FKs, and constraints explicit - [ ] Multi-tenant isolation if applicable - [ ] Normalization decisions justified - [ ] Index plan aligned with queries - [ ] Migration and ops considerations noted ## Tips for Effective Guidance - NULL semantics and defaults matter for bugs and migrations. - Pair with **db-migrate** for online schema changes. ## Handling Deviations - Document stores: embed vs reference with consistency story.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: adelpro/private-web-search-searchxng (version 1.3.0)

--- name: private-web-search-searchxng description: Self-hosted private web search using SearXNG. Use when privacy is important, external APIs are blocked, you need search without tracking, or want to avoid paid search APIs. metadata: { "openclaw": { "requires": { "bins": ["docker", "curl", "jq"] }, "install": [ { "id": "docker", "kind": "bin", "command": "docker --version", "label": "Docker required", }, ], }, } --- # Private Web Search (SearXNG) Privacy-respecting, self-hosted metasearch engine for AI agents. ## Quick Setup ```bash # 1. Start container docker run -d --name searxng -p 8080:8080 -e BASE_URL=http://localhost:8080/ searxng/searxng # 2. Enable JSON API docker exec searxng sed -i 's/ formats:/ formats:\n - json/' /etc/searxng/settings.yml docker restart searxng # 3. Verify curl -sL "http://localhost:8080/search?q=test&format=json" | jq '.results[0]' ``` ## Usage ### Basic Search ```bash curl -sL "http://localhost:8080/search?q=YOUR_QUERY&format=json" | jq '.results[:10]' ``` ### Using the Helper Script ```bash ./scripts/search.sh "openclaw ai" 5 ``` ### Environment Variables | Variable | Default | Description | |----------|---------|-------------| | SEARXNG_PORT | 8080 | Container port | | SEARXNG_HOST | localhost | Server host | | BASE_URL | http://localhost:8080 | Public URL | ## Available Engines Google, Bing, DuckDuckGo, Brave, Startpage, Wikipedia, and more. ## Management ```bash docker start searxng # Start docker stop searxng # Stop docker logs searxng # View logs docker rm searxng -f # Remove ``` ## Troubleshooting | Issue | Solution | |-------|----------| | No results | Check `docker logs searxng` | | 403 Forbidden | Enable JSON format (step 2) | | Connection refused | Run `docker start searxng` |


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: fofo365/openclaw-self-analyzer (version 1.0.0)

# OpenClaw Self Analyzer - 自分析工具 深度分析OpenClaw架构，自动生成钩子和扩展。 ## 功能 - 架构分析 - 钩子点检测 - 自动生成钩子 - 代码扫描 - 报告生成 ## 使用 ### 完整分析 ```bash cd /root/.openclaw/workspace/skills/openclaw-self-analyzer python3 core/architecture_analyzer.py ``` ### 生成钩子 ```python from generators.hook_generator import HookGenerator generator = HookGenerator() hook = generator.generate_hook_package( hook_name='my_hook', hook_type='pre', stage='context_gather', logic='// your logic' ) ``` ### 生成报告 ```python from reporters.report_generator import ReportGenerator generator = ReportGenerator() files = generator.save_reports(Path('./reports')) ``` ## 架构映射 OpenClaw处理流水线: 1. **input_receive** - 接收用户输入 2. **context_gather** - 收集历史上下文 3. **memory_retrieve** - 检索记忆 4. **prompt_assemble** - 组装prompt 5. **token_check** - token检查 6. **context_compress** - 上下文压缩 7. **llm_submit** - 提交给LLM 8. **response_process** - 处理响应 9. **memory_store** - 存储新记忆 每个阶段支持三种钩子: - **pre** - 前置处理 - **post** - 后置处理 - **replace** - 完全替换 Copyright © 2025-2026 Edison Wang (fofo365/[REDACTED_SECRET]) Authors. All Rights Reserved.


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