OpenClaw/clawhub-security-signals huggingface dataset:
Skill: leooooooow/product-angle-ideas (version 1.0.0)

--- name: product-angle-ideas description: Generate simple product marketing angles for TikTok and creator content. Use when you need clearer selling directions from product facts and audience pain points. --- # Product Angle Ideas ## Skill Card - **Category:** Content Conversion - **Core problem:** Teams know product details but struggle to turn them into clear selling angles. - **Best for:** TikTok Shop operators, creators, and affiliate teams preparing scripts or briefs. - **Expected input:** Product facts, audience segment, pain points, evidence, constraints. - **Expected output:** Angle list + hook lines + risk notes + test order. ## Workflow 1. Check input quality and missing essentials. 2. Build angle ideas from audience pain + product proof. 3. Draft short hooks for each angle. 4. Remove risky or unverifiable claims. 5. Recommend a practical test order. ## Output format Return in this order: 1. Input check 2. Angle list (>=8) 3. Hook lines 4. Risk notes 5. Test order ## Quality and safety rules - Keep angle names plain and human. - No fake guarantees or fabricated evidence. - Tie each angle to one audience pain. - Prefer action-ready output over abstract strategy talk. ## License Copyright (c) 2026 **Razestar**. This skill is provided under **CC BY-NC-SA 4.0** for non-commercial use. You may reuse and adapt it with attribution to Razestar, and share derivatives under the same license. Commercial use requires a separate paid commercial license from **Razestar**. No trademark rights are granted.

path: references/output-template.md
content: # Output Template

## 1) Input check
- Product facts:
- Audience:
- Missing info:

## 2) Angle list
| Angle | Best Audience | Why it works | Suggested hook |
|---|---|---|---|

## 3) Hook lines
- 

## 4) Risk notes
- 

## 5) Test order
- 


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: syed-tahmid99/lead-intel-analyzer (version 1.0.1)

# AI Lead Intelligence Generator ## Description Generates actionable B2B sales intelligence for any company to support cold outreach, lead qualification, and personalized prospecting. Produces structured insights including: - Company overview and business model - Target persona analysis - Likely pain points - Sales opportunities - Personalized outreach angles ## When to Use Trigger this skill when the user says things like: - "Research [company] for me" - "Give me a lead report on [company]" - "I want to cold email [company], help me prepare" - "Qualify [company] as a lead" ## Input Format The user provides: - Company name (required) - Website URL (optional) - Target persona (optional, e.g., "Head of Sales", "CEO") Example: "Research Notion for cold outreach targeting their Head of Marketing" ## Execution Instructions ### Step 1 — Extract inputs Parse from the user message: - company_name - target_persona (default: "decision maker" if not provided) - website_url (if provided) ### Step 2 — Call the backend API Send a POST request to the live backend: POST https://ai-lead-intelligence-acet.onrender.com/analyze-lead Headers: - Content-Type: application/json - X-Access-Token: [user's API key] Body: { "company": "<company_name>", "persona": "<target_persona>" } ### Step 3 — Format output Structure the response exactly as: --- **LEAD INTELLIGENCE REPORT** **Company:** [Name] **Target Persona:** [Role] **Company Overview** [2-3 sentence summary of what the company does, size, market] **Business Model** [How they make money, customer segments] **Key Pain Points** - [Pain point 1] - [Pain point 2] - [Pain point 3] **Sales Opportunities** - [Opportunity 1] - [Opportunity 2] **Outreach Angles** - [Angle 1 — specific and personalized] - [Angle 2 — specific and personalized] **Suggested Opening Line** [One punchy cold email or DM opener tailored to persona] --- ## Output Guidelines - Be specific, not generic - Prioritize sales relevance - Keep each section concise and scannable - Avoid filler phrases like "In conclusion" or "It's worth noting" ## Access Free tier: 3 reports/month (LLM-native, no API key needed) Pro ($9/month): Unlimited reports + real Hunter.io enrichment data Get your API key: https://your-lemonsqueezy-link.com ## Error Handling If API is unreachable: - Respond: "Unable to fetch lead intelligence at the moment. Please try again later." If company is completely unknown: - State clearly: "Limited public data available for this company." - Still attempt outreach angles based on industry/niche if detectable ## Version Notes v1.1.0 — Live backend connected. Real company enrichment via Hunter.io + Groq LLaMA 3.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: charlie-morrison/api-diff (version 1.0.1)

--- name: api-diff description: Compare two OpenAPI 3.x or Swagger 2.0 specs and generate a changelog of breaking and non-breaking changes. Detect removed endpoints, new required parameters, type changes, schema modifications, enum changes, security changes, server URL changes, and deprecations. Use when asked to diff APIs, compare API versions, detect breaking changes, generate API changelogs, or review API spec changes. Triggers on "API diff", "API changelog", "breaking changes", "OpenAPI compare", "spec diff", "API version compare". --- # API Diff — Changelog Generator Compare two OpenAPI/Swagger specs and generate a detailed changelog with breaking change detection. ## Quick Diff ```bash python3 scripts/api_diff.py old-spec.json new-spec.json ``` ## Output Formats ```bash # Text (default) python3 scripts/api_diff.py old.json new.json # JSON python3 scripts/api_diff.py old.json new.json --format json # Markdown python3 scripts/api_diff.py old.json new.json --format markdown ``` ## CI/CD Integration ```bash # Fail if breaking changes found python3 scripts/api_diff.py old.json new.json --fail-on-breaking echo $? # 0 = no breaking, 1 = breaking found # Show only breaking changes python3 scripts/api_diff.py old.json new.json --breaking-only ``` ## What It Detects ### Endpoint Changes | Change | Breaking? | Description | |--------|-----------|-------------| | Endpoint removed | Yes | Path+method no longer exists | | Endpoint added | No | New path+method | | Endpoint deprecated | No | Marked as deprecated | ### Parameter Changes | Change | Breaking? | Description | |--------|-----------|-------------| | Required param added | Yes | New mandatory parameter | | Optional param added | No | New optional parameter | | Param removed (required) | Yes | Required parameter removed | | Param type changed | Yes | Data type changed | | Param became required | Yes | Optional → required | | Param became optional | No | Required → optional | ### Schema Changes | Change | Breaking? | Description | |--------|-----------|-------------| | Schema removed | Yes | Definition removed | | Required property added | Yes | New mandatory field | | Optional property added | No | New optional field | | Property removed | Yes | Field removed | | Property type changed | Yes | Data type changed | | Enum value removed | Yes | Allowed value removed | | Enum value added | No | New allowed value | ### Other Changes | Change | Breaking? | Description | |--------|-----------|-------------| | Response code removed | Yes | HTTP status no longer returned | | Response code added | No | New HTTP status | | Security changed | Yes | Auth requirements changed | | Server URLs changed | No | Base URL changed | | API version changed | No | Info version updated | ## Requirements - Python 3.6+ - No external dependencies (stdlib only) - Input: JSON format OpenAPI 3.x or Swagger 2.0 specs


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: athola/nm-cartograph-call-chain (version 1.0.2)

--- name: call-chain description: | Trace execution paths from entry points through the code knowledge graph. Shows call chains with criticality scores and generates Mermaid flowcharts version: 1.9.5 metadata: {"openclaw": {"homepage": "https://github.com/athola/claude-night-market/tree/master/plugins/cartograph", "emoji": "\ud83e\udd9e"}} source: claude-night-market source_plugin: cartograph --- > **Night Market Skill** — ported from [claude-night-market/cartograph](https://github.com/athola/claude-night-market/tree/master/plugins/cartograph). For the full experience with agents, hooks, and commands, install the Claude Code plugin. # Call Chain Tracing Trace execution flows through the codebase using the code knowledge graph. ## Prerequisites This skill requires the **gauntlet** plugin for graph data. Discover it: ```bash GRAPH_QUERY=$(find ~/.claude/plugins -name "graph_query.py" -path "*/gauntlet/*" 2>/dev/null | head -1) ``` **If gauntlet is not installed**: Fall back to static analysis. Use `grep` to trace function calls and build a Mermaid diagram manually from import/call patterns. Skip graph-specific steps. **If installed but no graph.db**: Tell the user to run `/gauntlet-graph build`. ## Steps 1. **Accept target**: Get a function name or entry point from the user (or trace all entry points). 2. **Run flow tracing** (requires gauntlet): ```bash python3 "$GRAPH_QUERY" --action flows --depth 15 ``` To filter by entry point: ```bash python3 "$GRAPH_QUERY" --action flows --entry "main" ``` **Fallback (no gauntlet)**: Trace calls with rg (or grep): ```bash # Prefer rg (ripgrep) for speed; fall back to grep if command -v rg &>/dev/null; then rg -n "function_name\(" --type py . | head -20 else grep -rn "function_name(" --include="*.py" . | head -20 fi ``` Build the call tree manually from search results. 3. **Display as indented tree**: ``` main() [criticality: 0.72] -> validate_input() -> parse_config() -> process_data() -> db.execute_query() -> cache.store() -> send_response() ``` 4. **Generate Mermaid flowchart**: ```mermaid flowchart LR main --> validate_input main --> process_data main --> send_response validate_input --> parse_config process_data --> db.execute_query process_data --> cache.store ``` 5. **Show criticality breakdown**: - File spread: how many files the flow touches - Security sensitivity: auth/crypto code in the path - Test coverage gaps: untested nodes in the flow ## Criticality Scoring | Factor | Weight | Meaning | |--------|--------|---------| | File spread | 0.30 | Touches many files | | Security | 0.25 | Contains auth/crypto code | | External calls | 0.20 | Unresolved dependencies | | Test gap | 0.15 | Untested nodes in flow | | Depth | 0.10 | Deep call chains |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: extrastu/icp-exemption-skill (version 1.0.0)

--- name: apple-icp-exemption description: 生成 Apple 国区 ICP 豁免申请附件 PDF。当用户提到 ICP 备案、Apple 国区下架、ICP 豁免申请、App Store 中国区合规、申请例外批准等相关内容时，立即触发此 skill。收集用户的 Team ID、账户持有人姓名、App ID 等信息，调用脚本生成符合 Apple 要求的正式申请附件 PDF 文件。 --- # Apple 国区 ICP 豁免申请附件生成器 ## 概述 此 skill 用于生成 Apple App Store 中国大陆地区 ICP 备案豁免申请所需的正式附件 PDF。 ## 触发场景 - 用户提到 ICP 备案/豁免/例外 - 用户的 App 在国区被下架，需要申请豁免 - 用户需要准备 Apple 中国区合规材料 - 用户提到 "申请例外批准"、"ICP 相关申诉" 等 ## 信息收集 在生成 PDF 前，需要向用户收集以下信息： ### 必填信息 1. **Team ID**（团队 ID）— 在 App Store Connect → 账户 → 会员资格 中查看 2. **账户持有人法定姓名**（中文全名，与证件一致） 3. **App ID**（应用 ID）— 在 App Store Connect 的 App 详情页中查看 4. **申请日期**（默认今天，用户可更改） ### 信息收集方式 直接在对话中逐一询问，或一次性询问所有信息： ``` 请提供以下信息来生成 ICP 豁免申请附件： 1. Team ID（例如：ABCD123456） 2. 账户持有人法定姓名 3. App ID（例如：1234567890） 4. 申请日期（格式：YYYY年MM月DD日，留空则使用今天） ``` ## PDF 生成步骤 收集好所有信息后，运行以下命令生成 PDF： ```bash python3 /home/claude/icp-exemption-skill/scripts/generate_pdf.py \ --team-id "TEAM_ID" \ --name "法定姓名" \ --app-id "APP_ID" \ --date "YYYY年MM月DD日" \ --output "/mnt/user-data/outputs/ICP豁免申请附件.pdf" ``` 生成后使用 `present_files` 将 PDF 提供给用户下载。 ## 注意事项 - 生成的 PDF 需要用户**手写签名**后再提交 - 一个 App 对应一份附件，多个 App 需分别生成 - 提醒用户核实所有信息与 App Store Connect 账户完全一致 - PDF 使用中文，符合 Apple 中国区审核团队要求 ## 申请说明 生成附件后，告知用户提交流程： 1. 打印或在平板上手写签名 2. 扫描/拍照保存为 PDF 3. 登录 App Store Connect，找到被下架的 App 4. 点击「联系我们」→「ICP 相关问题」 5. 上传签名后的附件，说明 App 不联网或仅使用 Apple 服务 6. 提交等待 3-7 个工作日审核


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: derekhsu529/news-briefing (version 2.0.0)

--- name: news-briefing description: > Real-time AI news briefing for any topic. Say "给我看大谷翔平的新闻" or "AI今天有什么大新闻", and your agent instantly searches the web, summarizes the top stories in Chinese, and delivers a rich Feishu card with AI-powered deep insights — all collapsed cleanly for mobile reading. Trigger phrases: "看下XXX新闻", "查一下XXX最新动态", "XXX有什么新闻", "帮我追踪XXX", "briefing on XXX", "给我XXX的情报" version: 2.0.0 author: derekhsu529 tags: [news, briefing, ai-insights, feishu, realtime, chinese] --- # 📰 News Briefing > **让 AI Agent 成为你的私人情报官** > 任意话题 → 联网实时搜索 → 中文摘要 → AI深度洞察 → 飞书卡片，一句话触发。 ## 为什么选 News Briefing？ | 普通新闻 App | News Briefing | |------------|--------------| | 固定频道，被动推送 | **任意话题**，主动触发 | | 只有标题/摘要 | **AI深度洞察**：背景、逻辑、影响、信号 | | 英文/日文混杂 | **全程中文**输出 | | 需要打开 App | **飞书卡片**直达，不换 App | | 每天定时推送 | **随时触发**，也支持定时 | ## 快速开始 ### 对话触发（最简单） 直接对你的 Agent 说： ``` 看下大谷翔平的新闻 AI今天有什么大新闻 两会最新动态 帮我查一下特斯拉近期消息 ``` Agent 自动调用 News Briefing，发卡片到你的飞书。 ### 命令行调用 ```bash # 单主题 node {baseDir}/scripts/news-digest.mjs \ --topics "全球AI科技动态" \ --categories "AI" \ --title "🧠 AI情报简报" \ --target-user ou_xxx # 多主题合并（每日日报） node {baseDir}/scripts/news-digest.mjs \ --topics "全球AI科技,时政要闻" \ --counts "5,5" \ --categories "AI,GEO" \ --title "📰 每日情报简报" \ --target-user ou_xxx ``` ## 卡片效果 每条新闻： ``` 🧠 标题（25字以内，中文） 摘要一句话说清（50-60字，手机2-3行） [📄 查看原文] 💡 查看AI洞察 ▼ 📌 背景：为何发生 🔍 深层逻辑：核心驱动力 💥 影响：行业/市场影响 👁 关注点：值得追踪的信号 ``` ## 参数说明 | 参数 | 说明 | 默认 | |------|------|------| | `--topics` | 主题（逗号分隔多个） | 必填 | | `--counts` | 每主题条数 | `5` | | `--categories` | `AI/GEO/SPORT/BIZ/CUSTOM` | `AI` | | `--title` | 卡片标题 | 自动生成 | | `--target-user` | 飞书 open_id | 必填 | | `--no-insight` | 跳过 AI 洞察（更快） | - | | `--date` | 指定日期 | 今天 | ## 环境变量 | 变量 | 必须 | 说明 | |------|------|------| | `FEISHU_APP_ID` | ✅ | 飞书 App ID | | `FEISHU_APP_SECRET` | ✅ | 飞书 App Secret | | `PERPLEXITY_API_KEY` | ✅ | 联网实时搜索（Perplexity sonar） | | `PPIO_API_KEY` | 可选 | AI 洞察生成（缺少则跳过洞察） | | `HTTPS_PROXY` | 可选 | 代理地址 | | `TARGET_USER_ID` | 可选 | 默认接收人 open_id | ## 典型场景 ```bash # 🤖 AI 资讯 --topics "全球AI大模型最新进展" --categories "AI" --title "🧠 AI情报简报" --topics "OpenAI/Anthropic/DeepSeek最新动态" --categories "AI" # ⚾ 体育追踪 --topics "大谷翔平WBC最新动态" --categories "SPORT" --title "⚾ 大谷情报" --topics "MLB道奇最新战报" --categories "SPORT" # 🌍 时政财经 --topics "两会最新动态" --categories "GEO" --title "🏛️ 两会简报" --topics "中美贸易最新进展" --categories "GEO" # 💼 行业追踪 --topics "云计算IaaS市场动态" --categories "BIZ" --topics "苹果最新产品消息" --categories "BIZ" # 📰 每日综合日报（推荐配置） --topics "全球AI科技,时政要闻" --counts "5,5" --categories "AI,GEO" --title "📰 每日情报简报" ``` ## 定时推送（配合 OpenClaw Cron） 每天 8:30 自动推送： ```bash # 在 OpenClaw 中设置 cron，执行： node /path/to/news-briefing/scripts/news-digest.mjs \ --topics "全球AI科技,时政要闻" \ --title "📰 早间情报简报" \ --target-user ou_xxx ``` --- *由 [OpenClaw](https://openclaw.ai) 提供支持 · 作者: [@derekhsu529](https://clawhub.com/u/derekhsu529)*


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: cinience/aliyun-qwen-tts-voice-clone (version 1.0.0)

--- name: aliyun-qwen-tts-voice-clone description: Use when cloning voices with Alibaba Cloud Model Studio Qwen TTS VC models. Use when creating cloned voices from sample audio and synthesizing text with cloned timbre. version: 1.0.0 --- Category: provider # Model Studio Qwen TTS Voice Clone Use voice cloning models to replicate timbre from enrollment audio samples. ## Critical model names Use one of these exact model strings: - `qwen3-tts-vc-2026-01-22` - [REDACTED_SECRET] ## Prerequisites - Install SDK in a virtual environment: ```bash python3 -m venv .venv . .venv/bin/activate python -m pip install dashscope ``` - Set `DASHSCOPE_API_KEY` in your environment, or add `dashscope_api_key` to `~/.alibabacloud/credentials`. ## Normalized interface (tts.voice_clone) ### Request - `text` (string, required) - `voice_sample` (string | bytes, required) enrollment sample - `voice_name` (string, optional) - `stream` (bool, optional) ### Response - `audio_url` (string) or streaming PCM chunks - `voice_id` (string) - `request_id` (string) ## Operational guidance - Use clean speech samples with low background noise. - Respect consent and policy requirements for cloned voices. - Persist generated `voice_id` and reuse for future synthesis requests. ## Local helper script Prepare a normalized request JSON and validate response schema: ```bash .venv/bin/python skills/ai/audio/aliyun-qwen-tts-voice-clone/scripts/prepare_voice_clone_request.py \ --text "Welcome to this voice-clone demo" \ --voice-sample "https://example.com/voice-sample.wav" ``` ## Output location - Default output: [REDACTED_SECRET] - Override base dir with `OUTPUT_DIR`. ## Validation ```bash mkdir -p output/aliyun-qwen-tts-voice-clone for f in skills/ai/audio/aliyun-qwen-tts-voice-clone/scripts/*.py; do python3 -m py_compile "$f" done echo "py_compile_ok" > output/aliyun-qwen-tts-voice-clone/validate.txt ``` Pass criteria: command exits 0 and `output/aliyun-qwen-tts-voice-clone/validate.txt` is generated. ## Output And Evidence - Save artifacts, command outputs, and API response summaries under [REDACTED_SECRET]. - Include key parameters (region/resource id/time range) in evidence files for reproducibility. ## Workflow 1) Confirm user intent, region, identifiers, and whether the operation is read-only or mutating. 2) Run one minimal read-only query first to verify connectivity and permissions. 3) Execute the target operation with explicit parameters and bounded scope. 4) Verify results and save output/evidence files. ## References - `references/sources.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: unclekimwood/self-improving-agent-tuituitu (version 1.0.0)

--- name: self-improving-agent description: | Self-improvement and learning skill for the agent. Activate when: - User gives feedback on agent's performance - Agent makes mistakes or errors - User asks agent to "learn", "remember this", or "don't do X again" - Agent notices patterns of failures - After any task completion for self-reflection - Agent should update its own files based on new information --- # Self-Improving Agent Skill ## Core Principle **Every interaction is a learning opportunity.** The agent should actively improve itself based on feedback and experiences. ## Learning Triggers Activate this skill when: 1. User provides explicit feedback (positive or negative) 2. Agent makes an error or mistake 3. User says things like "learn from this", "remember", "don't do X again" 4. Agent notices a repeated mistake pattern 5. Task completes with lessons to extract ## Self-Improvement Workflow ### Step 1: Capture Learning After any triggering event, immediately: - Update `MEMORY.md` with new insights/lessons - Update `memory/YYYY-MM-DD.md` with the event - If mistake: add to "本猫犯过的错误/教训" section in MEMORY.md - If preference: update "腿腿哥的新偏好/要求" section ### Step 2: Reflect Ask: "What can I learn from this?" - What went wrong? - What should I do differently? - What files need updating? ### Step 3: Act Immediately update relevant files: - MEMORY.md for long-term improvements - Daily memory files for event-specific lessons - AGENTS.md or SOUL.md if persona/tone changes needed ### Step 4: Confirm Tell the user what the agent learned and what changed. ## File Update Guidelines ### MEMORY.md Updates Add to "本猫犯过的错误/教训" section: ``` - ❌ [Date]: [What went wrong] → 改进：[What to do differently] ``` Add to "腿腿哥的新偏好/要求" section: ``` - [New preference or requirement] (added [date]) ``` ### Daily Memory Updates Add to "做得不好/需要改进的地方": ``` - ❌ [What went wrong]: [Context] - 教训：[Learning] ``` ## Pattern Detection Actively look for: - Repeated mistakes (same error multiple times) - User preferences that aren't recorded - Missing knowledge that should be in memory - Workflow inefficiencies ## Quick Reference When user says "learn from this": 1. Read relevant memory files 2. Identify the lesson 3. Update MEMORY.md immediately 4. Confirm update to user When agent makes a mistake: 1. Acknowledge the mistake 2. Explain what went wrong 3. Update relevant files 4. Tell user what will change --- *This skill is part of the agent's self-improvement system.*


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: jerry-guo-mys/code-analyzer (version 1.0.0)

--- name: code-analyzer description: 深度代码分析工具。分析代码架构、执行流程、数据流、业务规则、外部依赖、数据模型，支持 DDD 模式识别（聚合根、实体、值对象、领域服务、仓储、领域事件、限界上下文）。使用场景：新代码库熟悉、架构文档生成、代码审查准备、技术债务评估、知识传承、DDD 模式识别。支持 Python、JavaScript、TypeScript、Rust、Java、Go 等 20+ 语言。 --- # Code Analyzer - 深度代码分析 专业的深度代码库分析工具，超越表面指标，深入理解： - 🏗️ **架构风格** - 识别 MVC、Clean Architecture、微服务等 - 🚀 **执行流程** - 入口点、调用图、执行路径追踪 - 💧 **数据流动** - 数据如何在系统中传输和转换 - 📜 **业务规则** - 从代码中提取验证逻辑、业务约束 - 🔗 **外部依赖** - API、数据库、第三方服务 - 📊 **数据模型** - 实体、DTO、值对象及其关系 - 🏛️ **DDD 模式** - 聚合根、实体、值对象、领域服务 ## 快速开始 ```bash # 完整深度分析 python3 scripts/analyze.py --path /path/to/project --output report.md # DDD 专项分析 python3 scripts/ddd-analyzer.py --path /path/to/project --output ddd-report.md # 排除特定目录 python3 scripts/analyze.py --path . --exclude "node_modules,vendor,target" --output analysis.md ``` ## 核心功能 ### 📊 代码质量评估 | 维度 | 说明 | |------|------| | **可维护性** | 代码结构、复杂度、可读性 | | **可测试性** | 单元测试覆盖率、可测试程度 | | **文档完整性** | 注释、文档覆盖率 | | **复杂度** | 圈复杂度、耦合度 | ### 🏛️ DDD 模式识别 | 模式 | 识别能力 | |------|----------| | **聚合根** | ✅ 识别一致性边界 | | **实体** | ✅ 识别有身份的对象 | | **值对象** | ✅ 识别不可变对象 | | **领域服务** | ✅ 识别无状态业务逻辑 | | **仓储** | ✅ 识别持久化抽象 | | **领域事件** | ✅ 识别事件驱动模式 | | **限界上下文** | ✅ 识别模块边界 | ### 📝 报告生成 - 执行摘要 - 质量评分 - 问题清单（分级） - 改进建议（分优先级） - 架构图（Mermaid） ## 输出示例 ```markdown # 代码分析报告 ## 执行摘要 - 总文件数：105 - 总代码行数：24,780 - 架构风格：Layered - 入口点：5 个 - 数据模型：45 个 - 业务规则：23 个 ## 质量指标 | 指标 | 评分 | 状态 | |------|------|------| | 可维护性 | 75/100 | 👍 | | 可测试性 | 82/100 | ✅ | | 文档完整性 | 68/100 | ⚠️ | | 复杂度 | 71/100 | 👍 | ## 发现的问题 ### 严重 (1) - 循环依赖：module_a ↔ module_b ### 主要 (3) - 高复杂度函数：calculate_score (复杂度=25) - 过大文件：admin.py (850 行) ``` ## 支持语言 | 语言 | 扩展名 | 分析深度 | |------|--------|----------| | Python | .py | 深度 (AST) | | JavaScript | .js | 深度 | | TypeScript | .ts | 深度 | | Rust | .rs | 深度 | | Java | .java | 中等 | | Go | .go | 中等 | | C/C++ | .c, .cpp | 基础 | ## 使用场景 ### 1. 新项目熟悉 ```bash python3 scripts/analyze.py --path /new/project --output onboarding.md ``` ### 2. 架构文档生成 ```bash python3 scripts/analyze.py --path . --output architecture.md ``` ### 3. 代码审查准备 ```bash python3 scripts/analyze.py --path ./feature --output pr-analysis.md ``` ### 4. 技术债务评估 ```bash python3 scripts/analyze.py --path . --exclude "tests" --output debt-review.md ``` ### 5. DDD 模式识别 ```bash python3 scripts/ddd-analyzer.py --path . --output ddd-analysis.md ``` ## 与 AI 助手配合 **Claude/Codex:** ``` "分析这个代码库并解释： 1. 主要入口点是什么？ 2. 核心数据模型有哪些？ 3. 编码了哪些业务规则？ 4. 数据如何在系统中流动？" ``` AI 会： 1. 运行 code-analyzer 2. 解读分析报告 3. 提供针对性解释 4. 回答具体问题 ## 最佳实践 详见 [references/best-practices.md](references/best-practices.md)： - 代码分析方法论 - 架构识别技巧 - DDD 模式识别指南 - 质量改进建议 ## 参见 - [OpenClaw 文档](https://docs.openclaw.ai) - [ClawHub Skills](https://clawhub.com)


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