project-analyzer（项目分析器）

Project Analyzer

This skill provides a systematic approach to analyzing projects with structured reporting and visual diagrams.

When to Use This Skill

Use this skill when the user:

• Asks to "analyze", "review", or "evaluate" a project
• Wants to understand the architecture of a codebase
• Needs a detailed evaluation of a project (local or remote)
• Requests a project report or summary
• Mentions "I want to analyze [project name/path]"
• Asks for recommendations about a specific project

Supported Project Types

Local Projects (Primary):

• Local directory paths: ~/work/my-project
• Current directory: .
• Relative paths: ./my-project
• Absolute paths: /Users/ccc/work/todo/kubernetes

Remote Projects (Optional):

• GitHub repositories: owner/repo
• Git URLs: https://github.com/owner/repo

Workflow Overview

The analysis follows a 12-step sequential process with progress reporting:

1. 📋 Project Basic Info - Basic metadata (language, file count, structure)
2. 🏗️ Project Structure - Directory structure and module relationships
3. 🛠️ Tech Stack - Dependencies and frameworks
4. 🎯 Core Features - Key features with sequence diagram
5. 🏛️ Architecture Design - Architecture patterns with diagrams
6. 📊 Code Quality - Code style, testing, complexity
7. 📚 Documentation Quality - README, API docs, guides
8. 📈 Project Activity - Commits, issues, PRs (from git if available)
9. ✅ Pros/Cons - Strengths and weaknesses
10. 🎯 Use Cases - When to use/not use
11. 💡 Learning Value - What's worth learning
12. 📝 Summary - Final verdict

Advanced Deep-Dive Analysis Mode

For complex or technical projects, enable Deep Analysis Mode which adds:

1. 🔧 Source Code Deep Dive - Key code paths, function call chains
2. ⚙️ Implementation Mechanics - Internal mechanisms and data flows
3. 🔍 Component Analysis - Deep dive into critical components
4. 📐 Protocol & Interface Analysis - API contracts and protocols
5. 🚀 Workflow Tracing - End-to-end flow analysis
6. 🛡️ Security Analysis - Security mechanisms and vulnerabilities
7. ⚡ Performance Analysis - Performance bottlenecks and optimizations
8. 🧪 Testing Strategy Analysis - Testing approaches and coverage

Analysis Process

Step 0: Preparation

1. Initialize Heartbeat Detection - Create memory-based progress tracking

- Write analysis start status to OpenClaw memory: memory/YYYY-MM-DD.md - Record current project path, analysis mode, and start timestamp - This prevents analysis interruption and enables resume capability

1. Read the template from ~/.agents/skills/project-analyzer/TEMPLATE.md
2. Create analysis directory at [project-path]/ai-analysis-docs/

- Important: Analysis documents are saved INSIDE the project being analyzed - Example: /Users/ccc/work/todo/kubernetes/ai-analysis-docs/

1. Create TODO list using the template from CHANGELOG_TEMPLATE.md

- Create analysis-todo.md with all planned topics - Initialize all topics with "Not Started" status - Set estimated times and priorities for each topic

1. Gather project info using:

For Local Projects (Primary):

• File system analysis: directory structure, file counts
• README analysis: local README files
• Code analysis: local source code examination
• Configuration files: package.json, go.mod, Cargo.toml, etc.
• Build scripts: Makefile, build.sh, etc.

For Remote Projects (Optional):

• GitHub API: gh api repos/owner/repo
• gh repo view owner/repo --json description,stargazersCount,forksCount,primaryLanguage,licenseInfo
• Web fetch for README and documentation
• Code structure exploration via gh api or git clone

Step 1-N: Sequential Analysis (Progressive)

For each of the 12 topics:

1. Update Heartbeat - Write progress to OpenClaw memory

- Update memory/YYYY-MM-DD.md with current topic and progress - This ensures analysis can resume if interrupted

1. Report starting to user with format: 🔵 [Topic Name] started (progress X/12) 📋 Analysis scope: [brief description of what will be analyzed] 🎯 Focus areas: [key aspects to investigate] 🔄 Starting analysis...
2. Update analysis-todo.md - Mark current topic as "In Progress"
3. Analyze the topic (collect info, create diagrams as needed)
4. Create individual topic document and save to project's ai-analysis-docs directory

- 🎉 File creation feedback: Immediately report when file is created - Format: 📄 Created: [file-path]

1. Update the main analysis file with findings

- 📝 File update feedback: Report when main file is updated - Format: 🔄 Updated: [main-analysis-file]

1. Update changelog.md with document creation record

- 📋 Changelog feedback: Report changelog update - Format: 📋 Updated: changelog.md

1. Update analysis-todo.md - Mark current topic as "Completed" and update progress statistics

- ✅ Progress feedback: Report progress update - Format: 📊 Updated: analysis-todo.md (progress X/12)

1. Update Heartbeat - Write completion to memory and mark topic complete
2. Report completion to user with format:

✅ [Topic Name] completed (progress X/12)

[Key findings summary]

📁 Files created/updated:
• Created: [topic-file-path]
• Updated: [main-analysis-file]
• Updated: changelog.md
• Updated: analysis-todo.md

🔄 Continuing to next topic...

1. Automatically proceed to next topic immediately (no user confirmation needed)

Important:

1. Always report when STARTING each topic analysis
2. Provide immediate file creation feedback
3. Report completion summary
4. Then automatically continue to the next topic without waiting for user confirmation

Final Step: Complete

After finishing all 12 topics:

1. Update Heartbeat - Mark analysis as complete in memory

- Write completion status to memory/YYYY-MM-DD.md - Clear active analysis flag to prevent resume attempts

1. Present summary with key insights
2. Show file location: [project-path]/ai-analysis-docs/[project-name]-analysis.md

- Example: /Users/ccc/work/todo/kubernetes/ai-analysis-docs/kubernetes-analysis.md

1. Offer follow-up (e.g., "Want me to dive deeper into any specific area?")

Information Gathering Strategy

For Local Projects (Primary)

For Basic Info (Topic 1):

# Directory analysis
ls -la [project-path]
find [project-path] -type f | wc -l
find [project-path] -name "README*" -o -name "readme*"

# Language detection
find [project-path] -name "*.go" | wc -l
find [project-path] -name "*.js" | wc -l
find [project-path] -name "*.py" | wc -l

# Configuration files
ls [project-path]/*.json [project-path]/*.mod [project-path]/*.toml

For Project Structure (Topic 2):

# Directory tree
tree -L 3 [project-path]  # or: find [project-path] -type d | head -20

# File statistics
find [project-path] -type f -name "*.go" | head -10
find [project-path] -type f -name "*.md" | head -10

# Key directories
ls -la [project-path]/cmd/
ls -la [project-path]/pkg/
ls -la [project-path]/src/

For Tech Stack (Topic 3):

# Check for dependency files
cat [project-path]/package.json
cat [project-path]/go.mod
cat [project-path]/requirements.txt
cat [project-path]/Cargo.toml

# Build tools
ls [project-path]/Makefile
ls [project-path]/build.sh
cat [project-path]/.github/workflows/*.yml

For Activity (Topic 8):

# Git history (if available)
cd [project-path] && git log --oneline -10
git log --since="1 month ago" --oneline | wc -l
git log --since="1 year ago" --pretty=format:"%h %ad" --date=short | head -10

For Remote Projects (Optional)

For Basic Info (Topic 1):

gh api repos/owner/repo

For Project Structure (Topic 2):

gh api repos/owner/repo/git/trees/main?recursive=1

For Tech Stack (Topic 3):

# Common dependency files
gh api repos/owner/repo/contents/package.json
gh api repos/owner/repo/requirements.txt
gh api repos/owner/repo/Cargo.toml
gh api repos/owner/repo/go.mod

For Activity (Topic 8):

gh api repos/owner/repo/issues?state=open&per_page=10
gh api repos/owner/repo/pulls?state=open&per_page=10
gh api repos/owner/repo/stats/commit_activity

Mermaid Diagram Guidelines

Use these diagrams based on project type:

Example Module Graph:

graph LR
    A[Core Module] --> B[Utils]
    A --> C[Config]
    D[API] --> A
    E[Tests] --> A

Example Sequence Diagram:

sequenceDiagram
    User->>Frontend: Action
    Frontend->>Backend: API Call
    Backend->>DB: Query
    DB-->>Backend: Data
    Backend-->>Frontend: Response
    Frontend-->>User: Result

Progress Reporting Format

Always report after completing each topic:

✅ [Topic Name] completed (progress X/12)

[2-3 bullet points of key findings]

[Optional: Show a small preview of the section content]

📁 Files created/updated:
• Created: [specific-file-path]
• Updated: [specific-file-path]
• Updated: changelog.md
• Updated: analysis-todo.md (progress X/12)

🔄 Continuing to next topic...

CRITICAL: Every topic completion MUST include explicit file operation feedback showing exactly which files were created and updated.

Template and Guide Locations

• Analysis template: ~/.agents/skills/project-analyzer/TEMPLATE.md
• Changelog template: ~/.agents/skills/project-analyzer/CHANGELOG_TEMPLATE.md
• Progressive workflow guide: ~/.agents/skills/project-analyzer/WORKFLOW.md
• Documentation guidelines: ~/.agents/skills/project-analyzer/DOCUMENTATION_GUIDELINES.md
• Path storage guide: ~/.agents/skills/project-analyzer/PATH_GUIDE.md
• Usage examples: ~/.agents/skills/project-analyzer/EXAMPLE_WORKFLOW.md
• Output directory: [project-path]/ai-analysis-docs/ (INSIDE the analyzed project)
• Output naming: [project-name]-analysis.md

Important: Document Location

Analysis documents are ALWAYS saved in the analyzed project directory:

[project-path]/ai-analysis-docs/
├── changelog.md
├── [project-name]-analysis.md
├── [project-name]-progress-tracking.md
├── analysis-todo.md
├── topics/
└── assets/

Examples:

• Analyzing /Users/ccc/work/todo/kubernetes → Documents saved in /Users/ccc/work/todo/kubernetes/ai-analysis-docs/
• Analyzing /Users/ccc/work/my-project → Documents saved in /Users/ccc/work/my-project/ai-analysis-docs/
• Analyzing . (current directory) → Documents saved in ./ai-analysis-docs/

Example Response Pattern

When user says "Analyze /Users/ccc/work/todo/kubernetes":

Starting analysis of /Users/ccc/work/todo/kubernetes project...

🔵 Project Basic Info started (progress 1/12)
📋 Analysis scope: Project metadata, language statistics, file structure overview
🎯 Focus areas: Primary language, file counts, project path, README analysis
🔄 Starting analysis...

📋 Project Basic Info completed (progress 1/12)
- Main language: Go (95%+)
- Total files: 50,000+
- Project path: /Users/ccc/work/todo/kubernetes

📁 Files created/updated:
• Created: /Users/ccc/work/todo/kubernetes/ai-analysis-docs/topics/01-project-basic-info.md
• Updated: /Users/ccc/work/todo/kubernetes/ai-analysis-docs/kubernetes-analysis.md
• Updated: /Users/ccc/work/todo/kubernetes/ai-analysis-docs/changelog.md
• Updated: /Users/ccc/work/todo/kubernetes/ai-analysis-docs/analysis-todo.md (progress 1/12)

🔄 Continuing to next topic...

🔵 Project Structure started (progress 2/12)
📋 Analysis scope: Directory organization, module relationships, component layout
🎯 Focus areas: Main directories, core components, file distribution patterns
🔄 Starting analysis...

🏗️ Project Structure completed (progress 2/12)
- Main directories: cmd/, pkg/, staging/
- Core components: kube-apiserver, kubelet, kube-proxy

📁 Files created/updated:
• Created: /Users/ccc/work/todo/kubernetes/ai-analysis-docs/topics/02-project-structure.md
• Updated: /Users/ccc/work/todo/kubernetes/ai-analysis-docs/kubernetes-analysis.md
• Updated: /Users/ccc/work/todo/kubernetes/ai-analysis-docs/changelog.md
• Updated: /Users/ccc/work/todo/kubernetes/ai-analysis-docs/analysis-todo.md (progress 2/12)

🔄 Continuing to next topic...
[... continues through all 12 topics ...]

✅ Analysis completed!
Analysis documents saved: /Users/ccc/work/todo/kubernetes/ai-analysis-docs/kubernetes-analysis.md

Would you like to dive deeper into any specific area?

Important Notes

• Always complete all 12 topics - don't stop early unless user says "stop"
• 🔵 Report STARTING each topic - always inform user when beginning each topic analysis
• Report after each topic - immediately inform user when each topic is done
• Continue automatically - proceed to next topic without waiting for user confirmation
• Create individual topic documents - each topic gets its own markdown file
• Save incrementally - create and save each topic document immediately after analysis
• Update main document - consolidate all findings into the main analysis file
• 🎉 CRITICAL: File operation feedback - ALWAYS report every file creation and update operation
• Detailed file tracking - distinguish between "Created" (new files) and "Updated" (modified files)
• Use mermaid diagrams where appropriate - they add significant value
• Be specific - avoid generic comments, provide concrete details
• Cite sources - mention where info came from (GitHub, docs, etc.)
• Template-driven - follow the template structure closely

Analysis Behavior Guidelines

When user requests analysis:

1. Start immediately - no confirmation or questions needed
2. Complete topic by topic - report after each topic completion
3. Continue automatically - automatically start next topic after reporting
4. Complete fully - finish all topics unless user says "stop"

Reporting Format:

✅ [Topic Name] completed (progress X/12)

Key findings:
• Finding 1
• Finding 2
• Finding 3

📁 Files created/updated:
• Created: [topic-document-path]
• Updated: [main-analysis-file]
• Updated: changelog.md
• Updated: analysis-todo.md (progress X/12)

🔄 Continuing to next topic...

User Experience:

• Users can see real-time progress
• Clear feedback when STARTING each topic - users know what's being analyzed next
• Clear feedback after each topic completion
• Immediate file creation feedback - users know exactly when files are created
• Detailed file operation tracking (created vs updated)
• No frequent interaction needed, analysis proceeds automatically
• Users can say "stop" at any time to interrupt the analysis
• Complete audit trail of all file operations

Incremental Documentation Strategy

File Organization

Each analysis generates multiple files:

[project-name]/
└── ai-analysis-docs/                 # All analysis documents in one place
    ├── analysis-todo.md             # Analysis TODO list (created in Step 0)
    ├── changelog.md                 # Analysis changelog (updated throughout)
    ├── [project-name]-analysis.md   # Main consolidated report
    ├── [project-name]-progress-tracking.md   # Progress tracking
    ├── topics/                      # Individual topic documents
    │   ├── 01-project-basic-info.md
    │   ├── 02-project-structure.md
    │   ├── 03-tech-stack.md
    │   ├── ...
    │   └── 20-testing-strategy-analysis.md   # For deep-dive mode
    └── assets/                      # Diagrams and images
        ├── architecture-diagram.md
        └── flowcharts/

Topic Document Template

Each individual topic document follows this structure:

# [Topic Name] - [Project Name]

## 📋 Topic Overview
- **Analysis Topic**: [Topic Name]
- **Project**: [Project Name]
- **Analysis Time**: [Timestamp]
- **Analysis Status**: ✅ Completed

## 🔍 Analysis Content

[Detailed analysis content for this specific topic]

## 📊 Key Findings

- [Key finding 1]
- [Key finding 2]
- [Key finding 3]

## 🔗 Related Resources

- Source location: [file:line]
- Reference docs: [links]
- Related topics: [links to other topic documents]

---

*This document was auto-generated by project-analyzer skill*
*Generated at: [timestamp]*

Deep Code Analysis Methodology (Advanced)

When conducting source code deep dives, follow this systematic approach:

Analysis Principles

1. From Architecture to Implementation: Understand overall architecture first, then dive into code
2. Flow-Driven: Trace through actual workflows to understand code paths
3. Visual + Code: Combine Mermaid diagrams with code annotations
4. Continuable: Provide guides for continued analysis
5. Practice-Oriented: Include configuration examples and troubleshooting

Code Analysis Structure

• Resource Structure Details: Source code locations, core type definitions, field explanations
• Working Principles: Architecture diagrams, key mechanisms, data flow processes
• Source Code Deep Analysis: Key code paths, function call chains, implementation details
• Implementation Comparison: Comparison of different implementations, pros/cons, use cases
• Configuration and Practice: Configuration examples, best practices, performance optimization
• Monitoring and Observability: Metrics, logging, monitoring solutions
• Troubleshooting: Common issues, troubleshooting steps, debugging commands

Progressive Analysis Workflow

1. Entry Point Analysis: Identify main entry points (main functions, API endpoints)
2. Data Structure Mapping: Understand core data structures and their relationships
3. Control Flow Tracing: Follow execution paths through the codebase
4. Dependency Analysis: Map dependencies between modules and components
5. Interface Analysis: Understand API contracts and communication patterns
6. State Management: Analyze how state is managed and transitions occur
7. Error Handling: Review error handling and recovery mechanisms
8. Extension Points: Identify plugin systems, hooks, or extension mechanisms

Analysis Triggers

Standard Analysis Mode (12 steps)

Use when user asks for:

• "analyze [project]"
• "review [project]"
• "evaluate [project]"
• General project understanding

Deep-Dive Mode (20 steps)

Use when user asks for:

• "deep dive into [project]"
• "source code analysis of [project]"
• "how does [project] work internally"
• "implementation details of [project]"
• Technical architecture evaluation
• Performance/security analysis requirements

Quick Assessment Mode (6 steps)

Use when user asks for:

• "quick overview of [project]"
• "brief analysis of [project]"
• "should I use [project]"
• Basic project evaluation (steps 1,3,4,9,10,12 only)

Related Skills and Resources

Related Skills

• github - For GitHub API access and repository data
• pretty-mermaid - For advanced Mermaid diagram rendering
• coding-router - For deeper code architecture analysis

Supporting Documentation

• WORKFLOW.md - Progressive analysis methodology and deep-dive workflows
• DOCUMENTATION_GUIDELINES.md - File organization standards and naming conventions
• PATH_GUIDE.md - Path storage rules and best practices
• EXAMPLE_WORKFLOW.md - Complete usage examples with Kubernetes project
• INTEGRATION_SUMMARY.md - Kubernetes analysis methodology integration details