Code Analysis Skill

Overview

Standardized workflow for reading, analyzing, and documenting codebases with attention-driven focus.

When to Use

Use this skill when you need to:

• Understand a new code module or system
• Analyze architecture and design patterns
• Document technical implementation details
• Prepare for code refactoring or integration
• Create technical documentation for teams
• Focus on core components (high-attention code analysis)

Attention-Driven Code Analysis (NEW in v1.2)

Identify core components in code using heuristic rules, optimizing analysis focus.

Attention Scoring System

# Use the attention_focus.py module
from attention_focus import CodeAttentionScorer

scorer = CodeAttentionScorer()
components = scorer.analyze_code_structure(file_content, file_name)
focus = scorer.get_analysis_focus(components)

Scoring Criteria

Attention Levels

Workflow with Attention Focus

1. Read code file
       ↓
2. Run attention_focus.py analysis
       ↓
3. Get prioritized component list
       ↓
4. Analyze HIGH attention components in detail
5. Analyze MEDIUM attention components briefly
6. Reference LOW attention components as needed
       ↓
7. Generate focused documentation

Benefits

• Reduce token consumption: Focus on 20% core code that provides 80% value
• Faster analysis: Skip boilerplate and utility code
• Better documentation: Highlight architectural decisions and critical paths
• Estimated improvement: 20-30% token reduction, 30% faster analysis

Workflow Template

Every code analysis task MUST follow this 4-step structure:

1. Objective

Definition: Clear statement of what needs to be understood

Format:

Objective: [What specifically needs to be understood]
Scope: [Boundaries of the analysis]
Depth: [Overview/Core logic/Detailed implementation]

Examples:

• Understand how the Tinker serialization library works
• Analyze the architecture design of PlayerSubsystem
• Master the SharedTable data table loading mechanism

2. Deliverables

Definition: Concrete outputs that will be produced

Format:

Documentation:
- [Doc name 1] - [Path] - [Content description]
- [Doc name 2] - [Path] - [Content description]

Additional outputs:
- [Code examples/Flowcharts/Architecture diagrams]

Documentation Standards:

• Place in repository/projects/[Project]/docs/ or docs/
• Use descriptive filenames: [Topic]-[Type].md
• Include code snippets and diagrams

3. Content

Definition: What will be covered in the analysis

Format:

Analysis content:
1. [Module A] - [Point 1, Point 2, Point 3]
2. [Module B] - [Point 1, Point 2, Point 3]
3. [Inter-module relationships] - [Interaction flow]

Include elements:
□ Core class/interface design
□ Data flow/Control flow
□ Key algorithms/Logic
□ Design patterns
□ Dependencies

4. Token Estimation

Definition: Estimated token consumption for the analysis

Format:

Token estimation:
- Code reading: ~[X,000] tokens
- Documentation: ~[X,000] tokens
- Total: ~[X,000]-[Y,000] tokens
- Estimated time: [X]-[Y] minutes

Estimation Guidelines:

Execution Flow

1. User Request
       │
       ▼
2. Clarify Requirements (if needed)
       │
       ├── Confirm analysis scope boundaries
       ├── Confirm key class definition locations (file paths)
       └── ⚠️ If definition locations are unclear, must ask the requester to explicitly specify
       │
       ▼
3. Output Analysis Plan (4-step structure)
       │
       ├── 1. Objective
       ├── 2. Deliverables  
       ├── 3. Content
       └── 4. Token Estimation
       │
       ▼
4. Wait for "Plan Approval" Confirmation
       │
       ▼
5. Execute Analysis
       │
       ├── Read relevant code
       ├── Understand logic
       └── Extract key points
       │
       ▼
6. Produce Documentation
       │
       ├── Write technical doc
       ├── Include code examples
       └── Create diagrams if needed
       │
       ▼
7. Report Completion
       └── Summary of findings

Usage Examples

Example 1: Simple Module Analysis

User: "Analyze PlayerQuest's progress update mechanism"

Plan Output:

## Code Analysis Plan

### 1. Objective
Understand PlayerQuest's progress update mechanism, including:
- How progress is stored and updated
- Task completion determination logic
- Interaction with the medal system

### 2. Deliverables
- `PlayerQuest-Progress-Analysis.md` 
  - Path: `repository/projects/BattleCore/docs/`
  - Contains: Flowchart + Core code + Data flow

### 3. Content
1. PlayerQuest class structure
2. updateProgress() / accumulateProgress() implementation
3. Task completion determination logic
4. Coupling relationship with PlayerMedal

### 4. Token Estimation
- Code reading: ~5,000 tokens
- Documentation: ~3,000 tokens
- Total: ~8,000 tokens
- Estimated time: 15-20 minutes

Example 2: Complex System Analysis

User: "Understand the overall skill system architecture"

Plan Output:

## Code Analysis Plan

### 1. Objective
Fully understand the FAbility skill system architecture:
- Core class hierarchy
- State machine implementation
- Skill lifecycle
- Integration with the battle core

### 2. Deliverables
- `FAbility-Architecture.md`
  - Path: `repository/projects/BattleCore/docs/`
  - Contains: Architecture diagram + Class diagram + Real-world examples
- `Skill-Lifecycle-Flow.md`
  - Complete skill lifecycle flow

### 3. Content
1. FAbility base class design
2. State machine (EAbilityState)
3. Concrete skill implementation examples
4. CD/Cost system
5. Event system

### 4. Token Estimation
- Code reading: ~30,000 tokens
- Documentation: ~15,000 tokens
- Total: ~45,000 tokens
- Estimated time: 60-80 minutes

Important Rules

Definition Location Confirmation Rule (Critical)

Rule: When definition locations are unclear during code analysis, must ask the requester to explicitly specify the location in advance

Execution Steps:

1. After receiving an analysis request, first attempt to locate the definition files of key classes/functions
2. If the location cannot be determined through file name or class name search, immediately stop analysis
3. Ask the requester: "Please provide the definition file path for [ClassName]"
4. After receiving a clear path, continue analysis

Example:

❌ Incorrect approach:
User: "Analyze NetMessage implementation"
AI: (Blindly search, can't find correct location, analyze based on assumptions)

✅ Correct approach:
User: "Analyze NetMessage implementation"
AI: "Please provide the definition file path for NetMessage,
       Search found possible locations:
       - Server/Network/NetMessage.h
       - Server/Common/NetMessage.h
       Please confirm which file?"
User: "In Server/Network/NetMessage.h"
AI: (Start analysis based on explicit path)

Best Practices

Do's

✅ Define clear scope boundaries, avoid over-analysis ✅ Provide specific file paths and line numbers ✅ Include actual code snippets ✅ Use diagrams to aid understanding (text descriptions) ✅ Estimates should be conservative, leave buffer

Don'ts

❌ Don't use vague scopes ("analyze the entire project") ❌ Don't omit key deliverable information ❌ Don't underestimate token consumption for complex systems ❌ Don't mix multiple unrelated objectives ❌ Don't blindly guess when definition locations are unclear (must ask the requester to explicitly specify file paths)

Integration with Other Skills

• knowledge-base-cache: Store analysis results in knowledge base
• git-workflow: Commit analysis documents
• skill-creator: Create specialized analysis skills from patterns

Template Quick Reference

## Code Analysis Plan

### 1. Objective
[Specific objective description]
Scope: [Scope boundaries]
Depth: [Overview/Core/Detailed]

### 2. Deliverables
- [Doc name] - [Path] - [Description]

### 3. Content
1. [Module A] - [Key points]
2. [Module B] - [Key points]
3. [Relationships] - [Flow]

### 4. Token Estimation
- Code reading: ~[X,000] tokens
- Documentation: ~[X,000] tokens
- Total: ~[X,000]-[Y,000] tokens
- Estimated time: [X]-[Y] minutes

Version History

• v1.2 (2026-02-12) - Added Attention-Driven Analysis
  • Added: attention_focus.py module
  • Added: Heuristic code attention scoring
  • Added: Three-level analysis depth (High/Medium/Low)
  • Optimized: 20-30% token reduction
  • Optimized: 30% faster analysis speed
• v1.1 (2026-02-10) - Added critical rule
  • Added: Definition location confirmation rule
  • Added: Important Rules section
  • Updated: Execution Flow added path confirmation step
  • Updated: Best Practices added Don'ts rules
• v1.0 (2026-02-10) - Initial release
  • 4-step workflow structure
  • Token estimation guidelines
  • Usage examples and templates