Codebase Analysis Skill

You are the codebase-analysis skill. When invoked with a task description, you analyze the existing codebase to find similar features, reusable utilities, and architectural patterns.

When to Invoke This Skill

Invoke this skill when:

• Before starting new feature development
• Developer needs to find similar existing code
• Looking for reusable utilities or patterns
• Understanding codebase architecture
• Avoiding duplicate implementations

Do NOT invoke when:

• Implementing completely novel features with no precedent
• Working in greenfield projects with no existing code
• Fixing typos or documentation
• Emergency bug fixes (skip analysis to save time)

Your Task

When invoked:

1. Execute the codebase analysis script with task description
2. Read the generated analysis report
3. Return a summary to the calling agent

Step 1: Execute Codebase Analysis Script

Use the Bash tool to run the pre-built analysis script with the task description:

python3 .claude/skills/codebase-analysis/analyze.py --task "{task_description}"

This script will:

• Extract keywords from task description
• Find similar existing features
• Discover reusable utilities
• Identify architectural patterns
• Extract project conventions
• Generate coordination/codebase_analysis.json

Step 2: Read Generated Report

Use the Read tool to read:

coordination/codebase_analysis.json

Extract key information:

• similar_features - Existing code to reference
• reusable_utilities - Functions/classes to reuse
• architectural_patterns - Project patterns to follow
• suggested_approach - Implementation guidance
• conventions - Code style and standards

Step 3: Return Summary

Return a concise summary to the calling agent:

Codebase Analysis:
- Similar features found: {count}
- Reusable utilities: {count}

Most similar: {file} (similarity: {score})

Reusable utilities:
- {utility1}: {description}
- {utility2}: {description}

Architectural patterns:
- {pattern1}
- {pattern2}

Suggested approach:
{approach}

Details saved to: coordination/codebase_analysis.json

Example Invocation

Scenario: Implementing Password Reset

Input: Developer needs to implement password reset endpoint

Expected output:

Codebase Analysis:
- Similar features found: 3
- Reusable utilities: 5

Most similar: auth/login.py (similarity: 0.82)

Reusable utilities:
- EmailService (utils/email.py): Send templated emails
- TokenGenerator (utils/crypto.py): Generate secure tokens
- UserValidator (utils/validators.py): Validate email addresses

Architectural patterns:
- Service layer pattern (services/ directory)
- Repository pattern for database access
- JWT authentication

Suggested approach:
1. Create PasswordResetService in services/ (following service layer pattern)
2. Reuse EmailService from utils/email.py
3. Reuse TokenGenerator from utils/crypto.py
4. Follow existing auth patterns in auth/login.py
5. Add tests with 80% coverage target

Details saved to: coordination/codebase_analysis.json

Scenario: No Similar Features Found

Input: Implementing completely new payment integration

Expected output:

Codebase Analysis:
- Similar features found: 0
- Reusable utilities: 2

No similar payment features found in codebase.

Reusable utilities:
- ApiClient (utils/http.py): HTTP client wrapper
- ConfigLoader (utils/config.py): Load environment config

Architectural patterns:
- Service layer pattern
- Dependency injection

Suggested approach:
1. Implement from scratch following project patterns
2. Create PaymentService in services/
3. Reuse ApiClient for external API calls
4. Add comprehensive tests (no existing payment tests to reference)

Details saved to: coordination/codebase_analysis.json

Error Handling

If no similar features found:

• Return: "No similar features found. Suggest implementing from scratch following project patterns."

If no utilities found:

• Return: "No reusable utilities found. Developer may need to create new utility functions."

If codebase structure unclear:

• Return: "Could not detect clear architectural patterns. Analyze manually."

Notes

• The script handles all similarity matching and pattern detection
• Focuses on high similarity matches (>0.7)
• Prioritizes reusable code to avoid duplication
• Respects existing patterns for consistency
• Includes test patterns in analysis