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XcluEzy7

serenasemantic-analysis

by XcluEzy7

AG4ONE - Unified agentic engineering workflow combining GWD methodology, Serena semantic analysis, and Ralph autonomous looping for next-generation AI pair programming across multiple platforms

0🍴 0📅 Jan 23, 2026

SKILL.md


name: serena:semantic-analysis description: Semantic Code Analysis Workflow

Semantic Code Analysis Workflow

This workflow enhances AG4ONE capabilities by integrating Serena's semantic code analysis and manipulation tools.

Purpose

Provide IDE-like capabilities to AI agents through semantic understanding of code structure, enabling efficient navigation and precise editing operations.

When to Use

  • Large codebases where grep/file reading is inefficient
  • Complex projects with many interconnected components
  • Precise editing needed at symbol/function level
  • Dependency analysis for understanding code relationships
  • Token optimization for cost-effective AI coding

Required Reading

  • @ag4one/serena/README.md - Serena integration overview
  • @AG4-STYLE.md - AG4ONE style and conventions
  • @.planning/STATE.md - Current project state

Process

Phase 1: Semantic Discovery

<step name="semantic_codebase_analysis" priority="first">
1. Use Serena to discover codebase structure:
   - Find all symbols (classes, functions, methods)
   - Map relationships and dependencies
   - Identify architectural patterns
2. Create semantic index of key components
3. Store analysis results in `SERENA-ANALYSIS.md`
</step>
<step name="semantic_pattern_search">
1. Use Serena semantic search instead of grep:
   - Find symbols by name patterns
   - Locate code by functionality
   - Search within specific scopes (files, directories)
2. Apply filters for language, type, accessibility
3. Retrieve only relevant code sections
</step>

Phase 3: Precise Editing

<step name="symbol_level_editing">
1. Use Serena symbol manipulation for changes:
   - Insert code at specific symbol locations
   - Replace function bodies while preserving signatures
   - Add methods to classes precisely
   - Modify imports and dependencies accurately
2. Validate edits with Serena's understanding
3. Verify surrounding code context remains intact
</step>

Phase 4: Dependency Management

<step name="relationship_analysis">
1. Use Serena to understand code relationships:
   - Find all references to modified symbols
   - Update dependent components automatically
   - Validate no broken references remain
2. Map impact of changes across codebase
3. Document dependencies for future iterations
</step>

Serena Tool Integration

Symbol Discovery

# Find all authentication-related symbols
serena find_symbol --pattern "auth*" --type function,class
# Search for database connection patterns
serena semantic_search --query "database connection" --language typescript

Precise Editing

# Add new method to UserService class
serena insert_after_symbol --class UserService --after_method --name validateEmail

Relationship Analysis

# Find all code using PaymentProcessor
serena find_referencing_symbols --symbol PaymentProcessor

Success Criteria

  • Semantic index created for codebase
  • Token usage reduced compared to file-based approaches
  • Precise symbol-level edits completed
  • All code relationships understood and maintained
  • No broken references or dependencies
  • Documentation updated with semantic findings

AG4ONE Enhancement

This workflow transforms AG4ONE from file-based to semantic-based operations:

Before: Read entire files, grep for patterns, string replacements After: Symbol discovery, semantic search, precise editing, relationship analysis

Result: More efficient AI coding with better context understanding and reduced token consumption.


AG4ONE Semantic Analysis - IDE-level capabilities for AI pair programming.

Score

Total Score

70/100

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