Serena

Serena

What I Do

Serena transforms LLMs into powerful coding agents that work directly on your codebase. Unlike basic tools that read entire files or perform string-based searches, Serena provides semantic code understanding at the symbol level, making code operations more efficient and accurate.

Key Capabilities

Symbol-Level Code Operations

Project Workflow

1. Project Creation: Configure project settings (language detection, indexing)
2. Project Activation: Make Serena aware of your project
3. Onboarding: Serena learns project structure and creates memories
4. Coding Tasks: Use semantic tools for efficient code operations

When to Use Me

Use this skill when:

• Working with structured codebases that benefit from symbol-level operations
• Need IDE-like capabilities (find references, rename symbols, navigate structure)
• Want to avoid reading entire files for small changes
• Need project-specific memories for context preservation
• Working with multi-file refactoring tasks

Universal Examples

Finding and Understanding Code

# Find all symbols matching a pattern
symbols = find_symbol(
    name_path_pattern="function_name",
    relative_path="src/",
    include_kinds=[12]  # Function kind
)

# Get overview of file structure
overview = get_symbols_overview(
    relative_path="src/main.py",
    depth=1  # Include methods/fields
)

# Find all references to a symbol
references = find_referencing_symbols(
    name_path="ClassName/method_name",
    relative_path="src/file.py"
)

Editing Code at Symbol Level

# Insert after a symbol definition
insert_after_symbol(
    body="\n    def new_method(self):\n        pass",
    name_path="ClassName/existing_method",
    relative_path="src/file.py"
)

# Rename symbol throughout codebase
rename_symbol(
    name_path="OldClassName",
    new_name="NewClassName",
    relative_path="src/"
)

# Replace entire symbol body
replace_symbol_body(
    body="def updated_function(param: Type) -> ReturnType:\n    new implementation",
    name_path="function_name",
    relative_path="src/file.py"
)

Project Memory Management

# Store project knowledge
write_memory(
    memory_file_name="architecture",
    content="""# Project Architecture
This project uses a layered architecture with:
- Data layer for persistence
- Service layer for business logic
- Presentation layer for UI
Key patterns: Repository, Service, Factory"""
)

# Recall project knowledge
memories = list_memories()
architecture = read_memory(memory_file_name="architecture")

File Operations

# Read specific file
content = read_file(relative_path="src/module.py")

# Create or update file
create_text_file(
    relative_path="new_file.py",
    content="# New module\n\ndef main(): pass"
)

# Replace content with pattern matching
replace_content(
    needle="old_function\(\)",
    repl="new_function()",
    mode="regex",
    relative_path="src/file.py"
)

Best Practices

1. Use Symbol Tools First: Always prefer find_symbol and symbol-based edits over file reading and string replacement
2. Create Memories: Store project context, architecture decisions, and important patterns in Serena memories
3. Onboarding: Let Serena perform onboarding for new projects to build initial context
4. Type Annotations: For dynamically typed languages, use type annotations to improve symbol detection
5. Modular Code: Serena works best with well-structured, modular codebases

Integration with Other Skills

• Combine with mcp-servers for MCP integration patterns
• Use with coding-principles for code quality during edits
• Leverage knowledge-management for cross-project knowledge sharing