Codebase Analysis

The Developer extension includes an analyze tool that helps you understand code structure, track symbol usage, and explore call graphs across your codebase. It's automatically available when the Developer extension is enabled and supports file types for multiple programming languages.

Prompt:

"Show me the call graph for the authenticate function"

Output:

:::info Desktop I'll analyze how the authenticate function is used across your codebase.

Based on my analysis, here's how the authenticate function is used in your codebase:

Function Definition

The authenticate function is defined in auth.py at line 15. It takes username and password parameters and handles user authentication.

Incoming Calls (Functions that call authenticate)

• login_handler() in api.py:25 - The API endpoint that processes login requests
• admin_login() in admin.py:102 - Special authentication flow for admin users
• test_auth() in tests/test_auth.py:45 - Test case for authentication

Outgoing Calls (Functions that authenticate calls)

• validate_credentials() in db.py:102 - Checks credentials against the database
• log_attempt() in logger.py:45 - Records authentication attempts
• check_rate_limit() in security.py:78 - Prevents brute force attacks

This shows that authenticate is a central function in your authentication flow, called by multiple entry points and depending on database validation, logging, and security checks. :::

Analysis Modes

The analyze tool operates in three modes—Structure, Semantic, and Focus—depending on whether you’re analyzing directories, files, or symbols. Invoke it through natural language or direct commands with parameters.

Understanding Project Organization

Get a structural overview of your codebase by analyzing a directory—understand project organization, identify large files, and view codebase metrics.

Natural language:

• "Can you analyze the structure of my src/ directory?"
• "Give me an overview of this project's code structure"
• "What's the main entry point of this Python project?"

Direct commands:

# Get overview with default depth (3 levels)
analyze path="src/"

# Get overview limited to 2 subdirectory levels
analyze path="." max_depth=2

Inspecting a File

Get semantic details for a single file—see its functions, classes, and imports to understand structure and find specific implementations.

Natural language:

• "What functions are in main.py?"
• "Show me the structure of src/utils.py"

Direct commands:

# Get file details
analyze path="main.py"

# Analyze specific file
analyze path="src/utils.py"

Tracking a Symbol Across Files

Focus on a specific function, class, or method to see where it’s defined and how it’s called across files—useful for refactoring and debugging.

Natural language:

• "Trace the dependencies for the authenticate function"
• "Show me the call graph for UserClass"

Direct commands:

# Track function usage
analyze path="src/" focus="authenticate"

# Track with deeper call chains
analyze path="." focus="UserClass" follow_depth=3

Common Parameters

Best Practices

Handling Large Outputs

If the analysis results exceed 1000 lines, the tool returns a warning message instead of the analysis. Options for managing large outputs:

• Use force=true to bypass the warning and see the full output (may consume significant conversation context)
• Narrow your scope by analyzing a specific subdirectory or file
• Reduce depth with max_depth=1 or max_depth=2 for directories
• Delegate to a subagent to analyze and summarize without filling your conversation history, for example: "Use a subagent to analyze the entire src/ directory and summarize the main components"

Performance Tips

• Start with smaller scopes (specific files or subdirectories) before analyzing entire projects
• Use max_depth=1 or max_depth=2 to limit directory traversal depth
• Use .gooseignore and .gitignore files to exclude unnecessary files from analysis (like node_modules/, build artifacts, or sensitive files)