Analysis takes too long on large codebases: Scope analysis to specific directories. Use --print with file path limits: “Analyze only src/api/ and src/services/, ignore everything else.” False positives in dead code detection: Exported functions may be used by external consumers not in the repo....

Automate Source Code Analysis with Claude 2026

Written by Michael Lip · Solo founder of Zovo · $400K+ on Upwork · 100% JSS Join 50+ builders · More at zovo.one

Last updated: April 21, 2026

The Workflow

Automate static code analysis tasks using Claude Code: calculate complexity metrics, map dependencies between modules, detect dead code, and generate architecture diagrams. Produces actionable reports without requiring expensive commercial analysis tools.

Expected time: 20-40 minutes for full analysis pipeline Prerequisites: Claude Code installed, project codebase, Python 3.10+ (for analysis scripts)

Setup

1. Create an Analysis Output Directory

mkdir -p ./analysis-reports

2. Configure CLAUDE.md for Analysis Tasks

# CLAUDE.md additions for code analysis

## Analysis Rules
- When analyzing code, read files but never modify them
- Report findings in markdown tables
- Include file paths relative to project root
- Calculate cyclomatic complexity as: decision_points + 1
- Flag functions over 50 lines or complexity over 10
- Count only production code (exclude test files, configs)

3. Install Optional Analysis Tools

# Python complexity analysis
pip install radon

# JavaScript/TypeScript
npm install -g madge typescript

# Verify
radon cc --version && madge --version
# Expected output:
# radon version numbers displayed
# madge version displayed

Usage Example

Run a comprehensive analysis pipeline:

#!/bin/bash
# analyze-project.sh — Full codebase analysis with Claude Code

PROJECT_DIR="${1:-.}"
REPORT_DIR="./analysis-reports/$(date +%Y%m%d)"
mkdir -p "$REPORT_DIR"

echo "=== Analyzing: $PROJECT_DIR ==="

# Step 1: Complexity Analysis
echo "--- Step 1: Complexity Metrics ---"
claude --print "Analyze the cyclomatic complexity of all functions in src/.
For each function with complexity > 5, report:
| File | Function | Lines | Complexity | Risk Level |
Risk levels: Low (1-5), Medium (6-10), High (11-15), Critical (>15)
Sort by complexity descending. Output as a markdown table." \
  > "$REPORT_DIR/complexity.md"

# Step 2: Dependency Mapping
echo "--- Step 2: Dependency Map ---"
claude --print "Map all import/require statements in src/.
Create a dependency matrix showing:
1. Which modules import which other modules
2. Circular dependencies (if any)
3. Modules with the most dependents (most imported by others)
4. Orphan modules (imported by nothing, import nothing)
Output as markdown with a table and a text-based dependency tree." \
  > "$REPORT_DIR/dependencies.md"

# Step 3: Dead Code Detection
echo "--- Step 3: Dead Code ---"
claude --print "Find dead code in src/:
1. Exported functions/classes never imported elsewhere in src/
2. Variables assigned but never read
3. Unreachable code after return/throw statements
4. Commented-out code blocks (more than 3 lines)
For each finding, show file:line and the dead code snippet.
Output as a markdown checklist." \
  > "$REPORT_DIR/dead-code.md"

# Step 4: Architecture Overview
echo "--- Step 4: Architecture ---"
claude --print "Generate an architecture overview of this project:
1. Layer diagram (which directories form which layers)
2. Data flow (how requests move through the system)
3. External dependencies (APIs, databases, services)
4. Entry points (main files, route handlers, CLI commands)
Use text-based diagrams with box-drawing characters.
Output as markdown." \
  > "$REPORT_DIR/architecture.md"

# Step 5: Summary
echo "--- Generating Summary ---"
claude --print "Read these analysis reports and create an executive summary:
$(cat "$REPORT_DIR/complexity.md")
$(cat "$REPORT_DIR/dead-code.md")
$(cat "$REPORT_DIR/dependencies.md")

Include:
- Total files analyzed
- Top 3 riskiest modules (by complexity)
- Estimated hours to address critical issues
- Priority action items (numbered list, max 5)
Output as markdown." \
  > "$REPORT_DIR/summary.md"

echo "=== Analysis complete. Reports in: $REPORT_DIR ==="
ls -la "$REPORT_DIR"

For TypeScript projects, combine with madge for visual dependency graphs:

# Generate dependency graph data
madge --json src/ > /tmp/deps.json

# Feed to Claude for analysis
claude --print "Analyze this dependency graph JSON and identify:
1. Circular dependency chains (list each cycle)
2. God modules (more than 10 incoming dependencies)
3. Suggested module boundary splits for modules over 500 lines

Dependency data:
$(cat /tmp/deps.json)" > "$REPORT_DIR/dependency-analysis.md"

For Python projects, use radon metrics as Claude input:

# Get raw complexity data
radon cc src/ -j > /tmp/complexity.json
radon mi src/ -j > /tmp/maintainability.json

# Claude interprets and prioritizes
claude --print "Given these code metrics:

Cyclomatic Complexity:
$(cat /tmp/complexity.json)

Maintainability Index:
$(cat /tmp/maintainability.json)

Identify the 5 modules most in need of refactoring.
For each, explain why and suggest a specific refactoring approach.
Output as an actionable markdown document." > "$REPORT_DIR/refactoring-plan.md"

Common Issues

Analysis takes too long on large codebases: Scope analysis to specific directories. Use --print with file path limits: “Analyze only src/api/ and src/services/, ignore everything else.”
False positives in dead code detection: Exported functions may be used by external consumers not in the repo. Add exclusions: “Exclude all functions exported from src/public-api.ts from dead code analysis.”
Dependency map is too complex to read: Break it into layers: “Show only dependencies between top-level directories, not individual files.”

Why This Matters

Manual code analysis of a 50,000-line codebase takes days. Claude Code produces equivalent insights in minutes, letting teams prioritize technical debt reduction with data rather than guesswork.