Code Context LLM: Generate Project Structure for AI-Assisted Development

Understanding Code Context LLM: Elevate Your LLM Interactions

Code Context LLM bridges the gap between your codebase and Large Language Models (LLMs) like ChatGPT, Claude, or GPT-4. It generates comprehensive Markdown outlines of your project's directory tree, empowering developers to enhance prompt engineering, improve AI-assisted code navigation, and facilitate intelligent code analysis.

Looking to generate a Markdown-based file tree for ChatGPT or other LLMs? Code Context LLM provides an out-of-the-box solution that's secure, customizable, and ready for your AI-driven development workflow.

Why Use Code Context LLM?

Whether you're working on a small project or managing a large codebase, Code Context LLM offers features to generate appropriate context for your LLM interactions:

• Secure Content Handling: Automatic redaction of sensitive information
• Smart Filtering: Respects .gitignore and custom exclusions
• AI-Optimized Output: Structured Markdown perfect for LLM consumption
• Flexible Usage: Both interactive and command-line interfaces

Quick Start Guide

Instant Setup with npx

The fastest way to enhance your LLM workflow:

npx code-context-llm

Global Installation for Regular Use

For teams and frequent users:

npm install -g code-context-llm

Usage Patterns & Best Practices

Interactive Mode: Guided Setup

Perfect for first-time users and exploratory analysis:

npx code-context-llm

The tool guides you through:

• Project directory selection
• Custom exclusion patterns
• Output file configuration

CI/CD Integration: Automated Context Generation

Ideal for DevOps workflows and documentation automation:

# Generate context for specific directories
npx code-context-llm --project-path ./server --output-file ServerContext.md
 
# Multiple directory context generation
npx code-context-llm --project-path ./frontend --output-file FrontendContext.md --skip-dirs node_modules,dist

Monorepo Support

Handle large, multi-project repositories effectively:

# Generate context for a specific package
npx code-context-llm --project-path ./packages/core --skip-dirs test,coverage
 
# Create context for multiple packages
npx code-context-llm --project-path . --skip-dirs "node_modules,**/dist,**/coverage"

Command-Line Options Reference

Customize your context generation:

• -p, --project-path <path>: Target directory (default: current directory)
• -o, --output-file <filename>: Output file name (default: ProjectStructure.md)
• --skip-dirs <dirs>: Directories to exclude
• --skip-files <files>: Files to exclude
• -h, --help: View documentation
• -V, --version: Check installed version

Security & Best Practices

Code Context LLM prioritizes your code's security by automatically redacting sensitive information:

• API keys and tokens
• Passwords and credentials
• Configuration secrets
• Environment variables

The tool replaces known sensitive data with [REDACTED] markers, ensuring safer sharing with AI models. However, we strongly recommend that you manually review the generated output to confirm that no additional sensitive information remains. Some environment-specific or custom secrets may not be detected by the default patterns.

Common Use Cases

1. Enhanced LLM Prompt Engineering

Generate accurate project context to improve AI responses:

npx code-context-llm --output-file context.md
# Use context.md in your LLM prompts for better code suggestions

2. Automated Documentation

Maintain up-to-date project structure documentation:

# Add to your CI pipeline
npx code-context-llm --project-path . --output-file docs/PROJECT_STRUCTURE.md

3. Code Review Assistance

Help reviewers understand project context:

npx code-context-llm --project-path ./changed-module --output-file PR_CONTEXT.md

Frequently Asked Questions

Q: Does it work with monorepos?

Yes! Use path and skip patterns to target specific packages or directories.

Q: How does it handle large directories?

The tool is optimized for performance and includes smart filtering options to handle large codebases efficiently.

Q: Can I customize the redaction patterns?

Yes, future versions will include configurable redaction patterns. Currently, it follows security best practices for common sensitive data patterns.

Next Steps

Ready to enhance your AI development workflow?

1. Try it now: npx code-context-llm
2. Star us on GitHub