env2llm

A fast, lightweight bash script that profiles your Python development environment and outputs human-readable text optimized for sharing with LLMs (Large Language Models).

Overview

env2llm helps developers quickly share their development environment context with LLMs for troubleshooting, setup assistance, and proof-of-concept development. It gathers relevant information about your system, Python environment, Docker, Kubernetes, project structure, and network configuration while keeping sensitive information secure.

Features

• Fast execution - Completes in under 5 seconds
• Graceful degradation - Works even when tools are missing
• Security-first - Never exposes sensitive information (passwords, tokens, keys)
• Cross-platform - Works on macOS and Linux
• LLM-optimized - Output format designed for AI context sharing
• Lightweight - Single bash script with no dependencies

Installation

Option 1: Direct Download

curl -o env2llm https://raw.githubusercontent.com/chrisammon3000/env2llm/main/env2llm
chmod +x env2llm

Option 2: Clone Repository

git clone https://github.com/chrisammon3000/env2llm.git
cd env2llm
chmod +x env2llm

Option 3: Add to PATH

# After downloading, copy to a directory in your PATH
sudo cp env2llm /usr/local/bin/

Usage

Basic Usage

# Output to terminal
./env2llm

# Save to file
./env2llm > my-environment.txt

# If installed in PATH
env2llm > profile.txt

Help

./env2llm --help

Sample Output

=== env2llm Profile ===
Generated: Tue Jul 8 15:46:02 MDT 2025
Purpose: Python/Docker/Kubernetes PoC Development

[SYSTEM]
- OS: macOS 14.1
- Memory: 16 GB
- Disk Available: 58Gi
- Architecture: arm64

[SHELL]
- Shell: /bin/zsh
- Working Dir: /Users/dev/myproject
- Virtual Env: myproject-env
- Framework: Oh My Zsh

[PYTHON]
- Python: Python 3.11.5
- pip: 23.2.1
- Installed packages: 42
- Project files: requirements.txt pyproject.toml

[DOCKER]
- Docker: 24.0.6
- Daemon: Running
- Running containers: 3
- Container names: web-app, redis, postgres
- Docker Compose: 2.21.0
- Docker files: Dockerfile docker-compose.yml

[KUBERNETES]
- kubectl: v1.28.2
- Current context: minikube
- Current namespace: default
- Cluster access: Connected
- Local K8s: minikube
- K8s manifests: 5 found

[PROJECT]
- Git branch: main
- Git status: Clean
- Git remote: https://github.com/user/project.git
- Top-level directories: 8
- Key directories: src tests docs
- Entry points: app.py
- Config files: .env config.py

[NETWORK]
Port 3000: Available
Port 5000: In use
Port 8000: Available
Port 8080: In use
- Internet: Connected (github.com reachable)
- Proxy: Not configured

=== End Profile ===

What's Included

System Information

• Operating system and version
• Available memory and disk space
• CPU architecture

Shell Environment

• Current shell and working directory
• Virtual environment status
• Key environment variables (PATH, PYTHONPATH)
• Shell framework detection (Oh My Zsh, etc.)

Python Environment

• Python and pip versions
• Package count
• Project files (requirements.txt, pyproject.toml, etc.)

Docker Environment

• Docker and Docker Compose versions
• Daemon status and running containers
• Docker-related files in current directory

Kubernetes Environment

• kubectl version and cluster connectivity
• Current context and namespace
• Local Kubernetes type detection
• Manifest file detection

Project Context

• Git branch and status
• Project structure overview
• Entry point detection
• Configuration files

Network Information

• Common development port availability
• Internet connectivity
• Proxy configuration

Security

env2llm is designed with security in mind:

• No sensitive data exposure - Passwords, tokens, and keys are never included
• Sanitized URLs - Git remotes are cleaned of embedded credentials
• No file contents - Only checks for file existence, never reads contents
• Limited path disclosure - Avoids exposing security-sensitive locations

Requirements

• Bash 4.0+ (available on all modern systems)
• Python 3.x development environment
• Docker (optional)
• Kubernetes (optional)

Use Cases

For Developers

• Share environment context with team members
• Document development setup for onboarding
• Troubleshoot environment issues with AI assistance
• Quick environment audits

For LLM Interactions

• Provide comprehensive context for coding assistance
• Enable environment-specific recommendations
• Support for containerized development workflows
• Help with deployment and configuration issues

Contributing

This is a one-shot project made with Claude Code, contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Version

Current version: 1.0.0