ln2md CLI

Turn your resume into an AI-readable knowledge base for coding agents.

ln2md generates structured developer skill profiles from resumes and professional profiles so AI coding agents can reason over your experience consistently. Compatible with agent workflows used by Claude, Codex, and Gemini.

🎬 Demo

ln2md pdf resume.pdf

ln2md/
skills/
graph.json

The CLI analyzes your resume and generates a deterministic, structured, AI-readable profile for downstream coding-agent use.

💡 Why This Matters

• Modern AI coding agents need structured developer context to make accurate decisions.
• Traditional resumes are unstructured text, which is hard for AI systems to interpret reliably.
• ln2md converts developer experience into deterministic, structured knowledge that agents can use directly.
• Structured output improves automation, retrieval, and consistency across agent workflows.

🧩 Example Generated Skill File

---
id: "python"
name: "Python"
type: "skill"
category: "technical"
level: "intermediate"
depends_on: []
tools: []
domains: []
---

## Skill Summary
- Strong Python development experience.

## Core Competencies
- N/A

## Tools and Technologies
- N/A

## Practical Experience
- Built backend automation workflows in Python.

## Automation Opportunities
- N/A

## Related Skills
- N/A

📦 Installation

pip install ln2md

For local development:

pip install -e .

⚡ Quick Start

# 1) Generate AI profile artifacts from PDF
ln2md pdf resume.pdf

# 2) Validate structure and agent compatibility
ln2md validate

Optional environment setup for Gemini-powered modules:

GEMINI_API_KEY="your-key-here"
GEMINI_MODEL="gemini-3.1-flash-lite-preview"

🗂️ Folder Structure Output

ln2md/
  profile.json
  graph.json
  skills/
    <skill>.md
  tools/
    <tool>.md
  domains/
    <domain>.md

🛣️ Roadmap

• Higher-precision LinkedIn PDF parsing
• Better profile name/headline extraction heuristics
• Multi-language resume support
• CI workflow for schema regression tests
• Plugin hooks for custom extraction pipelines

🤝 Contributing

Contributions are welcome.

1. Fork the repo.
2. Create a feature branch.
3. Add tests and keep output deterministic.
4. Run validation locally.
5. Open a PR with a clear change summary.

If you are adding fields, update the compatibility schema and validator in the same PR.

📄 License

MIT License