---
title: Markdown → Python Migration Plan
source: docs/research/markdown-to-python-migration-plan.md
---

# Markdown to Python Migration Plan

## Title: Migrating from Markdown to Python for Improved Efficiency and Quality

### Description:
A comprehensive plan detailing the benefits, risks, and steps involved in migrating from Markdown to Python for improved efficiency, quality, and enforceability of project documentation.

### Tags:
markdown, python, migration, efficiency, quality, enforceability

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## Introduction

This document outlines a plan to migrate from using Markdown for project documentation to Python, with the goal of improving efficiency, reducing redundancy, and enforcing compliance. The proposed migration will be incremental, starting with a quick win and gradually expanding to cover all relevant areas.

### Problems with Current Approach (Markdown)
- No enforcement: Markdown is used primarily for documentation, making it difficult to ensure compliance with established rules.
- Can't verify compliance: There is no way to automatically check if the documented rules are being followed.
- Can't test effectiveness: It is impossible to write tests to validate the correctness of the documented rules.
- Prone to drift: Over time, documented rules may become outdated or inconsistent with actual practices.

### Benefits of Proposed Approach (Python)
- Enforced at runtime: Rules can be enforced as part of the project's execution, ensuring compliance.
- 100% testable: All rules can be tested using Python's testing framework, such as pytest.
- Type-safe with hints: Python's type hints provide a way to ensure that variables are used correctly and consistently.
- Single source of truth: By moving away from Markdown for documentation, there will be only one source of truth for project rules.

## Migration Strategy

### Per Mode Migration
1. Read the existing Markdown mode.
2. Extract the rules and behaviors.
3. Design a Python class structure to represent the mode.
4. Implement the class with type hints.
5. Write tests (>80% coverage) for the implemented class.
6. Benchmark token usage before and after migration.
7. Update the command to use the new Python implementation.
8. Keep the Markdown as fallback documentation.

### Testing Strategy
1. Verify that the tool selection matrix is correct.
2. Verify that parallel execution auto-triggers correctly.
3. Verify resource management enforcement.

## Expected Outcomes

### Token Efficiency

**Before Migration**:
- Per Session: ~66,000 tokens/session
- Annual (200 sessions): 13,200,000 tokens
- Cost: ~$26-50/year

**After Python Migration**:
- Per Session: ~4,500 tokens/session
- Annual (200 sessions): 900,000 tokens
- Cost: ~$2-4/year
- Savings: 93% tokens, 90%+ cost

**After Skills Migration**:
- Per Session: ~3,500 tokens/session (unused modes)
- Savings: 95%+ tokens

### Quality Improvements
- Enforced at runtime
- 100% testable
- Type-safe with hints
- Single source of truth

## Risks and Mitigation

**Risk 1**: Breaking existing workflows
- **Mitigation**: Keep Markdown as fallback documentation.

**Risk 2**: Skills API immaturity
- **Mitigation**: Python-first works now, Skills later.

**Risk 3**: Implementation complexity
- **Mitigation**: Incremental migration (1 mode at a time).

## Conclusion

The proposed plan aims to improve the efficiency and quality of project documentation by migrating from Markdown to Python. The migration will be incremental, starting with a quick win and gradually expanding to cover all relevant areas.

**Start Date**: 2025-10-20
**Target Completion**: 2026-01-20 (3 months for full migration)
**Quick Win**: Orchestration mode (1 week).

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Citation: [source: docs/research/markdown-to-python-migration-plan.md]