motock/fagan

Overview

An MCP-native autonomous SDLC pipeline that uses frontier models for planning and review while local models handle implementation. Designed to keep token costs low, with a target budget around $20 per month. Written in Python.

Best for

Best for
Developers who want an inexpensive autonomous SDLC pipeline

Use cases

• Automate implementation tasks with local models
• Use frontier models for planning and code review
• Run a low-budget autonomous development pipeline

How to use

Tools exposed

• product-analyst
• solution-architect
• software-engineer
• security-engineer
• devops-release-engineer
• code-reviewer
• tech-writer

Tested with

Claude Code, Continue

Notes

An MCP-native autonomous SDLC pipeline that uses frontier models for planning and review while local models handle implementation. Designed to keep token costs low, with a target budget around $20 per month. Written in Python.

2 stars on GitHub. Last updated 2026-09-23. Licensed Apache-2.0.

Use cases

• Automate implementation tasks with local models
• Use frontier models for planning and code review
• Run a low-budget autonomous development pipeline

Pros

• Frontier models are reserved for judgment-heavy tasks
• Local models keep ongoing token spend low
• MCP-native architecture supports model context protocol

Cons

• Early-stage project with only 2 GitHub stars
• Requires coordinating both remote and local model workflows
• Autonomy level and reliability not yet widely tested

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.