Varietyz/Disciplined-AI-Software-Development

This methodology provides a structured approach for collaborating with AI systems on software development projects. It addresses common issues like code bloat, architectural drift, and context dilution through systematic constraints and validation checkpoints.

45
/ 100
Emerging

Implements a four-stage collaborative framework using behavioral persona plugins (JSON-based) and systematic constraints—150-line file limits, dependency-gated phases, and validation checkpoints—to maintain architectural consistency across extended AI sessions. Provides dual instruction formats: prose-based Markdown/XML for web interfaces and PAG (Pattern Abstract Grammar) for CLI agents, with benchmarking integrated early to drive optimization decisions from measured performance data rather than assumptions. Demonstrated across production systems like Discord bots, language runtimes, and CI/CD tools, all maintaining modular boundaries and measurable completion criteria.

394 stars.

No Package No Dependents
Maintenance 6 / 25
Adoption 10 / 25
Maturity 15 / 25
Community 14 / 25

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Stars

394

Forks

30

Language

Python

License

CC-BY-SA-4.0

Last pushed

Dec 12, 2025

Commits (30d)

0

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