data-driven-generation

Generate code by selecting strategies from git-derived stability and risk signals.

11|3|Updated Jan 29, 2026

Search Tags:#git #refactoring #stability #rag #mcp #codegen

Ready to run

npx skills add https://github.com/artk0de/TeaRAGs-MCP --skill data-driven-generation

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-driven-generation
Source: https://github.com/artk0de/TeaRAGs-MCP/tree/main/.claude-plugin/tea-rags/skills/data-driven-generation
Command: npx skills add https://github.com/artk0de/TeaRAGs-MCP --skill data-driven-generation

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill solves the issue of AI agents generating code based on generic patterns rather than the specific, battle-tested history of your codebase, preventing the propagation of technical debt and unstable implementations.

Core Features & Use Cases

Git-Signal Integration: Uses authorship, churn, and bug-fix rates to select the most stable code templates.
Adaptive Strategies: Automatically switches between Defensive, Stabilization, Conservative, and Standard generation modes based on file health.
Use Case: When implementing a new feature in a legacy module, the agent identifies the most stable nearby code and adopts its patterns, ensuring the new code is consistent with the project's proven architectural standards.

Quick Start

Invoke the data-driven-generation skill to implement the new user authentication method by analyzing the git history of the surrounding files.

Frequently Asked Questions about data-driven-generation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I generate code that matches my existing codebase patterns instead of generic templates?▼

Git-based code generation selects strategies by analyzing git-derived stability and risk signals, such as authorship, churn, and bug-fix rates, to ensure new code matches your project's proven architectural standards.

How does git history improve code generation stability for legacy modules?▼

Git history analysis identifies the most stable nearby code by evaluating bug-fix rates and churn, allowing the agent to adopt those battle-tested patterns when implementing new features in legacy modules to prevent technical debt.

When should I use defensive or conservative code generation strategies?▼

Use defensive or conservative code generation strategies when file health metrics indicate high risk or instability, as the system automatically switches between Defensive, Stabilization, Conservative, and Standard modes based on these git-derived signals.

Do I need TeaRAGs overlay labels to use data-driven code generation?▼

Yes, integration with TeaRAGs overlay labels and project-specific strategy templates is required to ensure architectural alignment when automating context-aware code generation based on git stability signals.

What's the best way to automate bug fixes using git risk signals?▼

Automate bug fixes by invoking git-driven code generation to analyze the bug-fix rates and churn of surrounding files, applying the most stable templates to ensure the fix aligns with proven codebase patterns.