aif-evolve

Analyze patches and codebase patterns to iteratively refine AI Factory skills.

83|5|Updated Oct 17, 2025
One-click install
npx skills add https://github.com/ArtemYurov/TomoBar --skill aif-evolve-artemyurov
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: aif-evolve
Source: https://github.com/ArtemYurov/TomoBar/tree/main/.claude/skills/aif-evolve
Command: npx skills add https://github.com/ArtemYurov/TomoBar --skill aif-evolve-artemyurov

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analyzes project context, patches, and codebase patterns to progressively enhance AI Factory skills, reducing recurring mistakes and automating smarter responses over time.

Core Features & Use Cases

  • Patch-driven improvement: learns from past patches to strengthen prevention rules and guardrails.
  • Context-aware evolution: uses current project description and conventions to tailor skill updates.
  • Incremental learning loop: records what worked and what didn't to refine future cycles.

Quick Start

Run the /aif-evolve command to begin analyzing patches and evolving skills for your project.

Frequently Asked Questions about aif-evolve

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

FAQPage Schema
How do I improve AI coding skills from project patches and codebase patterns?

You can improve AI coding skills from project patches by analyzing codebase patterns and conventions to iteratively refine guardrails and context-specific rules. This enables continual skill refinement across multiple development iterations.

How does patch-driven analysis prevent recurring AI coding mistakes?

Patch-driven analysis prevents recurring AI coding mistakes by learning from past patches to strengthen prevention rules and guardrails. It records what worked and what failed to refine future automated responses.

Can I automatically update AI rules based on evolving project conventions?

Yes, you can automatically update AI rules based on evolving project conventions. The tool uses context-aware evolution to tailor skill updates according to the current project description and coding standards.

What is the best way to extract codebase patterns for AI skill self-improvement?

The best way to extract codebase patterns for AI skill self-improvement is running an incremental learning loop that analyzes patches and project standards. This generates traceable improvements linked directly to your codebase conventions.

Do I need existing AI Factory skills to use context-aware codebase evolution?

Yes, context-aware codebase evolution requires existing AI Factory skills to function. It enhances these skills by incorporating learned guardrails and context-specific rules extracted from your project's ongoing patches and conventions.

Why does my AI coding assistant keep repeating mistakes despite project context?

Your AI coding assistant repeats mistakes when it lacks updated guardrails extracted from patch analysis. Iterative skill refinement solves this by recording past failures and applying traceable prevention rules to future cycles.