aif-evolve

Analyze patches and project context to improve AI Factory skills.

Updated Mar 4, 2026
One-click install
npx skills add https://github.com/mind-systems/mind_context --skill aif-evolve-mind-systems
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: aif-evolve
Source: https://github.com/mind-systems/mind_context/tree/main/.claude/skills/aif-evolve
Command: npx skills add https://github.com/mind-systems/mind_context --skill aif-evolve-mind-systems

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analyzes patches, project context, and codebase patterns to proactively improve AI Factory skills, reducing recurring mistakes and tightening guards across the skill set.

Core Features & Use Cases

  • Patch-driven evolution: reads .ai-factory/patches to extract problems, root causes, prevention rules, and tags.
  • Gap analysis: compares patch findings with existing SKILL.md guidance to identify missing guards, plan updates, and implementation checks.
  • Context-driven evolution: uses project DESCRIPTION and conventions to tailor skill improvements to the current tech stack and coding standards.
  • Use Case: When a set of patches reveals a recurring null-check issue in data handling, evolve /aif-evolve to add a null-guard recommendation to the /aif-fix skill.

Quick Start

Run the evolution workflow to start analyzing patches and project context, then apply approved improvements to installed skills.

Frequently Asked Questions about aif-evolve

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

FAQPage Schema
How do I automatically improve AI skills from past patches and codebase patterns?

To improve AI skills from patches, you analyze patch files and project context to extract recurring problems, root causes, and prevention rules, generating minimal, traceable updates to existing skill guidance.

What is patch-driven skill evolution and how does it work?

Patch-driven skill evolution works by reading patch files to identify recurring mistakes, comparing findings against existing skill guidance for gaps, and generating guardrails to prevent similar issues across the project.

How do I update AI guardrails based on recurring codebase mistakes?

You update AI guardrails by performing gap analysis on patch findings against existing skill files, then applying approved improvements that add null-checks and prevention rules tailored to your tech stack.

Can I target specific AI skills for improvement instead of the entire project?

Yes, you can operate on targeted skills or a project-wide basis, applying context-driven constraints from project descriptions and conventions to tailor improvements while preserving the original skill structure.

Does context-driven skill improvement preserve existing skill structure?

Yes, context-driven improvement preserves existing skill structure by generating minimal, traceable updates that satisfy patch-driven, tech-stack, and convention constraints without rewriting the original file.

When should I use automated skill improvement for my AI workflow?

Use automated skill improvement when a set of patches reveals recurring issues like null-check errors in data handling, requiring proactive guardrails to tighten rules across your installed skill set.