aif-evolve

Analyze project patches and codebase patterns to generate skill-context rules.

Updated May 16, 2026
One-click install
npx skills add https://github.com/vulikjulik/DeepLom --skill aif-evolve-vulikjulik
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: aif-evolve
Source: https://github.com/vulikjulik/DeepLom/tree/main/.opencode/skills/aif-evolve
Command: npx skills add https://github.com/vulikjulik/DeepLom --skill aif-evolve-vulikjulik

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Generic out-of-the-box AI Factory skills do not account for project-specific patterns, past mistakes, and coding conventions, leading to repeated errors and suboptimal AI performance when working on your codebase.

Core Features & Use Cases

  • Patch Analysis: Extracts actionable prevention rules from past project patches (bug fixes, mistakes) to stop recurring issues from happening again.
  • Convention Alignment: Scans your project's linter configs, error handling patterns, and file structure to align skill instructions with your team's actual standards.
  • Skill-Context Updates: Automatically writes project-specific rules to the correct skill-context files, making your AI smarter with every run without modifying base skill files.
  • Use Case: If your team frequently introduces null reference errors when accessing optional database relations, this skill will add a guard rule to the relevant AI Factory skill to check for nullable fields before access, eliminating the same bug from future work.

Quick Start

Invoke the aif-evolve skill with your target skill name or the argument "all" to analyze your project's patches and codebase conventions, then automatically update your installed AI Factory skills with project-specific rules to reduce recurring mistakes.

Frequently Asked Questions about aif-evolve

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

FAQPage Schema
How do I stop AI from repeating the same coding mistakes on my project?▼

To stop AI from repeating coding mistakes, you can analyze project patches to extract actionable prevention rules. This process updates AI skill-context files with guard rules, ensuring past bug fixes inform future AI-generated code without modifying base files.

What is self-improving AI and how does it align with codebase conventions?▼

Self-improving AI aligns with codebase conventions by scanning linter configs, error handling patterns, and file structures. It generates project-specific rules that make AI smarter incrementally, aligning outputs with your team's actual standards.

How do I update AI skills with project-specific rules from patch files?▼

You update AI skills by invoking an evolution process that analyzes patch files and extracts prevention points. It writes these project-specific rules directly into skill-context files, adding traceable guard rules based on evidence from your patches.

Can I evolve all installed AI Factory skills at once using codebase analysis?▼

Yes, you can evolve all installed AI Factory skills at once by passing an argument to analyze your codebase patterns and patches. This performs a full evolution, updating all compatible skills with project-specific prevention rules simultaneously.

Does evolving AI skills modify the original base skill files?▼

No, evolving AI skills does not modify the original base skill files. It automatically writes project-specific rules to separate skill-context files, ensuring all improvements are layered on top without altering the foundational skill instructions.

What's the best way to prevent recurring null reference errors in AI generated code?▼

The best way to prevent recurring null reference errors is to analyze past bug fix patches. This extracts a guard rule that checks for nullable fields before access, adding it to the relevant AI skill to eliminate future occurrences.