Analyze project context and patches to generate rules for AI skills.
npx skills add https://github.com/drsapaev/final --skill aif-evolve-drsapaev
Please help me install this Agent Skill. Skill: aif-evolve Source: https://github.com/drsapaev/final/tree/main/.codex/skills/aif-evolve Command: npx skills add https://github.com/drsapaev/final --skill aif-evolve-drsapaev
This Skill automatically improves the AI's ability to assist with your specific project by learning from past mistakes (patches), understanding your project's unique technologies, and adhering to its conventions.
.ai-factory/DESCRIPTION.md, patches in .ai-factory/patches/, and codebase conventions.Use the aif-evolve skill to improve all AI skills based on the project's current context and past fixes.
High-intent search queries and answers about installing and using this skill.
AI coding skills improve from past patches by analyzing project context and accumulated fixes to identify recurring problems, then generating project-specific rules to enrich existing skill definitions and prevent similar mistakes.
You update existing AI skills by analyzing your project's description file and patch history to extract tech-specific patterns and missing guards, which are then applied as targeted improvements to the current skill definitions.
Contextual self-improvement for AI codebase analysis is the process of reading project descriptions and patch files to learn recurring pitfalls, allowing the system to generate tailored rules that enhance future coding assistance.
Yes, you can use codebase patches to prevent recurring AI coding errors by evaluating accumulated patch files to detect repeated mistakes, which automatically generates project-specific guards to enrich the relevant skills.
You should run a skill evolution process on your codebase after several coding fixes when you notice recurring errors, ensuring the AI learns from past patches and updates its skills to handle project-specific conventions effectively.
AI skill evolution operates automatically without manual rule configuration by directly reading the project description and patch directory to identify missing guards and suggest targeted improvements based on existing codebase patterns.
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