self-improving-agent

Overview

Self-Improving Agent is a universal skill that implements a continuous learning system across all skill interactions using a multi-memory architecture. It combines semantic memory (accumulated patterns and knowledge), episodic memory (specific interaction experiences), and working memory (current session context) to evolve the codebase over time, with hooks-based triggers for automatic self-correction on skill completion and error events.

Key Features

• Multi-Memory Architecture - Implements semantic memory for patterns and knowledge, episodic memory for specific interaction experiences, and working memory for current session context, based on 2025 lifelong learning research
• Hooks-Based Triggers - Auto-triggers on skill lifecycle events including before_start (session logging), after_complete (optional PR creation), and on_error (error capture) for continuous improvement
• Self-Correction System - Detects guidance errors and fixes skill instructions when other skills (debugger, code-reviewer) complete their work, avoiding infinite recursion on errors
• Evolution Markers - Tracks all changes with source attribution for traceability, making it clear which improvements came from which learning experiences
• Self-Validation - Periodically verifies skill accuracy to ensure accumulated improvements remain correct and relevant

Who is this for?

This skill is for developers and teams running AI agent systems who want their agents to learn and improve from every interaction. It is particularly valuable for teams maintaining large skill libraries where continuous refinement based on real usage patterns can significantly improve agent reliability and output quality over time.