self-improving-agent

Universal self-improving agent that learns from all skill experiences using multi-memory architecture.

- Implements semantic, episodic, and working memory to extract patterns, abstract insights, and continuously evolve skill guidance across the codebase

- Auto-triggers on skill completion, errors, and session events via hooks-based integration; detects and corrects inaccurate guidance with traceable evolution markers

- Prioritizes updates across 10+ skill categories (PRD planning, architecture, API design, debugging, code review, performance, security, testing, deployment) based on pattern confidence and application frequency

- Consolidates feedback loops with user ratings, root cause analysis, and validation workflows to prevent over-generalization and maintain guidance accuracy

Self-Improving Agent

"An AI agent that learns from every interaction, accumulating patterns and insights to continuously improve its own capabilities." - Based on 2025 lifelong learning research

Overview

This is a universal self-improvement system that learns from ALL skill experiences, not just PRDs. It implements a complete feedback loop with:

- Multi-Memory Architecture: Semantic + Episodic + Working memory

- Self-Correction: Detects and fixes skill guidance errors

- Self-Validation: Periodically verifies skill accuracy

- Hooks Integration: Auto-triggers on skill events (before_start, after_complete, on_error)

- Evolution Markers: Traceable changes with source attribution

Research-Based Design

Based on 2025 research: