Self-Improving Agent

Captures corrections and reflections into a structured, tiered memory hierarchy for future behavior improvement.

Updated Apr 10, 2026

Search Tags:#automation #self-improvement #learning #agent #memory #corrections #reflections

Ready to run

npx skills add https://github.com/stevengonsalvez/nanoclaw-standalone --skill self-improving-agent-stevengonsalvez

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: Self-Improving Agent
Source: https://github.com/stevengonsalvez/nanoclaw-standalone/tree/main/container/skills/self-improving
Command: npx skills add https://github.com/stevengonsalvez/nanoclaw-standalone --skill self-improving-agent-stevengonsalvez

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Addresses the gap where assistants repeat mistakes and lack memory of corrections, enabling ongoing improvement through structured reflection and local memory.

Core Features & Use Cases

Self-corrects: captures user corrections, performs self-reflection, and learns from outcomes to improve future responses.
Memory-first workflow: stores lessons in a local, auditable hierarchy (memory.md, corrections.md, and domain/project namespaces).
Use Case: after a complex task, the agent reflects on results, extracts actionable lessons, and applies them to subsequent tasks.

Quick Start

Complete a task, then prompt the agent to reflect, capture a lesson, and apply it to future responses.

Frequently Asked Questions about Self-Improving Agent

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

FAQPage Schema

How do I stop my AI agent from repeating the same mistakes across different tasks?▼

To stop an agent from repeating mistakes, you need a self-improvement workflow that captures user corrections and self-reflections into structured memory. This Skill enables autonomous learning by storing these lessons in a local, auditable hierarchy to progressively refine behavior.

What is self-reflection in autonomous agents and how does it improve future responses?▼

Self-reflection in autonomous agents is the process of analyzing outcomes after a task to extract actionable lessons. By capturing these reflections alongside user corrections in structured memory, the agent applies them to subsequent tasks to improve future responses.

How do I set up local memory for an agent to learn from past corrections and outcomes?▼

You set up local memory for agent learning by creating a memory store at ~/self-improving/ with HOT, WARM, and COLD tiers. This hierarchy stores lessons, corrections, and domain namespaces, ensuring transparent and auditable self-improvement across tasks.

Can I apply learned corrections and patterns across different domains and projects?▼

Yes, you can apply learned corrections across different domains using namespace inheritance. The memory-first workflow stores lessons in domain and project namespaces, allowing the agent to safely transfer patterns and refine behavior across multiple tasks.

Do I need any external dependencies or specific frameworks to implement agent self-correction?▼

No external dependencies or frameworks are required to implement agent self-correction. The system relies entirely on a local memory store with HOT, WARM, and COLD tiers, along with follow-up workflows for corrections and patterns to ensure safe learning.

What are the limitations of using local memory for agent self-improvement?▼

The primary limitation of using local memory for agent self-improvement is that memory is restricted to a local store at ~/self-improving/. Learning does not sync across different machines or users, and managing the HOT, WARM, and COLD tiers requires manual follow-up workflows to maintain auditable namespaces.