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Self-Improving + Proactive Agent

Automate self-improvement by learning from corrections and maintaining memory state.

Updated Apr 14, 2026
Search Tags:#self-improvement#agent#memory#memory-management#heartbeat#corrections
One-click install
npx skills add https://github.com/novolei/if2Ai --skill self-improving-proactive-agent-novolei
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Self-Improving + Proactive Agent
Source: https://github.com/novolei/if2Ai/tree/main/src-tauri/resources/bundled-skills/self-improving
Command: npx skills add https://github.com/novolei/if2Ai --skill self-improving-proactive-agent-novolei

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill automates long-term agent improvement by learning from user corrections and maintaining an internal memory and heartbeat state to sustain context across tasks.

Core Features & Use Cases

  • Tracks corrections and derives reusable lessons to improve future outputs
  • Maintains domain- and project-level memories to organize knowledge and enable scoped learning
  • Proactively manages execution quality by referencing memory before non-trivial work

Quick Start

Load the memory system by following the setup instructions to initialize the memory, corrections, and heartbeat state files.

Frequently Asked Questions about Self-Improving + Proactive Agent

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

FAQPage Schema
How do I make AI agents remember context and learn from corrections across long sessions?▼

A self-improving agent maintains an internal memory and heartbeat state to sustain context across long-running sessions. It automates long-term improvement by tracking corrections and deriving reusable lessons to enhance future outputs.

How does an agent heartbeat state work for proactive execution management?▼

An agent heartbeat state works by persisting execution status in a local heartbeat-state.md file. This enables proactive follow-through and quality management by referencing memory before non-trivial work, ensuring continuous execution across multi-domain projects.

Can I organize project-level memory for multi-domain agent workflows?▼

Yes, you can organize project-level memory for multi-domain workflows using a structured local workspace. The system maintains domain- and project-level memory directories to organize knowledge and enable scoped learning across different projects.

What is the best way to persist agent preferences and workflows safely?▼

The best way to persist agent preferences safely is using a structured local workspace with frontmatter-enabled SKILL.md parsing. This stores patterns and workflows in dedicated memory.md and corrections.md files for reliable retrieval.

Do I need to set up local files before using a self-improving agent memory system?▼

Yes, you need to initialize local files before using a self-improving agent memory system. The setup requires creating memory.md, corrections.md, domains/, projects/, and heartbeat-state.md files to properly persist preferences and execution state.

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