Self-Improving + Proactive Agent

Track corrections and update local memory files across namespaces.

Updated Apr 7, 2026

Search Tags:#self-improvement #memory #workspace #heartbeat #corrections #local-storage #proactivity

Ready to run

npx skills add https://github.com/zhangyanbo2007/openclaw --skill self-improving-proactive-agent-zhangyanbo2007

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: Self-Improving + Proactive Agent
Source: https://github.com/zhangyanbo2007/openclaw/tree/main/workspace-fox-avatar/skills/self-improving
Command: npx skills add https://github.com/zhangyanbo2007/openclaw --skill self-improving-proactive-agent-zhangyanbo2007

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Self-Improving + Proactive Agent addresses the need for continuous learning, self-correction, and memory consolidation in AI systems by providing a localized, evolving knowledge base and a heartbeat-driven workflow that reduces repeated mistakes.

Core Features & Use Cases

Self-reflection, self-criticism, and self-learning to evaluate outputs after errors or user feedback.
Local memory lifecycle with a structured directory (memory.md, corrections.md, projects/, domains/) to capture patterns and preferences for reuse across tasks.
Proactive guidance and structured setup to apply learned patterns automatically to new tasks within defined workspace boundaries.

Quick Start

Set up the self-improving workspace by initializing memory templates and correction logs, then begin capturing explicit corrections to seed long-term improvements.

Frequently Asked Questions about Self-Improving + Proactive Agent

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

FAQPage Schema

How does local memory consolidation work for self-correcting agents?▼

You can automate AI agent self-improvement by tracking corrections and learning patterns locally. This Skill captures user feedback into structured memory files like corrections.md, applying learned patterns proactively across global, domain, and project namespaces.

What is the best way to structure local memory for an evolving AI knowledge base?▼

Yes, you can apply learned corrections across multiple project namespaces. The Skill organizes memory into global, domain, and project directories, ensuring that captured preferences and lessons are accessible and reused across diverse tasks within those defined workspace boundaries.

When should I use a heartbeat-driven workflow for agent self-improvement?▼

Agent memory consolidation does not require network access. The Skill keeps all data locally under the ~/self-improving directory, ensuring that memory.md, corrections.md, and heartbeat-state.md files are updated offline only when corrections are provided.

What are the limitations of using local storage for agent memory?▼

To set up a self-improving workspace, initialize local memory templates and correction logs under the ~/self-improving directory. Begin capturing explicit corrections during tasks to seed long-term improvements and update the structured memory lifecycle automatically.