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self-improvement

Log learnings and errors to markdown files under .learnings and promote them to project-wide memory files.

1|Updated Mar 14, 2026
Search Tags:#logging#self-improvement#hooks#learning#memory#promotion#openclaw
One-click install
npx skills add https://github.com/marcoamu/openclaw-workspace --skill self-improvement-marcoamu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: self-improvement
Source: https://github.com/marcoamu/openclaw-workspace/tree/main/skills/self-improving-agent
Command: npx skills add https://github.com/marcoamu/openclaw-workspace --skill self-improvement-marcoamu

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This skill provides a structured approach to capture learnings, corrections, and errors so teams can iteratively improve AI behavior and project processes.

Core Features & Use Cases

  • Log learnings to standardized markdown files under .learnings (LEARNINGS.md, ERRORS.md, FEATURE_REQUESTS.md) to preserve context after failures, corrections, or new capability requests.
  • Promote high-value learnings to project-wide memories by updating CLAUDE.md, AGENTS.md, or .github/copilot-instructions.md for long-term adoption.
  • Integrate with OpenClaw workspace hooks and sessions to prompt for logging and organize improvements.

Quick Start

After completing a task or encountering an error, log a learning entry to .learnings using the provided templates.

Frequently Asked Questions about self-improvement

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

FAQPage Schema
How do I log AI command failures and user corrections for continuous improvement?▼

You can log command failures, user corrections, and knowledge gaps into standardized markdown files like ERRORS.md and LEARNINGS.md under a .learnings directory to preserve context and enable continuous improvement.

What is the best way to capture feature requests and knowledge gaps during AI coding sessions?▼

Capturing feature requests and knowledge gaps involves logging actionable feedback into standardized markdown files like FEATURE_REQUESTS.md, ensuring teams iteratively improve AI behavior and project processes.

How do I promote high-value learnings to project-wide memory in AI coding assistants?▼

You promote high-value learnings by updating project-wide memory files such as CLAUDE.md, AGENTS.md, and .github/copilot-instructions.md to ensure long-term adoption of corrections and processes.

Can I use workspace hooks to automatically prompt for logging errors and learnings?▼

Yes, you can integrate with OpenClaw workspace hooks and sessions to automatically prompt for logging improvements and organize learnings after encountering errors or completing tasks.

Does this continuous improvement logging approach work without external dependencies?▼

Yes, this self-improvement logging approach operates without external dependencies, relying solely on standardized markdown files within your project directory to store and manage actionable feedback.

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