self-improvement

Name: self-improvement
Availability: InStock
Author: velamints

Log learnings, errors, and corrections to markdown files under .learnings.

Updated Mar 4, 2026

Search Tags:#logging #self-improvement #hooks #memory #prompts #learnings #openclaw

Ready to run

npx skills add https://github.com/velamints2/clawbot-lab --skill self-improvement-velamints2

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: self-improvement
Source: https://github.com/velamints2/clawbot-lab/tree/main/skills/self-improving-agent
Command: npx skills add https://github.com/velamints2/clawbot-lab --skill self-improvement-velamints2

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Captures learnings, errors, and corrections to enable continuous improvement across AI prompts and executions, providing a structured logs trail that helps teams improve responses and workflows over time.

Core Features & Use Cases

Centralized logging of learnings to markdown files under the .learnings directory (LEARNINGS.md, ERRORS.md, FEATURE_REQUESTS.md)
Promotion-ready entries to workspace memory or project-wide files such as CLAUDE.md, AGENTS.md, and/or .github/copilot-instructions.md
OpenClaw integration and hook-driven reminders to surface learnings and drive cross-session reuse

Quick Start

Log a recent correction or failure using the self-improvement skill format.

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 prompt errors and corrections for continuous improvement?▼

You can log AI prompt errors and corrections by capturing them into structured markdown files under a .learnings directory. This creates a persistent trail of learnings, errors, and feature requests to help improve responses over time.

What is the best way to track failed commands and missing features in AI workflows?▼

Tracking failed commands and missing features involves logging them into centralized markdown files like ERRORS.md and FEATURE_REQUESTS.md. This approach ensures that operational failures and knowledge gaps are recorded for cross-session reuse.

Can I promote logged learnings to CLAUDE.md or copilot-instructions.md?▼

Yes, logged learnings can be promoted directly to workspace memory files such as CLAUDE.md, AGENTS.md, and .github/copilot-instructions.md. This integrates continuous improvement entries into your project-wide AI instructions.

How does logging learnings work with OpenClaw for cross-session reuse?▼

Logging learnings with OpenClaw uses hook-driven reminders to surface past errors and corrections across different sessions. This workflow ensures that previously captured knowledge is automatically applied to new AI executions.

When should I trigger a learning log entry for an AI operation?▼

You should trigger a learning log entry when a command fails, a user provides a correction, a requested feature is missing, an external tool fails, or when existing knowledge is outdated. This captures actionable context for future sessions.