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self-improvement

Log learnings and errors under .learnings for continuous improvement.

Updated Apr 20, 2026
Search Tags:#self-improvement#memory#promotions#openclaw#hook-integration#error-detection#learning-logs
One-click install
npx skills add https://github.com/aspectrr/denchclaw-workspace --skill self-improvement-aspectrr
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: self-improvement
Source: https://github.com/aspectrr/denchclaw-workspace/tree/main/skills/self-improving-agent
Command: npx skills add https://github.com/aspectrr/denchclaw-workspace --skill self-improvement-aspectrr

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

The self-improvement process captures learnings, errors, and corrections to enable continuous improvement across development tasks and AI interactions.

Core Features & Use Cases

  • Centralized logging of learnings in .learnings with categories such as correction, insight, knowledge_gap, and best_practice.
  • Starter templates and scripts to initialize log files, track errors, and promote recurring insights to project memory.
  • Use cases include after command failures, user corrections, missing capabilities, and outdated knowledge.

Quick Start

Create the .learnings directory at the workspace root and begin logging learnings after prompts or errors, then review and promote valuable learnings as needed.

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 agent corrections and command failures for continuous improvement?▼

To log AI agent corrections and command failures, you capture them as structured entries in a .learnings directory using categories like correction, insight, knowledge_gap, and best_practice to enable continuous improvement across projects.

What is the best way to promote recurring development insights to project memory files?▼

Promoting recurring development insights to project memory involves elevating valuable entries from local .learnings logs into persistent instruction files like CLAUDE.md, AGENTS.md, or copilot-instructions.md so AI agents retain the knowledge.

Can I use this self-improvement workflow to track knowledge gaps after missing capabilities?▼

Yes, you can use this self-improvement workflow to track knowledge gaps by logging missing capabilities as structured entries under the knowledge_gap category within the .learnings directory at your workspace root.

Does this error detection and learning log integration require external dependencies?▼

No, this error detection and learning log integration requires no external dependencies, utilizing only starter templates and internal scripts to initialize log files and track errors within your existing developer workflows.

When should I not use a structured learning log for AI agent interactions?▼

You should avoid using a structured learning log for AI agent interactions when dealing with one-off errors or transient issues that do not provide recurring development insights worth promoting to persistent project memory files.

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