self-improvement

Name: self-improvement
Availability: InStock
Author: merkyorlynn

Write structured learning, error, and feature-request entries into local logs.

37|4|Updated Mar 31, 2026

Search Tags:#agent workflows #error capture #tool failures #knowledge promotion #learning logging #self improvement #multi agent

Ready to run

npx skills add https://github.com/MerkyorLynn/Lynn --skill self-improvement-merkyorlynn

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: self-improvement
Source: https://github.com/MerkyorLynn/Lynn/tree/main/skills2set/self-improving-agent
Command: npx skills add https://github.com/MerkyorLynn/Lynn --skill self-improvement-merkyorlynn

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Capturing what went wrong and what improved prevents repeated errors and helps an agent steadily get better over time.

Core Features & Use Cases

Learning capture from failures: Log unexpected command/tool errors so the next run can avoid the same dead-end.
User correction ingestion: Record when the user says your output is wrong, and preserve what was corrected.
Feature request tracking: Capture missing capabilities requested by the user for later implementation.
Promotion to higher-context memory: Promote broadly applicable learnings into workspace/project guidance for agents and tools.
Hook-based automation: Use included shell scripts to remind and detect errors during agent workflows (e.g., Claude Code / Codex / OpenClaw).

Quick Start

After a task fails or the user corrects you, instruct your agent to log the learning using the self-improvement skill format into .learnings so it can be reviewed and promoted later.

Frequently Asked Questions about self-improvement

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

FAQPage Schema

How do I capture agent workflow errors and user corrections for continuous learning?▼

To capture agent workflow errors and user corrections for continuous learning, the skill appends structured entries into local learning logs using consistent schemas, preserving failed operations and corrected outputs for future review.

What is the best way to log unexpected tool failures during multi-agent operations?▼

The best way to log unexpected tool failures during multi-agent operations is to use hook-based shell scripts that detect errors and append structured learning entries to the local .learnings directory.

Can I promote validated learnings into project workspace guidance for agents?▼

Yes, you can promote validated learnings into project workspace guidance by elevating broadly applicable items from local learning logs into structured guidance documents for agents and tools.

How do I track feature requests and outdated knowledge discovered during agent-assisted work?▼

You track feature requests and outdated knowledge discovered during agent-assisted work by instructing the agent to log these items using the skill's feature-request and error schemas into local learning logs.

Do I need specific dependencies to use hook-based error detection for learning capture?▼

No specific dependencies are required; the skill operates independently using included shell scripts to detect errors during agent workflows.