self-improving-agent

Log errors, learnings, and feature requests into markdown files for iterative refinement.

Updated Aug 23, 2026

Search Tags:#error handling #learning #feedback loop #continuous learning #self-correction #agent improvement

Ready to run

npx skills add https://github.com/Niraven/pokee-deep-research-skill --skill self-improving-agent-niraven

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: self-improving-agent
Source: https://github.com/Niraven/pokee-deep-research-skill/tree/main/skills/self-improving-agent
Command: npx skills add https://github.com/Niraven/pokee-deep-research-skill --skill self-improving-agent-niraven

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the challenge of AI agents repeating errors or failing to adapt to new information by creating a structured feedback loop for continuous improvement.

Core Features & Use Cases

Error Logging: Records unexpected command failures and exceptions.
Learning Capture: Stores user corrections, new discoveries, and better approaches.
Feature Request Tracking: Logs capabilities users desire but the agent lacks.
Use Case: If the agent fails to execute a Git command due to incorrect authentication, it logs the error and the fix. If a user corrects the agent's understanding of a command, that correction is saved for future reference, preventing the same mistake.

Quick Start

Log a user correction by stating 'User correction: [your correction details]'.

Frequently Asked Questions about self-improving-agent

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

FAQPage Schema

How do I make an AI agent learn from mistakes and stop repeating errors?▼

You can capture user corrections by explicitly stating 'User correction: [details]', which triggers the agent to save the learning into a dedicated log file for future reference and continuous improvement.

What is a continuous learning feedback loop for autonomous agents?▼

The feedback loop works by recording errors in an ERRORS.md file, saving user discoveries in LEARNINGS.md, and logging desired capabilities in FEATURE_REQUESTS.md, consolidating high-value entries into a persistent MEMORY.md.

How do I track feature requests and user corrections for an AI agent?▼

Feature requests are logged into FEATURE_REQUESTS.md, while user corrections and new discoveries are stored in LEARNINGS.md, creating an accessible record for the agent to review and adapt its future actions.

Does this self-correction mechanism require external dependencies or libraries?▼

It operates without dependencies, using standard markdown files like ERRORS.md and LEARNINGS.md to record agent failures and user feedback, ensuring broad compatibility across different environments.

When should an agent consolidate learnings into a persistent memory file?▼

Consolidation into MEMORY.md happens periodically to preserve high-value learnings, preventing the temporary feedback logs from growing unmanageable while maintaining the agent's improved state across sessions.