claudeception

Extract reusable knowledge from work sessions into new skills.

1|Updated Feb 27, 2026

Search Tags:#debugging #skills #learning #agent #continuous improvement #knowledge extraction

Ready to run

npx skills add https://github.com/gatieme/agent-toolkit --skill claudeception-gatieme

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: claudeception
Source: https://github.com/gatieme/agent-toolkit/tree/main/skills/claudeception
Command: npx skills add https://github.com/gatieme/agent-toolkit --skill claudeception-gatieme

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the issue of AI agents starting each session from scratch, leading to repeated debugging and knowledge loss by enabling continuous learning and skill extraction.

Core Features & Use Cases

Automatic Skill Extraction: Identifies and saves non-obvious solutions, workarounds, and project-specific patterns discovered during a session as new, reusable skills.
Retrospective Analysis: Allows for manual triggering of a learning retrospective to explicitly save knowledge.
Use Case: After successfully debugging a complex, project-specific error, Claudeception can extract the solution into a new skill, ensuring that the same problem is resolved much faster in the future.

Quick Start

Use the claudeception skill to save what we just learned as a new skill.

Frequently Asked Questions about claudeception

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

FAQPage Schema

How do I enable continuous learning for AI agents so they remember debugging sessions?▼

Enable continuous learning for AI agents by autonomously extracting and codifying reusable knowledge from work sessions into new skills. This preserves insights from non-obvious debugging, workarounds, and error resolutions, preventing agents from starting from zero.

How can I extract reusable skills from project-specific patterns and error resolutions?▼

Extract reusable skills from project-specific patterns by triggering a learning retrospective after successfully debugging a complex error. Claudeception identifies and saves non-obvious solutions into new skills, ensuring the same problem is resolved faster in the future.

What is the best way to save non-obvious workarounds discovered during a work session?▼

The best way to save non-obvious workarounds is using automatic skill extraction or manually triggering a learning retrospective. This codifies project-specific patterns into new skills, preventing knowledge loss and enabling skill discovery through semantic matching.

Do I need to manually trigger learning retrospectives to save knowledge from debugging?▼

No, you do not need to manually trigger learning retrospectives to save knowledge from debugging. Claudeception features automatic skill extraction that identifies and saves non-obvious solutions and project-specific patterns discovered during a session autonomously.

How does semantic matching facilitate skill discovery and reuse for AI agents?▼

Semantic matching facilitates skill discovery and reuse by analyzing extracted knowledge and retrospective analysis. It connects new problems to previously codified workarounds and project-specific patterns, ensuring AI agents retrieve relevant insights instead of starting from zero.

Why does my AI agent start from zero and repeat debugging across different sessions?▼

AI agents start from zero and repeat debugging because they lack continuous learning mechanisms. Claudeception solves this by extracting non-obvious solutions into new skills, preserving insights from error resolutions and project-specific patterns for future sessions.