learn

Convert ad-hoc session solutions into structured, reusable skill definitions.

127|22|Updated Feb 20, 2026

Search Tags:#documentation #workflow automation #skill creation #reusability #knowledge capture #session learning

Ready to run

npx skills add https://github.com/flonat/claude-code-flonat --skill learn-flonat

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: learn
Source: https://github.com/flonat/claude-code-flonat/tree/main/skills/learn
Command: npx skills add https://github.com/flonat/claude-code-flonat --skill learn-flonat

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill allows you to capture valuable insights, workarounds, and multi-step procedures discovered during a session and transform them into reusable skills for future use, preventing knowledge loss and repeated effort.

Core Features & Use Cases

Knowledge Capture: Extracts non-obvious, repeatable, multi-step workflows into new skill definitions.
Workflow Formalization: Turns ad-hoc solutions into structured, documented skills.
Use Case: After developing a complex debugging process for a specific type of error, you can use /learn to save this process as a new skill, making it instantly available for future similar issues.

Quick Start

Use the learn skill to save the current multi-step workaround as a new skill.

Frequently Asked Questions about learn

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

FAQPage Schema

How do I save reusable knowledge from an AI session?▼

To save reusable knowledge from an AI session, you extract non-obvious, multi-step workflows and ad-hoc solutions into structured, actionable skill definitions, preventing knowledge loss. This formalization ensures future accessibility for similar issues without repeated effort.

What is the best way to formalize ad-hoc workarounds into structured skills?▼

The best way to formalize ad-hoc workarounds is to capture the multi-step procedure and convert it into a new skill definition. This workflow formalization process turns temporary debugging solutions into documented, actionable skills for future use.

How does knowledge capture prevent knowledge decay in workflow automation?▼

Knowledge capture prevents knowledge decay by converting ad-hoc solutions and workarounds into persistent skill definitions. By formalizing these multi-step procedures, the workflow automation ensures that session discoveries remain accessible and reusable for future issues.

Can I create a new skill from a complex debugging process developed during a session?▼

Yes, you can create a new skill from a complex debugging process. By using knowledge capture to extract the repeatable, multi-step procedure, you save the workflow as a structured skill definition, making it instantly available for similar future issues.

When do I need to convert session discoveries into persistent skill definitions?▼

You need to convert session discoveries into persistent skill definitions when you develop a repeatable, multi-step workaround or solution. Formalizing this knowledge prevents knowledge decay and ensures the workflow is immediately accessible for future similar problems.

Does knowledge capture work for documenting multi-step procedures without external dependencies?▼

Yes, knowledge capture works for documenting multi-step procedures without external dependencies. The skill operates independently using internal scripts and references to transform ad-hoc session solutions into structured, reusable skill definitions.