learner

Capture domain-specific insights from conversations and formalize them as reusable skills.

Updated Feb 15, 2026
One-click install
npx skills add https://github.com/leejaedus/pepcode --skill learner-leejaedus
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: learner
Source: https://github.com/leejaedus/pepcode/tree/main/skills/learner
Command: npx skills add https://github.com/leejaedus/pepcode --skill learner-leejaedus

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill captures non-trivial, domain-specific insights from conversations and formalizes them as reusable knowledge for future tasks.

Core Features & Use Cases

  • Capture a domain-specific insight as a reusable skill
  • Standardize metadata for discovery and reuse across prompts and workflows
  • Use case: after a tricky debugging session, extract the decision-making pattern and store it for future guidance

Quick Start

Extract a high-value learned skill from the current conversation and save it for future reuse.

Frequently Asked Questions about learner

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

FAQPage Schema
How do I capture domain-specific insights from a conversation as a reusable skill?

To capture domain-specific insights as a reusable skill, you formalize non-trivial findings from collaborative sessions into a SKILL.md file. This file requires YAML frontmatter with a name and description to standardize the knowledge for future discovery.

What is the best way to document non-obvious workarounds discovered during debugging sessions?

The best way to document non-obvious workarounds is to extract the decision-making pattern and save it as a learned skill. This formalizes undocumented behaviors into reusable knowledge for future engineering, product, or research workflows.

How do I format metadata for knowledge capture to ensure it is discoverable across prompts?

To format metadata for knowledge capture and ensure discoverability, use YAML frontmatter in a SKILL.md file. You must include a name and description, and you can optionally add scripts, references, or asset directories to enrich operational guidelines.

Can I include scripts and references when extracting learned skills from conversations?

Yes, you can include optional scripts, references, and assets directories when extracting learned skills. These components enrich the skill's metadata and operational guidelines, providing deeper context for complex engineering or research tasks.

When should I formalize conversation insights into reusable knowledge?

You should formalize conversation insights into reusable knowledge when you discover non-trivial, domain-specific information like deep debugging patterns or undocumented behaviors during collaborative engineering, product, or research workflows.

Does knowledge capture require any specific dependencies or components?

No, knowledge capture requires no external dependencies or components. The only strict requirement is a SKILL.md file with YAML frontmatter containing a name and description to standardize the extracted insight.