learner

Extract reusable debugging Skills from conversations with quality gates and YAML frontmatter.

Updated Apr 29, 2026
One-click install
npx skills add https://github.com/nichobbs/lyric-lang --skill learner-nichobbs
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: learner
Source: https://github.com/nichobbs/lyric-lang/tree/main/.claude/skills/learner
Command: npx skills add https://github.com/nichobbs/lyric-lang --skill learner-nichobbs

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It helps convert hard-won, codebase-specific insights from a conversation into a reusable Skill artifact that teams can activate later, while preventing generic or easily Googleable takeaways from being saved.

Core Features & Use Cases

  • Guided, quality-gated extraction: Captures only non-obvious, debugging-revealed learning with clear recognition signals and precise triggers.
  • Structured Skill outputs: Produces a stable format with an optional Expertise/Workflow split so improvement cycles can update principles without destabilizing procedures.
  • Project-level knowledge retention: Supports saving learned skills into the repo’s .omc/skills/ location for team persistence.
  • Use Case: After resolving a tricky Lyric-related failure, extract the exact root cause and the precise fix into a reusable decision heuristic with concrete file/line pointers and trigger phrases.

Quick Start

Extract a Level 7 learned Skill from the current conversation, using the Expertise/Workflow split and including a triggers list derived from the specific error symptoms and file paths.

Frequently Asked Questions about learner

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

FAQPage Schema
How do I extract reusable debugging heuristics from a developer troubleshooting conversation?

To extract reusable debugging heuristics, the conversation must be analyzed to capture non-obvious, codebase-specific insights with clear recognition patterns, precise triggers, and concrete file pointers, filtering out easily Googleable takeaways.

What is the best way to save codebase-specific knowledge management insights into a structured format?

The best way to save codebase-specific knowledge is by generating a Skill artifact with stable workflows, an Expertise/Workflow split, and required YAML frontmatter for discovery, ensuring improvement cycles update principles without destabilizing procedures.

Can I save extracted troubleshooting skills directly into my repository for team persistence?

Yes, you can save extracted troubleshooting skills into the repository's `.omc/skills/` directory, which provides project-level knowledge retention so that teams can activate those learned debugging heuristics later.

How does the expertise and workflow classification rule improve knowledge management for debugging?

The expertise and workflow classification separates stable procedures from mental models, allowing improvement cycles to update context-specific debugging principles and recognition patterns without destabilizing the core procedural steps.

Why are generic takeaways filtered out when extracting debugging mental models?

Generic takeaways are filtered out by quality gates to ensure that only non-Googleable, context-specific debugging efforts are preserved, preventing easily searchable information from diluting the actionable mental models and triggers.