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.