learner

Extract codebase-specific debugging fixes into reusable skills with YAML frontmatter.

Updated May 18, 2026
One-click install
npx skills add https://github.com/solitude6060/Yao-skills --skill learner-solitude6060
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: learner
Source: https://github.com/solitude6060/Yao-skills/tree/main/skills/learner
Command: npx skills add https://github.com/solitude6060/Yao-skills --skill learner-solitude6060

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you capture a learned solution from a debugging session into a reusable, high-quality skill that others can apply later without relearning the same investigation.

Core Features & Use Cases

  • Skill extraction with quality gates: Ensures the captured insight is non-Googleable, codebase-specific, and hard-won before it’s saved.
  • Structured expertise vs workflow classification: Separates durable principles from stable step-by-step procedures so future improvements don’t destabilize operations.
  • Validated, trigger-driven skill files: Requires YAML frontmatter and rejection rules for vague or overly generic outputs to improve discovery and matching.

Quick Start

Use the learner skill after you solve a tricky, codebase-specific bug by providing the exact error, the files/lines involved, and the precise fix so it can extract a reusable principle or workflow.

Frequently Asked Questions about learner

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

FAQPage Schema
How do I capture debugging insights into reusable knowledge for my team?▼

Capturing debugging insights involves extracting the exact error, affected file paths, and precise fix from a conversation into a structured skill file. This ensures future teams can recall the same hard-won principle without relearning the investigation.

What inputs are needed to extract a debugging session into a workflow skill?▼

Extracting a debugging session requires evidence-rich inputs: the specific error, file paths and line numbers, triggers, and the exact fix. Providing these details ensures the output captures a durable, codebase-specific principle rather than a generic solution.

How do I structure extracted troubleshooting outcomes for trigger-based retrieval?▼

Troubleshooting outcomes are structured using YAML frontmatter formatting to enable trigger-based retrieval. This format enforces classification into expertise versus workflow and applies rejection rules to prevent vague, un-Googleable outputs from cluttering the knowledge base.

When should I classify an incident triage outcome as expertise versus workflow?▼

Classify an incident triage outcome as expertise when it represents a durable principle, and as workflow when it outlines stable step-by-step procedures. This separation ensures future improvements to procedures do not destabilize core operational principles.

Does the skill extraction process reject generic or easily Googleable fixes?▼

Yes, the skill extraction process applies quality gates that reject generic or easily Googleable fixes. It specifically requires the insight to be non-Googleable, codebase-specific, and hard-won before saving it as a validated, trigger-driven skill file.

Why use YAML frontmatter for incident triage knowledge capture?▼

YAML frontmatter is used for incident triage knowledge capture to improve discovery and matching. It enforces validated, trigger-driven metadata that categorizes the fix, ensuring fast recall of specific principles or procedures during future incidents.