research-methodology

name	research-methodology
description	Systematic research methodology for deep-diving into topics, frameworks, and tech stacks before course creation. Defines source quality standards, research artifact formats, and framework extraction criteria.

Research Methodology

When This Skill Applies

This skill activates when you need to investigate a topic deeply enough to teach it. It applies before any course content is written — the research phase that produces the knowledge artifacts (platform-summary.md, teaching-context.md, framework-extraction.md) that the content creation pipeline consumes.

Use this when:

• Planning a new course on a technology or framework
• Deep-diving into a topic before writing text classes
• Analyzing a reference repository for patterns worth teaching
• Building a platform-summary.md for an existing course
• Extracting frameworks from documentation or codebases

The Research Principle

Extract architecture, not features. Anyone can list what a tool does. Course authors need to understand HOW it works, WHEN to use each part, and WHERE things go wrong. Architecture → decision points → frameworks → course content.

Source Quality Hierarchy

Not all sources are equal. Rate every source:

Tier	Source Type	Trust Level	Use For
S	Official documentation	Highest	Architecture, API, terminology, key numbers
A	Official blog posts, changelogs	High	Recent changes, design decisions, roadmap
A	Official GitHub repos, examples	High	Patterns, conventions, working code
B	Community-maintained docs	Medium	Verify against official before using
B	Expert blog posts (recognized contributors)	Medium	Insights, opinions, worked examples
C	Forum discussions, Stack Overflow	Low	Common mistakes, gotchas, real-world issues
D	Random blog posts, tutorials	Lowest	Cross-reference only, never trust alone

Rule: Every fact in a platform-summary.md must trace to a Tier S or A source. Tier B-D sources are useful for identifying WHAT to investigate, not for stating facts.

Research Artifact Standards

Every research session produces artifacts under the consumer plugin's research directory (path convention is consumer-specific; dojo-academy uses content/courses/{course-slug}/research/). Each artifact follows a specific format defined in the resources:

• RESEARCH.md — session log (what was investigated, key findings, open questions)
• sources.md — registry of all sources with tier ratings
• framework-extraction.md — named frameworks ready for text classes
• repo-analysis-{name}.md — per-repo analysis documents
• competitive-landscape.md — what other courses/tutorials cover this topic

The 5 Things to Extract

For every topic researched, extract these 5 categories:

Category	What to Capture	Why It Matters for Course
Architecture	How it works — layers, components, data flow	Students need mental models, not feature lists
Terminology	Exact official names and definitions	Wrong terms in course content = lost credibility
Key Numbers	Limits, defaults, thresholds, pricing	Students need these for real-world decisions
Decision Points	When to use X vs Y, trade-offs	These become named frameworks in text classes
Common Mistakes	Gotchas, anti-patterns, misconceptions	These become "What Goes Wrong" sections

Framework Extraction Criteria

A research finding becomes a named framework when it meets ALL of these:

• It models a decision students will face (not just a fact)
• It can be structured as a table, matrix, tree, or checklist
• It has a memorable name (specific, not generic)
• A worked example can demonstrate it in action
• It's testable — you could write a quiz question about it

See resources/framework-extraction.md for the full extraction methodology.

Resources

• resources/research-template.md — standard RESEARCH.md structure
• resources/source-quality.md — detailed source evaluation criteria
• resources/framework-extraction.md — how to identify and name teachable frameworks