bootstrap-learning

name	bootstrap-learning
description	Domain-agnostic knowledge acquisition — from zero to structured expertise through conversational learning

Bootstrap Learning Skill

Turn any unfamiliar domain into structured, connected knowledge through progressive conversation.

The Bootstrap Problem

Learning a new domain is hard because you don't know what you don't know. This skill provides a systematic approach to go from zero knowledge to a well-structured skill file.

Learning Methodology — The 5 Phases

Phase 1: Discovery — Map the territory

Goal: Understand the domain's shape before diving in.

Technique	Example Question	What You Learn
Boundary mapping	"What does X include and exclude?"	Scope
Vocabulary scan	"What are the 5 key terms?"	Entry points
Expert identification	"Who are the authorities?"	Trust sources
Adjacent domains	"What's related but different?"	Context

Exit criteria: Can describe the domain in one sentence. Can list 5-10 key terms.

Phase 2: Foundation — Nail the core concepts

Goal: Understand the 3-5 ideas everything else builds on.

• Ask for the simplest possible explanation of each core concept
• Demand concrete examples, not abstractions
• Test understanding by explaining it back in your own words
• Red flag: If the explanation uses jargon from the same domain, you haven't bottomed out

Exit criteria: Can explain core concepts without jargon. Can answer "why does this exist?"

Phase 3: Elaboration — Add depth through cases

Goal: Move from "I understand the concept" to "I can apply it."

Elaboration Type	Purpose	Example
Happy path	How it works normally	"Walk me through a typical OAuth flow"
Edge cases	Where it breaks	"What happens when the token expires mid-request?"
Anti-patterns	Common mistakes	"What do beginners always get wrong?"
Trade-offs	Decision framework	"When would you NOT use event sourcing?"

Exit criteria: Can identify when to use and when NOT to use the thing.

Phase 4: Connection — Link to existing knowledge

Goal: Integrate new knowledge with what you already know.

• Map analogies: "This is like [existing concept] because..."
• Find contradictions: "This conflicts with [existing belief] — which is right?"
• Identify synergies: "Combining this with [skill X] could improve..."
• Update synapses: Create connections in synapses.json

Exit criteria: At least 2 connections to existing skills identified.

Phase 5: Consolidation — Create persistent memory

Goal: Store the learning in the right format and location.

What You Learned	Store As	Location
Domain reference knowledge	SKILL.md	skills/[domain]/
Step-by-step procedure	.instructions.md	instructions/
Interactive workflow	.prompt.md	prompts/
Cross-project pattern	GK-*	Global knowledge
One-off insight	GI-*	Global insights

Exit criteria: At least one memory file created. Synapses updated.

Gap Identification Patterns

Signal	Type of Gap	Action
"I don't know the right question to ask"	Vocabulary gap	Return to Phase 1
"I understand the words but not the concept"	Foundation gap	Return to Phase 2
"I understand it but can't apply it"	Elaboration gap	Return to Phase 3
"I know this but it feels isolated"	Connection gap	Phase 4
"I keep re-learning this"	Consolidation gap	Phase 5

Questioning Strategies

Progressive Depth

1. What — "What is X?" (definition)
2. Why — "Why does X exist?" (motivation)
3. How — "How does X work?" (mechanism)
4. When — "When should I use X?" (context)
5. When not — "When should I NOT use X?" (boundaries)

The Feynman Check

If you can't explain it simply, you don't understand it well enough.

After learning a concept, try to explain it in one paragraph using no jargon. If you can't, identify which part is unclear and loop back.

Skill File Quality Bar

A good bootstrap learning output (SKILL.md) should:

• Contain domain knowledge an LLM wouldn't know generically
• Include concrete examples, not just category labels
• Have tables with real data (thresholds, trade-offs, decision criteria)
• Avoid the "capabilities list" anti-pattern ("Expert in X. Can do Y.")
• Pass the Feynman check — any section should be explainable simply

Synapses

See synapses.json for connections.