skills for everythingalex-bootstrap-learning-skill
Domain-agnostic knowledge acquisition through conversational learning and progressive skill building
SKILL.md
| Name | alex-bootstrap-learning-skill |
| Description | Domain-agnostic knowledge acquisition through conversational learning and progressive skill building |
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
- What — "What is X?" (definition)
- Why — "Why does X exist?" (motivation)
- How — "How does X work?" (mechanism)
- When — "When should I use X?" (context)
- 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.