domain-vocab

# Domain Vocabulary Learning

Build conceptual vocabulary for any domain to enhance AI prompting effectiveness. This skill uses **token priming** to activate domain-specific knowledge, **divergent exploration** for creative discovery, and **depth progression** from popular to expert-level terminology.

## Core Principle: Token Priming

> "Response quality improves dramatically when you inject tokens that experts actually use, rather than just adopting an expert persona."

The skill works by injecting precisely fitting tokens that prime the model's attention toward the target domain. Like a game where visiting a region unlocks its map, the right tokens unlock domain-specific knowledge.

## When to Use

- Learning a new technical domain
- Preparing to work with domain experts
- Improving AI prompt quality for specific fields
- Exploring unfamiliar territories through divergent thinking
- Deepening from popular knowledge to expert-level understanding

## Core Workflow (6 Phases)

### Phase 0: Token Priming

**Objective:** Prime the context with authentic domain tokens before concept extraction.

**Actions:**
1. **Summon key figures** (3-5 people) associated with the domain
2. **Extract their vocabulary** - terms/concepts these figures would actually use
3. **Sample recent literature** - pull tokens from arXiv, papers, or authoritative sources
4. **Prime the context** - use these tokens to "lay the foundation" before proceeding

**Example for "DevOps":**
```
Key Figures: Gene Kim, Jez Humble, Nicole Forsgren
Their Tokens: "deployment frequency", "lead time for changes",
              "MTTR", "change failure rate", "DORA metrics"
Recent arXiv: "AIOps", "observability-driven development", "GitOps reconciliation"
```

**Output:** Context primed with authentic expert-level tokens

### Phase 1: Domain Identification

**Objective:** Clarify the target domain, scope, and desired depth.

**Actions:**
1. Confirm the domain from user input
2. If domain is broad, ask about focus a