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Layer 2 · Problem space

The domain

/layers-domain

Map domain concepts, terminology conflicts, and bounded contexts — the raw material the conceptual model is built from.

Example prompts

Drop one of these into your AI tool. The skill takes it from there.

  • Help me /layers-domain — there's confusion about what "order", "cart", and "basket" mean across our product.
  • Map the bounded contexts in this CRM with /layers-domain. Different teams clearly use the same words differently.
  • Run /layers-domain on these stakeholder notes — pull out the noun harvest and the terminology conflicts.

The skill itself

What /layers-domain tells the AI to do

Below is the actual skill — the instructions your AI tool follows when you run it. Reading it is optional; the skill loads itself.

/layers-domain

Assumes /layers-intro has been loaded. This skill is a library of techniques, not a script — see “How to use these skills” there.

The domain layer maps what exists in the real world independently of any product: the concepts, terminology, processes, relationships, and mental models users bring with them. This is observation, not design.


The decisions this layer makes

  • What the key concepts in this domain are, and how they relate
  • What language people use — and where it conflicts or diverges
  • Where the natural seams are: communities that use the same words differently
  • What events and processes structure the domain’s activity over time

If the domain is already well understood and uncontested, you may not need this layer — go straight to the conceptual model.


Disciplines — what keeps domain work honest

  • Don’t resolve contradictions — record them. Messy, inconsistent domain language is data: it signals where communities have diverged and where the product will later have to choose. Resolution belongs to the conceptual model, not here. Capture synonyms (same thing, different names) and polysemy (same name, different things in different contexts) as findings, not problems.
  • Stay in the real world. Push back whenever answers drift toward product or interface decisions. The question is always how the domain works before your product enters it.
  • Real objects vs instances (for the noun harvest). A true object is instanceable (you can have many), structured (has its own attributes), and useful (people care about it in its own right). Watch for instances mistaken for objects: “CAC”, “ROAS”, “LTV” aren’t objects, they’re instances of one object, Metric. Mark each noun object / attribute / instance-or-value / unclear — and don’t filter aggressively; the conceptual model does the sorting.
  • Beliefs vs reality. If you’re mapping what the team believes rather than researched fact, say so throughout — it’s the team’s model, not necessarily how users experience the domain.

Techniques

Pick what fits — concept mapping plus a terminology audit is the usual core.

TechniqueUse it when
Concept maps / bubble diagrams (graph TD/LR)The domain is complex and poorly understood. Informal nodes-and-lines show how concepts relate without forcing premature structure.
Terminology auditCapture, per concept: the names used, who uses which and when, and whether the conflict is synonymy or polysemy. Don’t pick a winner.
Bounded-context mappingCommunities share vocabulary internally but diverge across groups. Name and describe each seam — it will matter when the model is defined.
Noun harvestCompile every noun surfaced, marked object / attribute / instance-or-value / unclear. Raw material for the conceptual model.
Domain event storming (Brandolini)Process-heavy domain. Name significant events in past tense on a timeline; note triggers and results. Objects with events around them likely need state diagrams later.
Expert interviewsDomain knowledge lives in people, not documents — surfaces tacit knowledge and contested terms.
Document & artefact analysisThe domain produces contracts, forms, invoices that reveal natural structure and vocabulary.
Competitive analysisEntering an established domain — existing products reveal how others modelled it, and where they disagree.
ShadowingWorkflows are hard to articulate; watching reveals what people actually do.

Working with the designer

Open by asking what domain you’re mapping, who operates in it, and what they’re trying to accomplish before any product exists. Then surface concepts — listen for nouns (candidate objects), verbs (processes), and the natural vocabulary, including what people actually call things.

Offer the technique that fits the live question: a concept map when relations are unclear, a terminology audit when language is contested, event storming when the domain is process-heavy. Do the next useful thing, not all of them.

Capture only the residue — the concept map if it earned its place, the documented terminology conflicts and bounded contexts, any key events, and the noun harvest. This is raw material, not a finished document.

When the domain is mapped, the noun harvest is the starting point for /layers-conceptual-model.

Example output

An example of what /layers-domain captures

From a domain analysis on a permissions system.

Noun harvest — competing terms

Term used Source Conflict
Block Designers
Report Engineers Same thing as Block; lives in code only
Member Customer support Sometimes means User, sometimes a paying seat
User Engineering Always a person with login
Permission set Sales decks Same as Role
Role Product Used in UI

Bounded contexts

  • Customer-facing UI uses: Workspace, User, Role, Folder, Dashboard, Block, View.
  • Internal codebase uses: Tenant, Member, Permission, Group, Report.

The mismatch isn't accidental — it's the surface signal of a model that hasn't been committed to. Recommendation: pick one vocabulary per concept and migrate the codebase over time.

Install

Install the skills

Install in Claude Code, Cursor, Codex & more

npx skills add jamiemill/layers-skills

Install once with the skills package, then run any /layers-* skill in your AI tool.