Build and evolve a living, representation-agnostic conceptual model for a product (programming) project — the domain's entities, attributes, relationships, and meaning — and choose how it is rendered. Use to model the domain, do conceptual data modelling in UML, run ontology-engineering at the concept level (stable-IRI policy, vocabulary reuse), decide the model *source* (LinkML directly vs model2owl-first), set up a conceptual model, or run terminology/definitions/glossary management. Trigger on "model the domain", "conceptual/UML data model", "set up conceptual model", "which model source", "ontology/terminology management", "ubiquitous language glossary". For the LinkML craft itself (authoring, generation, gates) see linkml-engineering; for generic modelling conventions see modelling-conventions. Conditional: applies to product-development repos that build software; a doc-only/non-product repo does not need it.
How this skill is triggered — by the user, by Claude, or both
Slash command
/meaningfy-architecture:conceptual-modellingThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
> **Provisional pending the first-engagement gate.** This skill's consulting-facing depth will be refined
Provisional pending the first-engagement gate. This skill's consulting-facing depth will be refined after the first real engagement that builds a domain model; treat the wiring here as the floor.
The conceptual model is the single, living, representation-agnostic definition of the domain:
entities, attributes, relationships, and their meaning. It is generative — code, contracts, and
documentation are derived deterministically from it, never hand-maintained in parallel. The
generation layer is a make generate-models-style bridge that stays outside the LLM path (the
principled answer to spec-drift and failed model-driven development): the LLM authors the source,
a deterministic toolchain renders the targets.
Conditional. This skill applies only to product-development (programming) projects — repos that build software with a domain to model. A documentation-only or non-product repo does not need a conceptual model and should not scaffold one.
This skill owns the model; it does not own system topology, code layering, or normative requirements — see Boundary & Related Skills.
The model is a first-class asset with a home of its own. Pick the mode explicitly:
model/ directory (default). This is what project-setup scaffolds by default
(EPIC-09): the model lives beside the code it generates, versioned with the repo. Generated
artefacts land under the relevant package (e.g. models/, see cosmic-python) via
make generate-models. Use this for a model owned by one product.Golden-thread implication. Model entities carry stable IDs (see ontology practices). When the
model lives in its own repo, downstream code and specs cite those entity IDs across repos — the
cross-repo rung of the golden thread (see ../../spine/golden-thread.md,
which notes cross-repo IDs are convention-only for now).
LinkML is the wired default source for the model. It is declarative, generates many targets, and
is the canonical domain-definition format the architecture skill already references for contracts.
Choosing the source is an explicit decision point — never silently defaulted:
We deliberately do not build a source-adapter abstraction (YAGNI) — alternatives are documented
as named patterns, not wired behind an interface. The source decision and the concept-level
ontology policy are owned here (see references/ontology-practices.md);
the LinkML craft that executes on the chosen source is owned by linkml-engineering.
From the chosen source, code, contracts, and documentation are generated deterministically and
outside the LLM path — reproducible, diffable, CI-checkable. This skill owns that this is how the
model becomes artefacts; it does not own the LinkML authoring, the generator wiring, the custom
templates, or the quality gates — those belong to
../linkml-engineering/SKILL.md. The semantic core (Pydantic, JSON
Schema, OWL, SHACL) and the enable-on-demand targets are catalogued there; see
references/generators.md for the pointer.
The seam to cosmic-python. The model owns the contract; the generated Pydantic/JSON Schema
is the contract. A service's entrypoints/api consumes the generated contract — it does not
redefine the domain. This skill owns the source side of that seam; linkml-engineering owns the
generation that crosses it.
A model is more than a schema. Two adjacent practices are part of the discipline:
references/terminology-management.md.This skill OWNS: the living conceptual model (representation-agnostic source of domain truth); conceptual data modelling in UML; the concept-level ontology policy (stable-IRI policy, modularity, vocabulary-reuse principle); the model-source decision (LinkML-direct vs model2owl-first); and terminology / definitions / glossary management (ubiquitous language).
This skill REUSES:
../modelling-conventions/SKILL.md.
This skill applies those conventions; it does not restate them.This skill DELEGATES:
../linkml-engineering/SKILL.md. This skill picks the source and
owns the model; linkml-engineering executes the LinkML.../architecture/SKILL.md. Architecture authors which contracts
exist; this skill owns the domain model that backs them.../cosmic-python/SKILL.md. entrypoints/api consumes the
generated contract; this skill owns the model that produces it.../../spine/README.md). Model classes are ubiquitous language
that specs reference; specs state the rules.Conditional: product-development (programming) projects only — see the Overview.
Related: modelling-conventions, linkml-engineering, architecture, cosmic-python.
Guides collaborative design exploration before implementation: explores context, asks clarifying questions, proposes approaches, and writes a design doc for user approval.
Creates structured, bite-sized implementation plans from specs or requirements before writing code. Useful for breaking down multi-step tasks into testable steps with file structure and task boundaries.
Provides Slack GIF creation utilities with dimension/FPS/color constraints and Python PIL-based frame generation. Use for animated Slack emoji or message GIFs.
npx claudepluginhub meaningfy-ws/skillery --plugin meaningfy-architecture