Meaningfy git and GitHub conventions — Conventional Commits (imperative, no trailing punctuation), branch naming, rebase/merge etiquette, the pull-request workflow, free-tier GitHub constraints, and dev-environment hygiene. Use when committing, branching, opening or maintaining a PR, or setting up a development environment for a Meaningfy project.
System-level solution architecture — C4 levels (Context, Container, Component, Code), ArchiMate and UML notations, ADRs, and contracts (OpenAPI/AsyncAPI/LinkML). Use for system and solution design, architecture documentation, and architectural decision-making. Distinct from code structure inside a service (see the cosmic-python skill).
Write BDD Gherkin feature files and fabricate test data from a specification. Use to project a use case catalogue (Cockburn White/Blue) or turn acceptance criteria or an EPIC into business-language `.feature` scenarios — Scenario Outline with Examples, explicit edge cases, no implementation detail, traced back to use cases. Trigger on "write Gherkin", "write feature files", "BDD scenarios for this acceptance criterion", "derive scenarios from use cases", "fabricate test data".
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.
Clean Architecture and Cosmic Python guidance for well-tested, layered Python systems. Use for designing Python projects with layered architecture (models, adapters, services, entrypoints), enforcing Clean Code and SOLID principles, testing strategies (unit tests, BDD, Gherkin), CI/CD setup (pytest, tox, importlinter), and architectural decision-making (ADRs). Applicable to systems requiring strict boundary enforcement, clean separation of concerns, and comprehensive test coverage.
The operational LinkML craft, downstream of an existing model or spec — never greenfield. Use to derive a LinkML schema from a UML model, a text spec, a model2owl output, or another existing model; to author/refine it (reusable slots, the URI-as-datatype artifice, implicit class_uri/slot_uri, enums, schema-level constraints); to generate the full target set (Pydantic/JSON Schema/OWL/SHACL/TS/SQL) with custom templates and make-target automation including diagrams; and to establish LinkML quality gates. Trigger on "write/derive a LinkML schema", "generate models/OWL/SHACL from LinkML", "custom Pydantic template", "LinkML quality gates", "per-module generation", "make generate-models". Reuses modelling-conventions for generic craft; defers the model concept to conceptual-modelling. Not an ontology-authoring skill.
The shared, representation-agnostic modelling craft reused across the modelling skills — naming discipline, modelling anti-patterns, and the guardrails a modeller follows while working, plus the two load-bearing principles (decouple attributes into reusable first-class properties; identify everything by a stable URI, implicit by default). Use when authoring or reviewing ANY model regardless of representation (conceptual, UML, LinkML, ontology). Trigger on "modelling conventions", "naming conventions for a model", "modelling anti-patterns", "reusable properties/slots", "should this attribute be shared", "URI/identity discipline", "review this model for smells". This is the cosmic-python-style reuse layer for modelling — cited by conceptual-modelling and linkml-engineering, never restated by them.
Scaffold or modernise a Meaningfy-standard repo and PROJECT the Meaningfy spine into it — a top-level package (no src/), Poetry + dedicated root tool configs, cosmic-python layering with import-linter guardrails, TDD+BDD tests, a CLAUDE-canonical agentic setup (CLAUDE.md is canonical; AGENTS.md is an optional symlink), the openspec/ spine (config + pinned meaningfy schema + /opsx:* commands + golden thread), three archetypes (product/library/doc-only) with fixed gate profiles, conditional model/ and CD seam, Antora docs, infra, and CI. Use when starting a new repo or bringing an existing one up to standard. Trigger on "set up a new project", "scaffold a repo", "bootstrap a Python project", "new Meaningfy project", "initialise project structure", "add the standard tooling/docs/CI", "project the spine / set up openspec", "modernise/revamp an existing repo", "bring this project up to standard", "gap analysis against Meaningfy standards".