# Linkml Engineering > 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. - Skill: `meaningfy-ws/linkml-engineering` (Agent Skill, multi-file: 6 files) - Install (CLI): `npx skillmds@latest add meaningfy-ws/linkml-engineering` - Raw SKILL.md: https://api.skillmd.com/api/skills/meaningfy-ws/linkml-engineering/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Data & Analytics - License: Apache 2.0 - Author: meaningfy-ws (https://skillmd.com/u/meaningfy-ws) - Updated: 2026-09-10 - Page: https://skillmd.com/skills/meaningfy-ws/linkml-engineering --- # LinkML Engineering ## Overview LinkML is the **pivot artefact** in Meaningfy's single-source-of-specifications approach: from one hand-authored schema, a deterministic toolchain renders Pydantic, JSON Schema, OWL, SHACL, JSON-LD, TypeScript, SQL, docs, and diagrams. This skill owns the *engineering* of that pivot — how to run LinkML in a layered project without it becoming a foot-gun. It is deliberately **not a greenfield schema-authoring skill**. LinkML is always a *follow-up* representation of a model that already exists (a UML model, a text specification, a model2owl output, another existing model). If no model exists yet, that is `conceptual-modelling`'s job first. This skill **reuses** [`../modelling-conventions/SKILL.md`](../modelling-conventions/SKILL.md) for the representation-agnostic craft (reusable properties, URI-everywhere, anti-patterns, naming) and adds the **LinkML-specific mechanics** that realise those principles. It is not an ontology-authoring skill — but it keeps ontologies in mind, because a well-authored schema must generate complete, correct OWL and SHACL even when the delivered target is JSON or Python. ## The workflow: derive → author → generate & guard ### 1. Derive — LinkML from an existing source Turn the upstream artefact into an initial schema. There is a distinct entry point per input kind (UML, text spec, model2owl output, existing model). See [`references/deriving-linkml.md`](references/deriving-linkml.md). ### 2. Author / refine — LinkML mechanics atop the shared conventions Apply the shared conventions through LinkML's own constructs: top-level reusable `slots:`, the URI-as-datatype artifice, minted `prefixes`/`default_prefix` with **implicit** `class_uri`/`slot_uri`, `enums` over free strings, and constraints declared in the schema. SEMIC and model2owl conventions and checkers are the cited authority for the URI/naming/datatype slice. See [`references/authoring-mechanics.md`](references/authoring-mechanics.md). **Complete OWL/SHACL is a first-class outcome, not a side effect.** Because every class and property is URI-identified, the schema can render *complete* OWL and SHACL — the elements that MUST be present for each are listed in [`references/owl-shacl-completeness.md`](references/owl-shacl-completeness.md). Author for that completeness even when the project only ships JSON/Python/DB, so the semantic option stays open. ### 3. Generate & guard Render the targets deterministically, **outside the LLM path**, via `make`-driven generation with custom templates (a strict project Pydantic base, custom code generation for other languages) and per-module artefact layout. Then establish quality gates — **chosen with the user, not imposed**. See [`references/generation-and-templates.md`](references/generation-and-templates.md) and [`references/quality-gates.md`](references/quality-gates.md). ## The working loop 1. Locate the schemas dir and the `make` target that generates from it — never invoke generators ad hoc. 2. Make the change in **YAML only** (never hand-edit a generated file), respecting the conventions. 3. Lint the schema (warnings not muted); fix what it reports. 4. Regenerate the targets. 5. Review the generated diff — it should touch only what you expect. 6. Validate the inline examples; run the project's tests. 7. Commit the YAML **and** the regenerated artefacts together, never separately. ## Boundary & Related Skills **This skill OWNS:** the operational LinkML discipline — deriving LinkML from an existing source; LinkML-specific authoring mechanics (reusable `slots:`, URI-as-datatype, implicit `class_uri`/`slot_uri`, enums, schema-level constraints); complete-and-correct OWL/SHACL generation; custom generator templates and `make`-target transformation automation (including diagrams); per-module artefact generation; and the guided quality-gate process. **This skill REUSES:** - Generic modelling craft (reusable-property and URI-everywhere *principles*, anti-patterns, naming, guardrails) → [`../modelling-conventions/SKILL.md`](../modelling-conventions/SKILL.md). This skill states the LinkML *mechanism*; it does not restate the principle. **This skill DELEGATES:** - The representation-agnostic model, UML conceptual modelling, terminology, and the LinkML-vs-model2owl **source decision** → [`../conceptual-modelling/SKILL.md`](../conceptual-modelling/SKILL.md). - Where the generated Pydantic/JSON Schema contract is *consumed* (a service's `entrypoints/api`) → [`../cosmic-python/SKILL.md`](../cosmic-python/SKILL.md). - Scaffolding the gates/automation/per-module layout into a repo → `project-setup` (conditional on the project using LinkML). **Conditional:** product-development projects that use LinkML. A project modelling only in OWL/UML without a LinkML pivot does not need this skill. **Related:** `modelling-conventions`, `conceptual-modelling`, `architecture`, `cosmic-python`, `project-setup`.