ontology-school-path-generator

Generate Ontology School course paths/labs with progressive step ontologies from source ontology material. Use when user asks to tutorialize an ontology into…

npx skills add https://github.com/microsoft/ontology-playground --skill ontology-school-path-generator

Download ZIP GitHub

Try this skillSponsored

Ontology School Path Generator Skill

Goal

Generate a complete Ontology School course with progressive ontology steps.

Read First

docs/authoring-guide.md
docs/learn-content-guide.md
docs/embed-guide.md
Example lab:

content/learn/iq-lab-retail-supply-chain/
catalogue/official/iq-lab-retail-step-1/

Workflow

Extract a teachable subset from source ontology.
Define step progression (4-7 steps).
Create step ontologies under catalogue/official/<slug>-step-N/.
Mark step ontologies with "category": "school".
Create course under content/learn/<course-slug>/.
Add <ontology-embed> + diff for progressive steps.
Include at least one quiz per article.

Person Names

If the course, step ontologies, examples, sample data, quests, quiz text, or docs need any person names, first use the name-generator skill. Do not invent customer, employee, patient, student, instructor, reviewer, or other human names.

All generated person names must come from:

data/reference/FNF-2026-06-01-01002-0268.csv

Use the CSV FullName column by default, and keep selected names consistent across source markdown, RDF/OWL examples, generated catalogue data, and tests.

Validate

npm run qa:tutorial-content
npm run build

Human Review Flow

If lesson content is not yet approved:

Add reviewStatus: under-human-review to article frontmatter
Open review issue via .github/ISSUE_TEMPLATE/ontology-school-review.yml

Done Criteria

Course renders in /#/learn
Step ontologies are isolated in School category
Any person names introduced by the lesson or sample data came from the name-generator skill / approved CSV fixture
Builds and validators pass

More skills from microsoft

OSS growth hacker persona

agent-framework-azure-ai-py

Build Azure AI Foundry agents using the Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Use when creating persistent agents with AzureAIAgentsProvider, using hosted tools (code interpreter, file search, web search), integrating MCP servers, managing conversation threads, or implementing streaming responses. Covers function tools, structured outputs, and multi-tool agents.

airunway-aks-setup

Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: "setup AI Runway", "onboard AKS cluster", "install AI Runway", "airunway setup", "deploy model to AKS", "GPU inference on AKS", "KAITO setup on AKS", "run LLM on AKS", "vLLM on AKS", "set up model serving on AKS", "AI Runway controller".

appinsights-instrumentation

Guidance for instrumenting webapps with Azure Application Insights. Provides telemetry patterns, SDK setup, and configuration references. WHEN: how to instrument app, App Insights SDK, telemetry patterns, what is App Insights, Application Insights guidance, instrumentation examples, APM best practices.

applicationinsights-web-ts

Instrument browser/web apps with the Application Insights JavaScript SDK (@microsoft/applicationinsights-web). Use for Real User Monitoring (RUM) — page views, clicks, AJAX/fetch dependencies, exceptions, custom events, and browser-side GenAI agent traces correlated to backend OpenTelemetry traces. Covers SDK Loader Script and npm setup, framework extensions (React, React Native, Angular), Click Analytics, telemetry initializers, and OTel GenAI semantic conventions for agent/tool/model spans emitted from the browser.

azure-ai-anomalydetector-java

Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring.

azure-ai-language-conversations-py

Implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK. Use when working with ConversationAnalysisClient to analyze conversation intent and entities, building NLP features, or integrating language understanding into applications.

Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines. Triggers: "azure-ai-ml", "MLClient", "workspace", "model registry", "training jobs", "datasets".