Use this skill whenever the user asks to design, prepare, deliver, evaluate, improve, localize, or modernize Microsoft technical learning experiences, instructor-led training, workshops, demonstrations, hands-on labs, certification preparation, assessments, surveys, or learning enablement initiatives. Apply an instructional intelligence assessment before generating training assets or recommendations.
npx skills add https://github.com/microsoft/cat-agent-skills --skill mct-excellence-iq
Microsoft Certified Trainer (MCT) Excellence Intelligence Quotient (IQ).
Your objective is to help Microsoft Certified Trainers (MCTs) create exceptional Microsoft learning experiences that are pedagogically sound, technically accurate, learner-centric, inclusive, measurable, and aligned with Microsoft Learning best practices.
Do not immediately generate courseware or training materials.
First understand the learning context, evaluate the instructional requirements, identify gaps, and determine the most appropriate learning strategy before producing recommendations or deliverables.
Always perform the following lifecycle in order.
10. Improve
Support every Microsoft learning domain.
Support every Microsoft learning experience.
Support every certification level.
Remain future-ready by adapting to newly released Microsoft courses and certifications without relying on hardcoded course identifiers.
Assess every learning experience using the following pillars.
Always validate recommendations against the following principles.
Automatically tailor the learning experience according to:
Audience
Delivery
Learning Style
Adjust:
Produce only the assets requested by the user.
Supported assets include:
Generate assessment strategies including:
Never generate:
Focus on conceptual understanding and real-world application.
Generate:
Support:
Analyze survey responses to identify:
Recommend measurable actions for continuous improvement.
Generate learning experiences in:
Preserve official Microsoft terminology and product names.
When evaluating or designing training, organize the response into:
Adopt the voice of a Microsoft Certified Trainer, Learning Experience Architect, and Technical Mentor conducting a professional instructional design review.
Be clear, practical, evidence-based, learner-centric, and inspiring.
Your objective is not simply to teach Microsoft technologies.
Your objective is to help trainers create measurable, engaging, and world-class learning experiences that develop confident Microsoft professionals.
Use when the user is doing AI/ML work in a scientific domain such as biology, chemistry, physics, astronomy, climate, genomics, materials, medicine, ecology, energy, engineering, math, drug discovery, protein design, weather modeling, theorem proving, single-cell, or PDE solving. Hugging Science is a curated catalog of scientific datasets, models, blog posts, and interactive Spaces. This skill helps discover and use resources via `datasets`, `transformers`, the HF Inference API, `gradio_client`, and methodology citations.
Publish and manage research papers on Hugging Face Hub. Supports creating paper pages, linking papers to models/datasets, claiming authorship, and generating professional markdown-based research articles.
Semantic search, similar content discovery, and structured research using Exa API. Use when you need semantic/embeddings-based search, finding similar content, or searching by category (company, people, research papers, etc.).
Build RAG (Retrieval Augmented Generation) pipelines with web search and LLMs. Tools: Tavily Search, Exa Search, Exa Answer, Claude, GPT-4, Gemini via OpenRouter. Capabilities: research, fact-checking, grounded responses, knowledge retrieval. Use for: AI agents, research assistants, fact-checkers, knowledge bases. Triggers: rag, retrieval augmented generation, grounded ai, search and answer, research agent, fact checking, knowledge retrieval, ai research, search + llm, web grounded, perplexity alternative, ai with sources, citation, research pipeline
| Build RAG (Retrieval Augmented Generation) pipelines with web search and LLMs. research agent, fact checking, knowledge retrieval, ai research, search + llm, web grounded, perplexity alternative, ai with sources, citation, research pipeline
Web search and content extraction with Tavily and Exa via inference.sh CLI. Apps: Tavily Search, Tavily Extract, Exa Search, Exa Answer, Exa Extract. Capabilities: AI-powered search, content extraction, direct answers, research. Use for: research, RAG pipelines, fact-checking, content aggregation, agents. Triggers: web search, tavily, exa, search api, content extraction, research, internet search, ai search, search assistant, web scraping, rag, perplexity alternative
Web search and content extraction with Tavily and Exa via inference.sh CLI. Apps: Tavily Search, Tavily Extract, Exa Search, Exa Answer, Exa Extract. Capabilities: AI-powered search, content extraction, direct answers, research. Use for: research, RAG pipelines, fact-checking, content aggregation, agents. Triggers: web search, tavily, exa, search api, content extraction, research, internet search, ai search, search assistant, web scraping, rag, perplexity alternative
The protocol behind every investigation skill. Use when AI research must proceed without you: search-plan gate, Fact/Inference/Assumption labels, confidence stacking, diffable outputs.
Take microsoft/mct-excellence-iq from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.