Skill UI

Deploy and govern MCP declarative agents within the Microsoft 365 admin center, managing their lifecycle, assignments, access controls, and compliance so Copilot delivers organizational capabilities securely.

Reads `.agent.md` or `.prompt.md` files, assesses task complexity, tooling, and requirements, then recommends the most cost-efficient GitHub Copilot model(s) (e.g., GPT-5, Claude Sonnet) with rationale for users across different subscription tiers and priorities.

Napkin gives Copilot CLI users a browser whiteboard to draw, sketch, and add sticky notes; Copilot reads snapshots and JSON data, then replies with analysis, suggestions, and next steps for business-oriented thinkers.

Connects to Google NotebookLM to query your uploaded documentation via Gemini’s source-grounded answers, automating authentication, notebook management, and question workflows through the provided run.py wrapper.

The Phoenix CLI is a powerful tool for debugging complex LLM and agent workflows. It allows users to fetch detailed traces, spans, and sessions, analyze errors, and structure findings using open and axial coding techniques. It is essential for investigating complex LLM failures, building robust failure taxonomies, and guiding the next steps in application evaluation after initial instrumentation.

Phoenix Evals lets teams build evaluators for AI/LLM apps by wiring deterministic code with LLM nuance, running Python or TypeScript workloads, validating against humans, and iterating through error analysis, RAG, and production workflows.

Guides hooking Phoenix AI services with OpenInference tracing, covering setup, spans, instrumentation, and production deployment for LLM workflows using Python or TypeScript.

A persistent, graph-based knowledge base designed to accumulate structured knowledge throughout the entire research lifecycle. It stores and connects diverse entities—including published papers, novel ideas, conducted experiments, and testable claims. Inspired by LLM Wiki patterns, it ensures knowledge compounding, meaning the wiki becomes progressively smarter with every piece of input, rather than requiring re-derivation for every query.

Roundup gathers GitHub, calendar, email, and chat data from your configured sources and synthesizes it into status briefings that match your tone, audience, and requested timeframe.

This skill is designed to manage complex, multi-step tasks by coordinating the efforts of multiple specialized AI agents. It provides structured methods for listing available agents, initiating real-time conversations, submitting complex background tasks (such as data analysis or report generation), tracking task status, and maintaining conversational context using session IDs. It is essential when a core task requires diverse expert knowledge or sequential collaboration.

Analyzes and generates a self-contained, explorable HTML report detailing usage metrics for Claude Code sessions. It tracks token consumption (input/output), subagent usage, skill calls, and cache efficiency across specified time ranges. This tool helps developers visualize resource waste and pinpoint areas for workflow optimization, ensuring cost-effective and efficient AI development cycles.

Generates concise TLDR-format summaries for GitHub Copilot customization files, MCP server docs, or related URLs, ensuring actionable examples and context-aware verbosity for inline or chat view sessions.