3102 skills
Generate an executive assessment report from GitHub Quick Review (ghqr) scan data. Produces an executive summary, a dedicated section per validated subject with all findings, and a prioritized 30/60/90-day remediation plan. Use when the user asks for a report, executive summary, best practices posture overview, or a remediation roadmap from ghqr scan results.
Build agentic applications with GitHub Copilot SDK. Use when embedding AI agents in apps, creating custom tools, implementing streaming responses, managing sessions, connecting to MCP servers, or creating custom agents. Triggers on Copilot SDK, GitHub SDK, agentic app, embed Copilot, programmable agent, MCP server, custom agent.
Analyzes recently modified code and creates pull requests with simplifications that improve clarity, consistency, and maintainability while preserving functionality
Migrate Python apps from Azure OpenAI Chat Completions to the Responses API. Covers AzureOpenAI/AsyncAzureOpenAI client migration to the v1 endpoint, streaming, tools, structured output, multi-turn, EntraID auth, and model compatibility checks. Python-focused, Azure OpenAI-specific. USE FOR: migrate to responses API, switch from chat completions, openai responses, upgrade openai SDK, responses API migration, move from completions to responses, gpt-5 migration, azure openai python migration, chat completions to responses, AzureOpenAI to OpenAI client, python azure openai upgrade. DO NOT USE FOR
Scaffold a complete, production-ready Azure project from scratch for ANY use case: RAG chatbots, multi-agent systems, API backends, data pipelines, Azure Functions, full-stack web apps, ML training/inference, or event-driven microservices. USE FOR: gathering requirements through adaptive questioning, generating complete project folder structures with all source files, Azure Bicep infrastructure, azure.yaml for azd-up deployment, Docker configuration, CI/CD workflows, README with architecture diagrams, and Responsible AI documentation. Produces fully deployable Azure projects targeting Azure Co
Trigger ADO pipelines for a Copilot-created PR by posting /azp run comments. Use when the user asks to trigger CI pipelines for a specific PR.
Use when asked to review code, review a branch, or do a code review. Spawns Breaker (correctness) and API Analyst (compatibility/conventions) sub-agents while the orchestrator reviews architecture, tests, performance, and security.
The authority on pulling a pull request — Azure DevOps or GitHub — into an isolated local worktree so a human can review it in their editor, and on posting human-controlled review comments back to the PR (ADO via the ado PR-thread MCP tools or the Azure DevOps REST API, GitHub via the gh CLI). Auto-detects the platform and all identifiers from the PR URL; not tied to any specific repository. The AI assists an interactive human review and only ever posts comments the human explicitly requests. If you find yourself checking out a PR branch over the user's own working tree, or posting AI-authored
This skill should be used when the user asks to "run policy check", "check policy", "policy-check", or needs to validate package compliance. Provides guidance on running policy checks for specific packages or the entire repository.
Triage test failures in an Azure DevOps pipeline run for Fluid Framework. Use when the user shares an ADO build/pipeline URL or build ID and wants failures analyzed, when they ask "why did this pipeline fail", "summarize the test failures", "is this test flaky or a real bug", "compare these runs", or wants bugs filed for genuine failures. Covers the Real Service End to End Tests pipeline as the worked example but applies to any FF test pipeline.
Fluid Framework client release group — minor releases, patch releases, and post-release type test updates. Covers release prep, branching, version bumps, changelogs, release notes, and type test baselines. In autonomous mode, auto-detects state from the schedule and repo, attempts to execute, and falls back to a GitHub issue on failure. Triggers on "release", "do the release", "release status", version bump, release notes, changelog, release branch, or release engineering.
Use when creating a pull request in the Fluid Framework repo. Composes a PR title and body following Fluid Framework conventions, proposes them to the user, then pushes the branch and creates the PR on GitHub. Triggers on "create a PR", "make a PR", "open a PR", "submit a PR", or "push and create a PR".
Use when composing, writing, drafting, or reviewing a PR title, PR description, or PR body in Fluid Framework — provides title style, body template, and section guidance.
Internal reusable workflow for resolving the correct Git comparison base for branch reviews and change analysis. Finds the PR target or canonical Fluid Framework upstream, selects the newest shared target-history commit, and detects substantial divergence.
Use when the user explicitly asks for a CI check or to push their branch — e.g. "ci readiness", "check ci", "pre-push check", "ready for CI", "ci check", "ready to push", "push my changes", "push the branch", "let's push". Catches common CI failures before pushing — formatting, stale API reports, missing changesets, policy violations.
Use when customer-facing API changes were made — i.e., API report .md files differ from the branch's resolved comparison base. Guides through release tag assignment, API Council review requirements, breaking change classification, deprecation process, and changeset guidance. Triggered automatically by ci-readiness-check when api-report diffs are detected.
Use when design is complete and you need detailed implementation tasks for engineers with zero codebase context - creates comprehensive implementation plans with exact file paths, complete code examples, and verification steps assuming engineer has minimal domain knowledge
Use this skill to build features or debug anything that uses a webapp frontend.
Use this when you have finished making code changes and you are ready to update the documentation based on those changes.
Use when writing or changing tests, adding mocks, or tempted to add test-only methods to production code - prevents testing mock behavior, production pollution with test-only methods, and mocking without understanding dependencies
Use when implementing any feature or bugfix, before writing implementation code - write the test first, watch it fail, write minimal code to pass; ensures tests actually verify behavior by requiring failure first
