
用于自动操作浏览器、截图并执行端到端测试流程
This open-source curriculum introduces the fundamentals of Model Context Protocol (MCP) through real-world, cross-language examples in .NET, Java, TypeScript, JavaScript, Rust and Python. Designed for developers, it focuses on practical techniques for building modular, scalable, and secure AI workflows from session setup to service orchestration.
SkillOpt is a text-space optimizer that trains reusable natural-language skills for frozen LLM agents through trajectory-driven edits, validation-gated updates, and deployable best_skill.md artifacts.
Skills, MCP servers, Custom Agents, Agents.md for SDKs to ground Coding Agents
Official agent plugin providing skills and MCP server configurations for Azure scenarios.
Use this skill to run a multi-persona expert advisory review on a labelled pull request in microsoft/apm. The panel fans out to five mandatory specialists plus a test-coverage specialist (active on every PR that touches src/) plus two conditional specialists (auth, doc-writer), all running in their own agent threads, and a CEO synthesizer. The orchestrator is the sole writer to the PR: ONE recommendation comment, no verdict labels, no merge gating. The panel is advisory -- it surfaces findings, prioritizes follow-ups, and renders a ship-recommendation that the maintainer and author weigh. Activate when a non-trivial PR needs a cross-cutting recommendation (architecture, CLI logging, DevX UX, supply-chain security, growth/positioning, optionally auth, docs, and test coverage, with CEO arbitration).
Use when your system manages multiple concurrent instances or sessions that each need isolated storage directories, per-instance file locking, or a prepare-once/create-many session factory pattern.
Use this skill to run a four-panel adversarial advisory review on any pull request that touches the OpenAPM specification artifact (docs/src/content/docs/specs/openapm-*.md), its inline / sidecar JSON Schemas (docs/src/content/docs/specs/schemas/*.schema.json), or the conformance fixture seed (tests/fixtures/spec-conformance/**). The panel fans out to four spec-ecosystem reviewers (swagger-openapi-editor, oci-distribution-editor, pkgmgr-registry-contract-editor, w3c-tag-architect), each running in its own agent thread, and a spec-editor synthesizer that produces a fold-now / defer-v0.1.1 / defer-v0.2 / reject list plus a ship decision keyed off a 1..10 shocked_meter scale. The orchestrator is the sole writer to the PR: ONE consolidated comment, no verdict labels, no merge gating. The panel is advisory -- it surfaces findings, prioritizes folds, and renders a ship recommendation that the maintainer weighs.
Use when the /systems-design mode is active. 9-phase structured design methodology -- problem framing, system classification, constraints, candidate architectures, tradeoff analysis, risk review, refinement, migration planning, and documentation. Governs conversation flow, delegation patterns, and user validation checkpoints.
Domain patterns for ML/AI serving and training systems — model serving, feature stores, training pipelines, experiment tracking, A/B testing, GPU scheduling, and failure modes. Use when designing or evaluating machine learning infrastructure, model serving platforms, or AI-powered product features.
Use when building a new CLI tool that needs one-line install via uv or npm, subcommand dispatch with a default action, or 3-tier config resolution (CLI flags, config file, hardcoded defaults).
Analyze images using LLM vision APIs (Anthropic Claude, OpenAI GPT-4, Google Gemini, Azure OpenAI). Use when tasks require: (1) Understanding image content, (2) Describing visual elements, (3) Answering questions about images, (4) Comparing images, (5) Extracting text from images (OCR). Provides ready-to-use scripts - no custom code needed for simple cases.
Diagnose issues in the current Amplifier session — misconfigured tools, failing operations, unexpected behavior. Use when something isn't working right.
Use this skill to drive any open microsoft/apm issue (bug, feature, docs, refactor, perf) from raw intake to a mergeable PR with triage as the central, paramount gate. Run the apm-triage-panel rubric per issue first, then present ONE consolidated triage review for the whole batch and escalate to the maintainer BY DEFAULT on any doubt (needs-design, decline, duplicate, defer, auto-handle, breaking- change, auth/security/governance surface, low arbiter confidence, unbounded scope, or a missing brief); only auto-implement clear, bounded, high-confidence accepts the maintainer approved. Then drive each accepted PR to mergeability batch-bug- shepherd style via the shepherd-driver loop: fold copilot + panel follow-ups by default, watch CI green, iterate under a bounded cap. Invoke MANUALLY, in-session, on an issue list or queue -- never by label or event. Activate when the maintainer asks to auto-tackle the issue queue, clear the backlog to PRs, or run issues to merge -- even if "autopilot" is not named.
Use when the /systems-design-review mode is active. 7-step design review methodology -- understand the design, classify the system, evaluate against codebase, adversarial analysis, tradeoff validation, synthesis, and action items. Governs conversation flow, delegation patterns, and user validation checkpoints.
Object-oriented design principles as a lens for system architecture — SOLID, composition over inheritance, the actor model, design patterns (and when they're wrong), encapsulation, polymorphism, and responsibility-driven design. Use when evaluating code organization, module boundaries, or object/component relationships.
Catalog of reusable architectural primitives — boundaries, contracts, state machines, queues, caches, consistency models, and more. For each: what it is, when it's right, when it's WRONG. Use when selecting patterns for a design or evaluating whether a pattern fits.
Domain patterns for Azure cloud architecture — compute selection, managed services, identity (Entra ID), networking, data platform, messaging, deployment, cost management, and operational patterns. Use when designing or evaluating a system deployed on Microsoft Azure.
Foundational patterns for distributed systems — consensus, consistency models, replication, partitioning, clock synchronization, distributed transactions, and failure modes. Use when designing or evaluating any system that spans multiple nodes, processes, or failure domains.
Domain patterns for event-driven and message-based systems — pub/sub, event sourcing, CQRS, sagas, delivery guarantees, schema evolution, and failure modes. Use when designing or evaluating systems built around events, messages, or asynchronous workflows.
Domain patterns for real-time and collaborative systems — persistent connections, state synchronization, conflict resolution, presence, fan-out, and failure modes. Use when designing or evaluating chat systems, collaborative editors, live dashboards, gaming backends, or any system with bidirectional real-time communication.
Domain patterns for workflow orchestration systems — long-running processes, state machines, saga coordination, human-in-the-loop gates, retry and timeout hierarchies, and failure modes. Use when designing or evaluating workflow engines, job orchestrators, or multi-step business process systems.
Guide for creating new Amplifier modules including protocol implementation, entry points, mount functions, and testing patterns. Use when creating new modules or understanding module architecture.
Use when your service needs authentication that works without friction locally but secures remote access, automatic TLS certificate setup, or token-based auth with auto-generation and localhost bypass.
Use when running tasks in Docker containers with safety limits, watchdog monitoring for resource enforcement, orphan container recovery, sidecar container provisioning, or scripting reproducible dev stack environments.
Use when building an HTTP service with FastAPI lifecycle management, background poll loops, SPA static file serving with API reverse proxy, bidirectional WebSocket relay, or SSE event streaming.
Use when designing a curl-piped install script for a project that cannot use uv tool install or npm publish — multi-service stacks (Docker Compose), raw TS/React apps, tools that bootstrap system dependencies, or installs for non-technical audiences. Documents the security trade-off, the community convention used by rustup, bun, deno, fly, ollama, and supabase, and the cases where this pattern is the wrong answer.
Use when adding a doctor diagnostic command, self-update/upgrade mechanism, cross-platform service installation (systemd and launchd), or post-upgrade verification to a CLI tool.
Authoritative consultant for all skills-related questions. Use when creating or modifying skills, understanding the Agent Skills spec, troubleshooting skill loading or invocation issues, leveraging enhanced format features (context fork, model_role, user-invocable), writing cross-harness portable skills, ensuring Claude Code Skills 2.0 compatibility, or deciding between skills vs agents.
Use when verifying that completed work actually works. Auto-surface during /verify mode, post-implementation review, or before claiming a task is done. Teaches the discipline of testing outcomes vs implementation, the unit/integration/smoke gradient, and what "done" actually means.
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.
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 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.
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.
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".
Adversarial review of a system design from 6 critical perspectives -- SRE, security, staff engineer, finance, operator, and developer advocate. Produces a unified risk assessment. Use for INTERACTIVE on-demand reviews during a design conversation (/adversarial-review). For RECIPE-DRIVEN reviews (where prior step context is needed), use the systems-design-critic agent instead.
Domain-Driven Design as a lens for system architecture — bounded contexts, aggregates, ubiquitous language, context mapping, domain events, and strategic vs tactical patterns. Use when modeling complex business domains, defining service boundaries, or evaluating whether a system's structure reflects its domain.
The Unix/Linux design philosophy as a lens for system design — mechanism vs policy, composability, small tools, text streams, convention over configuration, and the principle of least surprise. Use when evaluating designs for composability, simplicity, or separation of concerns.
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.
Domain patterns for data pipeline architecture — batch processing, stream processing, ETL/ELT, DAG scheduling, data quality, schema evolution, backfill strategies, and failure modes. Use when designing or evaluating data pipelines, ETL systems, or streaming data infrastructure.
Domain patterns for edge computing and offline-first systems — sync protocols, conflict resolution under partition, local-first architecture, constrained resources, intermittent connectivity, and failure modes. Use when designing or evaluating mobile apps, IoT systems, field-deployed software, or any system that must work without reliable network access.
Domain patterns for enterprise integration — legacy modernization, strangler fig, anti-corruption layers, API gateways, canonical data models, event-carried state transfer, and failure modes. Use when designing or evaluating integration between existing enterprise systems, legacy modernization, or multi-system orchestration.
Domain patterns for multi-tenant SaaS platforms — tenant isolation models, data partitioning, noisy neighbor mitigation, per-tenant configuration, billing metering, and failure modes. Use when designing or evaluating SaaS products that serve multiple customers from shared infrastructure.
Domain patterns for peer-to-peer system architecture — network topologies, NAT traversal, peer discovery, data distribution, consistency without central authority, identity and trust, and incentive mechanisms. Use when designing or evaluating a decentralized, peer-to-peer, or mesh-networked system.
Domain patterns for single-page application architecture — client-side routing, state management, rendering strategies, authentication, performance, and offline support. Use when designing or evaluating a browser-based SPA, progressive web app, or rich client-side application.
Domain patterns for web service architecture — API design (REST/GraphQL/gRPC), scaling, data layer, observability, failure modes, and anti-patterns. Use when designing or evaluating a web service, API, or request/response system.
Domain patterns for CLI tools and developer SDKs — command structure, configuration layering, plugin architecture, distribution, backward compatibility, shell integration, and failure modes. Use when designing or evaluating command-line tools, developer platforms, or SDK libraries.
Amplifier design philosophy using Linux kernel metaphor. Covers mechanism vs policy, module architecture, event-driven design, and kernel principles. Use when designing new modules or making architectural decisions.
Python coding standards for Amplifier including type hints, async patterns, error handling, and formatting. Use when writing Python code for Amplifier modules.
Adapt a skill written for another AI coding assistant (Claude Code, Cursor, etc.) into a properly structured Amplifier SKILL.md file. Reads the source skill, identifies platform-specific conventions, researches the source platform if needed, and produces an Amplifier-native skill conforming to the Agent Skills specification with Amplifier extensions. Use when the user wants to adapt a skill, port a skill, convert a skill to amplifier, translate a skill, or has a SKILL.md from another platform they want to bring into Amplifier.
Review changed code for reuse, quality, and efficiency, then fix any issues found.
Use when your tool needs persistent configuration files with safe defaults merging, atomic state writes that survive crashes, or conventional file locations for config vs state vs secrets.
Curmudgeonly engineering advisor that provides grounded skepticism, evidence-linked judgment, and constructive progress on architectural decisions, legacy refactors, tooling choices, and broad "how should I start?" questions. Sounds like a senior systems engineer who has reviewed too many designs to be impressed, but still cares about correctness. Use when: architectural decisions, legacy replacements, new tooling evaluation, broad planning questions.
Use when processes need to communicate via the filesystem without a message broker — JSONL event logs, atomic state snapshots, async request/response via file pairs, or SSE streaming from file tailing.
Structured tradeoff analysis methodology — the 8-dimension comparison frame, tradeoff matrix template, and common tradeoff patterns. Use when evaluating design alternatives, comparing technology choices, or when the answer is 'it depends.'
Research and plan a large-scale change, then execute it in parallel across isolated agents that each open a PR.
Use when making a system extensible with runtime plugin discovery via Python entry points, a file-based plugin registry, multi-backend provider abstractions, or schema-driven input validation.
Use when building a React frontend that dynamically loads independent bundles sharing a single React instance via import maps, needs frecency-based autocomplete, dynamic schema-driven forms, or Zustand state with localStorage.
Capture a repeatable process from the current session into a reusable Amplifier SKILL.md skill file. Analyzes the conversation, interviews the user to confirm structure, and writes a complete skill to disk. Use when the user wants to create a skill, save a workflow as a skill, turn a process into a reusable skill, or mentions "skillify", "create skill", "make a skill", "save as skill", "capture workflow", "turn this into a skill", "new skill", or wants to automate a repeatable process they just performed.
Activate for changes to project positioning, release communication, community-facing artifacts, or breaking-change decisions in microsoft/apm. Triggers on README, MANIFESTO, PRD, CHANGELOG, release workflows, and issue templates.