Conversational agent that guides developers through creating complete Azure PowerShell cmdlet design specifications. Walks through an interactive interview covering service release details, contacts, scenarios, cmdlet syntax, parameter sets, piping, test cases, and spec links. Validates against PowerShell design guidelines, pre-populates fields from TypeSpec, and files the design as a GitHub Issue in Azure/azure-powershell-cmdlet-review-pr. USE FOR: 'create PowerShell design', 'PS design review', 'cmdlet design', 'PowerShell cmdlet review', 'submit PS design'. DO NOT USE FOR: SDK generation, TypeSpec authoring, releasing packages.

Turn a mission or lab idea into a fully researched GitHub issue spec on microsoft/agent-academy, then post it with the GitHub CLI. Use this skill whenever the user wants to propose new Agent Academy content before writing it — course missions (Recruit, Operative, Commander), standalone Special Ops, or Cowork Collective missions. Trigger whenever the user says "write a spec for a mission", "create an issue for a new lab", "I want to propose a Special Ops", "file a mission proposal", "spec out a lab", "open an issue for a new module", or hands over Microsoft Learn URLs and asks to turn them into an Agent Academy lab. This skill handles: mining source documentation, interviewing for scope, repo reconnaissance and duplicate detection across both docs and existing issues, frontmatter and tag validation, writing-style conformance, drafting the issue body, title conventions, milestone and assignee metadata, and posting plus verifying the issue via `gh`.

Apply a prompt-agnostic execution and verification loop to any substantial task while preserving the original request. Use when the user asks to fan out work, use subagents or independent reviewers, loop until done, benchmark against references, apply a harsh critic, compare candidates blind, improve an existing prompt with verification, or continue until explicit quality gates pass.

Route a "how do I use Codewhale" question to the installed help, config, and doctor surfaces instead of reciting a manual from memory. Explicit-only.

Help a new contributor get productive on this checkout - inspect sync state against main, build, run the repository's exact verification gate, and produce a local what's-new digest. Never fetches, pulls, or modifies a dirty tree on its own. Explicit-only.

Load when modernizing, migrating, or upgrading C++ code from older standards (C++98/03/11/14) to modern C++ (17/20/23), or when applying systematic code improvements that preserve behavior.

Load when debugging crashes, memory errors, data races, undefined behavior, or performance issues in C++ code, or when performing a systematic safety audit.

Load when reviewing, refactoring, modernizing, debugging, or designing non-trivial C++ code where ownership, lifetime, RAII, concurrency, templates, interfaces, exceptions, or Core Guidelines compliance may affect correctness, safety, maintainability, or performance.

Load when reviewing, refactoring, modernizing, debugging, or designing non-trivial C++ code where ownership, lifetime, RAII, concurrency, templates, interfaces, exceptions, or Core Guidelines compliance may affect correctness, safety, maintainability, or performance.

Builds generative AI applications on Amazon Bedrock. Covers model invocation (Converse API, InvokeModel), RAG with Knowledge Bases, Bedrock Agents, Guardrails, and AgentCore (including the Harness managed agent loop). Use when invoking models, setting up Knowledge Bases, creating agents, applying guardrails, deploying to AgentCore, migrating/porting/converting a Bedrock Agent (including inline agents) to an AgentCore Harness, troubleshooting Bedrock errors (ThrottlingException, AccessDeniedException), or choosing models (Claude, Llama, Nova, Titan). ALSO USE for prompt caching setup and debugging, quota health checks and throttling diagnosis, cost attribution and tracking, migrating between Claude model generations (4.5 to 4.6 to 4.7), chunking strategies, API selection (Converse vs InvokeModel), guardrail capabilities, and model selection. Also covers AgentCore Payments setup (x402, microtransactions, Payment Manager, Connector, Instrument, Coinbase CDP, Stripe Privy, 402 Payment Required, pay for content, paid endpoint, agent payments). NOT for custom model training, Rekognition, or Comprehend.

Deploys and operates containerized workloads on ECS, Fargate, and ECR. Covers task definitions, Fargate services, ECR repository setup and lifecycle policies, ECS Exec debugging, service scaling, deployment strategies, load balancer integration, and logging configuration. Use when deploying, debugging, or optimizing containers on AWS. ALSO USE for container deployment options (ECS vs ECS Express Mode), networking modes, health check troubleshooting, OOM errors, secrets injection, blue/green deployments, ECR image management, and App Runner sunset guidance and migration. NOT for Kubernetes, EKS, or CI/CD pipelines.

Builds generative AI applications on Amazon Bedrock. Covers model invocation (Converse API, InvokeModel), RAG with Knowledge Bases, Bedrock Agents, Guardrails, and AgentCore. Use when invoking models, setting up Knowledge Bases, creating agents, applying guardrails, deploying to AgentCore, migrating/porting/converting a Bedrock Agent (including inline agents) to an AgentCore Harness, troubleshooting Bedrock errors (ThrottlingException, AccessDeniedException), or choosing models (Claude, Llama, Nova, Titan). ALSO USE for prompt caching setup and debugging, quota health checks and throttling diagnosis, cost attribution and tracking, migrating between Claude model generations (4.5 to 4.6 to 4.7), chunking strategies, API selection (Converse vs InvokeModel), guardrail capabilities, and model selection. Also covers AgentCore Payments setup (x402, microtransactions, Payment Manager, Connector, Instrument, Coinbase CDP, Stripe Privy, 402 Payment Required, pay for content, paid endpoint, agent payments). NOT for custom model training, Rekognition, or Comprehend.

Use when adding capabilities to an existing agent project — memory, app integration, VPC, multi-agent, migration, model changes, browser, code interpreter, or resource removal. Triggers on: "add memory", "remember across sessions", "call agent from app", "invoke agent from code", "auth to call agent", "streaming responses", "VPC", "VPC connectivity", "VPC error", "can't reach from VPC", "multi-agent", "A2A", "A2A auth", "orchestrator not delegating", "specialist not called", "migrate Bedrock Agent", "after import", "migration issue", "framework for migration", "change model", "browser tool", "code interpreter", "delete agent", "tear down", "agentcore remove", "cross-account memory", "resource-based policy on memory", "pay for x402 content", "402 Payment Required", "microtransactions", "paid API or tool". Not for connecting to external APIs via Gateway — use agents-connect. Not for scaffolding a new project — use agents-get-started. Not for CLI/dev server errors — use agents-debug. Strands vs LangGraph in a migration context routes here.

Dealing with delayed DOM generation, lazy loading, and optimistic state synchronization in vanilla JavaScript without a reactive framework.

Project N.E.K.O. 胶囊化 UI、品牌蓝视觉系统规范

**重要提醒**: 每次完成复杂调试或开发任务后，主动执行此流程！ 将学到的经验归档为 skill，供以后参考。不要等用户提醒。

Use to list Liferay worktrees with their assigned ports, running status, offset, and database, or to print the port and database summary for a single worktree.

Generate a targeted local test plan for branch changes. Use when the user wants to know which tests to run before merging, asks for a test plan, wants to validate changes locally, or mentions running tests for their branch. This skill analyzes commits on top of master and produces a runnable shell script covering Unit, Integration, Playwright, and Poshi tests within a 20-minute local budget.

Resolve a single Liferay test failure end-to-end.

Create a GitHub PR for the current branch. Use when the user asks to create a PR, send a PR, or invokes /pr.