Pre-deployment validation checkpoint. Run deep checks to ensure your application is ready for Azure deployment. Validates configuration, infrastructure, permissions, and prerequisites. USE FOR: validate my app, check deployment readiness, run preflight checks, verify configuration, check if ready to deploy, validate azure.yaml, validate Bicep, test before deploying, troubleshoot deployment errors. DO NOT USE FOR: creating or building apps (use azure-prepare), executing deployments (use azure-deploy).

Azure Resource Manager SDK for Arize AI Observability and Evaluation (.NET). Use when managing Arize AI organizations on Azure via Azure Marketplace, creating/updating/deleting Arize resources, or integrating Arize ML observability into .NET applications. Triggers: "Arize AI", "ML observability", "ArizeAIObservabilityEval", "Arize organization".

Azure AI Content Understanding SDK for Python. Use for multimodal content extraction from documents, images, audio, and video. Triggers: "azure-ai-contentunderstanding", "ContentUnderstandingClient", "multimodal analysis", "document extraction", "video analysis", "audio transcription".

Azure Speech to Text REST API for short audio (Python). Use for simple speech recognition of audio files up to 60 seconds without the Speech SDK. Triggers: "speech to text REST", "short audio transcription", "speech recognition REST API", "STT REST", "recognize speech REST". DO NOT USE FOR: Long audio (>60 seconds), real-time streaming, batch transcription, custom speech models, speech translation. Use Speech SDK or Batch Transcription API instead.

Azure AI VoiceLive SDK for Java. Real-time bidirectional voice conversations with AI assistants using WebSocket. Triggers: "VoiceLiveClient java", "voice assistant java", "real-time voice java", "audio streaming java", "voice activity detection java".

Azure AI Transcription SDK for Python. Use for real-time and batch speech-to-text transcription with timestamps and diarization. Triggers: "transcription", "speech to text", "Azure AI Transcription", "TranscriptionClient".

Create custom agent capabilities when discovering novel tools, receiving task-agnostic tips from reviewers, or after researching specialized workflows not covered in existing instructions. Teaches structure, YAML optimization for LLM discoverability, and token efficiency.

Generate hierarchical AGENTS.md knowledge base for a codebase. Creates root + complexity-scored subdirectory documentation.

Score a repository's agentic legibility from repo-visible evidence only. Use when Codex needs to audit how easy a codebase is for coding agents to discover, bootstrap, validate, and navigate, especially for harness-engineering reviews, developer-experience audits, repo cleanup, or before/after comparisons after improving docs, tooling, or architectural constraints.

Instructions for rust async await module patterns.

Test locomotion system (slide, snap turn, teleport, jump) against the locomotion example using mcp-call.mjs WebSocket CLI.

Test level system (LevelRoot, LevelTag, default lighting, scene hierarchy) against the poke example using mcp-call.mjs WebSocket CLI.

Debug continuous behavior in WebXR scenes — physics, animations, collisions, game loops, or any real-time interaction that happens too fast for an agent to observe. Uses ECS pause/step/snapshot/diff to freeze time and inspect state frame by frame.

**USE THIS FOR COMPLEX TASKS.** Implements Manus-style file-based planning for multi-step tasks. Creates task_plan.md, findings.md, and progress.md in .agent_cache/<task-name>/. Use when: implementing multiple APIs, refactoring modules, research tasks, or ANY task requiring >5 tool calls.

Guide for searching and exploring external GitHub repositories using the gh CLI. Use this when you need reference implementations, patterns, or code examples from open-source projects to help complete your task.

Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).

Amazon Bedrock AgentCore Policy for defining agent boundaries using natural language and Cedar. Deterministic policy enforcement at the Gateway level. Use when setting agent guardrails, access control, tool permissions, or compliance rules.

Agentic Workflow Pattern

Upgrade any skill to v5 Hybrid format using decision theory + modal logic

Feature planning with 4 phases - Specify requirements, Design architecture, break into granular Tasks, Implement and Validate. Creates atomic tasks that agents can implement without errors. Triggers on "plan feature", "design", "new feature", "implement feature", "create spec".