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Research Components

After architecture planning, research each selected component to gather best practices before generating artifacts.

Process

  1. Identify Components — List all Azure services from architecture plan
  2. Load Service References — For each service, load services/<service>/README.md first, then specific references as needed
  3. Check Resource Naming Rules — For each resource type, check resource naming rules for valid characters, length limits, and uniqueness scopes
  4. Load Recipe References — Load the selected recipe's guide (e.g., AZD) and its IAC rules, MCP best practices, and schema tools listed in its "Before Generation" table
  5. Check Region Availability — Verify all selected services are available in the target region per region-availability.md
  6. Load Runtime References — For containerized apps, load language-specific production settings (e.g., Node.js)
  7. Invoke Related Skills — For deeper guidance, invoke mapped skills from the table below
  8. Document Findings — Record key insights in .azure/plan.md

Service-to-Reference Mapping

Azure Service Reference Related Skills
Hosting
Container Apps Container Apps azure-diagnostics, azure-observability, azure-nodejs-production
App Service App Service azure-diagnostics, azure-observability, azure-nodejs-production
Azure Functions Functions —
Static Web Apps Static Web Apps —
AKS AKS azure-networking, azure-security-hardening
Data
Azure SQL SQL Database azure-security
Cosmos DB Cosmos DB azure-security
PostgreSQL — —
Storage (Blob/Files) Storage azure-storage, azure-security-hardening
Messaging
Service Bus Service Bus —
Event Grid Event Grid —
Event Hubs — —
Integration
API Management APIM azure-aigateway (invoke for AI Gateway policies)
Logic Apps Logic Apps —
Security & Identity
Key Vault Key Vault azure-security, azure-keyvault-expiration-audit
Managed Identity — azure-security, entra-app-registration
Observability
Application Insights App Insights appinsights-instrumentation (invoke for instrumentation)
Log Analytics — azure-observability, azure-kusto
AI Services
Azure OpenAI Foundry microsoft-foundry (invoke for AI patterns and model guidance)
AI Search — azure-ai (invoke for search configuration)

Research Instructions

Step 1: Load Internal References (Progressive Loading)

For each selected service, load the README.md first, then load specific files as needed:

Selected: Container Apps, Cosmos DB, Key Vault

→ Load: services/container-apps/README.md (overview)
  → If need Bicep: services/container-apps/bicep.md
  → If need scaling: services/container-apps/scaling.md
  → If need health probes: services/container-apps/health-probes.md

→ Load: services/cosmos-db/README.md (overview)
  → If need partitioning: services/cosmos-db/partitioning.md
  → If need SDK: services/cosmos-db/sdk.md

→ Load: services/key-vault/README.md (overview)
  → If need SDK: services/key-vault/sdk.md

Step 2: Invoke Related Skills (When Deeper Guidance Needed)

Invoke related skills for specialized scenarios:

Scenario Action
Using GitHub Copilot SDK Invoke azure-hosted-copilot-sdk (scaffold + config, then resume azure-prepare)
Using Azure Functions Stay in azure-prepare — load selection.md → Follow composition.md algorithm
PostgreSQL with passwordless auth Handle directly without a separate skill
Need detailed security hardening azure-security-hardening
Setting up App Insights instrumentation appinsights-instrumentation
Building AI applications microsoft-foundry
Cost-sensitive deployment azure-cost-optimization

Skill/Reference Invocation Pattern:

For Azure Functions:

  1. Load: selection.md (decision tree)
  2. Follow: composition.md (algorithm)
  3. Result: Base template + recipe composition (never synthesize IaC)

For PostgreSQL:

  1. Handle passwordless auth patterns directly without a separate skill

Step 3: Document in Plan

Add research findings to .azure/plan.md under a ## Research Summary section with source references and key insights per component.

Common Research Patterns

Web Application + API + Database

  1. Load: services/container-apps/README.md → bicep.md, scaling.md
  2. Load: services/cosmos-db/README.md → partitioning.md
  3. Load: services/key-vault/README.md
  4. Invoke: azure-observability (monitoring setup)
  5. Invoke: azure-security-hardening (security baseline)

Serverless Event-Driven

  1. Load: services/functions/README.md (contains mandatory composition workflow)
  2. Load: services/event-grid/README.md or services/service-bus/README.md (if using messaging)
  3. Load: services/storage/README.md (if using queues/blobs)
  4. Invoke: azure-observability (distributed tracing)

AI Application

  1. Invoke: microsoft-foundry (AI patterns and best practices)
  2. Load: services/container-apps/README.md → bicep.md
  3. Load: services/cosmos-db/README.md → partitioning.md (vector storage)
  4. Invoke: azure-security (API key management)

GitHub Copilot SDK Application

  1. Invoke: azure-hosted-copilot-sdk skill (scaffold, infra, model config)
  2. After it completes, resume azure-prepare workflow (validate → deploy)

After Research

Proceed to Generate Artifacts step with research findings applied.