Implement

Implement Skill

Requires spec.md. If spec.md is missing, auto-run specify with the user's prompt and then auto-run plan. If plan.md is missing, auto-run plan. Load workspace context per copilot-instructions.md.

Scaffold (if ./infra/ is missing)

In an OS temp dir (not the workspace), run one of:

azd init -t <AZD template> -e <AZD environment> -s <Azure Subscription Id> -l <Azure Region>
azd init --minimal -e <AZD environment> -s <Azure Subscription Id> -l <Azure Region>

Pick the form recorded in ./.azure/deployment-plan.md. Treat non-zero exit as failure only if azure.yaml or (non-minimal) infra/ are missing.

Move azure.yaml and infra/ into the workspace root. Ask before overwriting; Delete the temp dir.

Persist environment variables

./.azure/deployment-plan.md must have no [NEEDS CLARIFICATION: …] markers. Run azd env set for AZURE_RESOURCE_GROUP first, then for every other variable in the deployment plan.

Execute

Implement per ./docs/plan.md, keeping it updated with progress. On resume, re-read ./docs/plan.md and continue from the next unchecked step.

Accelerate with parallelism. Keep shared contracts (APIs, env vars, azure.yaml service names) consistent across agents, and serialize steps with true dependencies (scaffold and env-var persistence before service code).

Create or update ./docs/implementation.md to capture the architecture that was actually built and the key implementation details (components, data flow, key decisions, deviations from plan.md). Include one or more animated Mermaid flowchart diagrams to visualize the runtime architecture and request/data flow.

Pause if: a task is unclear, implementation reveals a spec/plan gap, or any error/blocker is hit — report and wait, do not guess.

Post-implementation checks

Check the following and fix any gaps before claiming implementation is complete and ready for verify/deploy:

• Check that AZD_AGENT_SKIP_ACR="false" in the azd env variables if any container images are built for Azure Container Apps or Azure Kubernetes Service.
• Check if ./azure.yaml has service configuration (host: azure.ai.agent) for each implemented agent.
• Check if ./azure.yaml has service configuration for each MCP server.
• Check if ./azure.yaml has service configuration for the frontend.
• Check if ./azure.yaml has service configuration for the backend.
• Check if ./azure.yaml has services configured with cloud build (remoteBuild: true) set for container images. Use docker if the user prefers local build and it's running; otherwise, use ACR cloud build.
• Check if CORS is configured between frontend and backend for both local and cloud environment (./infra).
• Check that any backend calling a Foundry hosted agent targets the agent's dedicated endpoint. Obtain it by running azd ai agent show <agent-name> and reading the Endpoint protocol and URL, and authenticate with a DefaultAzureCredential bearer token (scope https://ai.azure.com/.default). Make sure to use the protocol supported by the agent (invocations, responses, etc.).
• Check that an ACR exists for any container image build: AI-starter azd templates often don't create one. If absent, have ./infra provision its own registry; container apps need it even though hosted agents use Foundry's remote build.

Report

Summarize the updated ./docs/implementation.md (architecture, key decisions, and any deviations from plan.md), report the result of the post-implementation checks, and prompt the user to continue with verify (local tests against provisioned Azure resources) or skip straight to deploy (provision and deploy to Azure with azd up).