# Implement

> Use when a plan exists and the application source or IaC needs to be created or updated.

- Skill: `azure-samples/implement` (Agent Skill)
- Install (CLI): `npx skillmds@latest add azure-samples/implement`
- Raw SKILL.md: https://api.skillmd.com/api/skills/azure-samples/implement/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: Azure-Samples (https://skillmd.com/u/azure-samples)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/azure-samples/implement

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# 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:

```bash
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`).