ArgoCD Application Manifest Prompt Template

This prompt is designed for GitOps and platform engineers who need to generate valid, deployable ArgoCD Application manifests from a natural language description of a service. It accelerates the authoring of Kubernetes resources by translating a developer's or operator's intent—such as a target source repository, path, cluster destination, and sync preferences—into a complete YAML resource that conforms to the ArgoCD Application CRD schema. The primary job-to-be-done is reducing the manual toil of writing repetitive manifests, preventing copy-paste errors across similar services, and embedding manifest generation directly into an internal developer platform or CLI tool.

Use this prompt when the source of truth for a deployment is a raw manifest that needs to be created or updated frequently, such as when onboarding new microservices or managing a suite of nearly identical applications. It is particularly effective in workflows where a developer describes a service in a ticket or chat interface, and the AI produces a pull request containing the new Application YAML. The prompt expects a structured description of the deployment's source, destination, and sync policy, and it returns a manifest that can be validated against the ArgoCD CRD schema using tools like kubeconform or kubectl apply --dry-run=server. However, do not use this prompt when the deployment logic is already defined in a Helm chart, Kustomize overlay, or other templating tool that should be referenced directly by the Application manifest. In those cases, the prompt would generate an unnecessary abstraction layer that drifts from the established source of truth.

Avoid this prompt for complex multi-step deployment workflows that require canary analysis, blue-green promotion, or manual approval gates. Those scenarios are better served by Argo Rollouts, Argo Workflows, or a CI/CD pipeline orchestrator, where the Application manifest is just one small component of a larger state machine. Similarly, if your organization enforces strict GitOps separation of duties where only a limited set of platform admins can create Application resources, using an AI-generated manifest without a human approval step would violate that policy. In such high-risk environments, the generated YAML should always pass through a pull request review by a platform engineer before being applied to the cluster.

This prompt template is designed to generate a single, valid ArgoCD Application manifest in YAML. It is structured to be used directly in an AI harness or a chat interface. The template uses square-bracket placeholders for all variable inputs, such as the application name, source repository, and sync policy. The core instruction is to output only the YAML document, with no surrounding commentary, markdown fences, or explanatory text. This strict output constraint is critical for integrating the generated manifest directly into a GitOps pipeline or a kubectl apply command without manual cleanup.

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Generate an ArgoCD Application manifest in valid YAML. The output must conform to the argoproj.io/v1alpha1 Application schema. Do not include any markdown fences, commentary, or extra text outside the YAML document. Use the following inputs to populate the manifest.

[APP_NAME]
[PROJECT_NAME]
[SOURCE_REPO_URL]
[SOURCE_TARGET_REVISION]
[SOURCE_PATH]
[DESTINATION_SERVER]
[DESTINATION_NAMESPACE]
[SYNC_POLICY_AUTOMATED_ENABLED]
[SYNC_POLICY_PRUNE_ENABLED]
[SYNC_POLICY_SELF_HEAL_ENABLED]
[SYNC_OPTIONS]
[IGNORE_DIFFERENCES]

To adapt this template for your own use, replace each bracketed placeholder with a concrete value. For boolean fields like [SYNC_POLICY_PRUNE_ENABLED], use true or false. For list fields like [SYNC_OPTIONS], provide a valid YAML list (e.g., - CreateNamespace=true). If a section like [IGNORE_DIFFERENCES] is not needed, you can remove the entire placeholder line or replace it with an empty YAML mapping ([]). The critical rule is to maintain the strict output format: the model's entire response must be the YAML document. Before deploying the generated manifest, always validate it against the ArgoCD CRD schema using kubectl apply --dry-run=client -f - or a dedicated schema validation tool to catch any structural errors.

The ArgoCD Application Manifest prompt is designed to be called from a CI/CD pipeline, an internal developer platform, or a ChatOps interface. The prompt expects structured input describing the desired application, source repository, target cluster, and sync preferences. Before calling the model, assemble the [INPUT] from user-provided fields or a service catalog lookup. Always inject the [OUTPUT_SCHEMA] as the ArgoCD Application CRD schema (v1alpha1 or v2) to constrain the model to valid fields. If your organization enforces sync wave ordering, include the [CONSTRAINTS] placeholder with a list of existing Applications in the same AppProject to prevent wave collisions.

After the model returns the YAML manifest, run it through a strict validation harness before it touches any cluster. Use argocd app create --dry-run or kubeconform against the ArgoCD Application schema. Check for missing required fields (spec.source.repoURL, spec.destination.server), invalid sync policy combinations (e.g., automated.prune: true without automated.selfHeal), and namespace mismatches. If validation fails, do not retry blindly. Log the raw model output, the validator error, and the input context to an audit table. Then construct a repair prompt that includes the original input, the invalid YAML, and the specific validation error, asking the model to fix only the failing fields. Limit repair attempts to two retries before escalating to a human reviewer via a PR or ticket.

For production use, prefer a model with strong YAML and schema-following performance. Gate the generated manifest behind a pull request workflow: commit the proposed Application YAML to a GitOps repository, run argocd app diff in a CI check, and require approval from a platform engineer before merging. This gives you an audit trail, prevents direct cluster mutation, and allows reviewers to catch subtle errors like overly broad resource inclusions or missing health checks. Avoid wiring the prompt directly to argocd app create without a human-in-the-loop step for any cluster that runs production workloads.