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AGENTS.md: deadline-cloud-samples

This file gives AI coding assistants the context they need to work effectively in this repository.

What this repo is

deadline-cloud-samples is a public collection of samples for AWS Deadline Cloud. It is not a single buildable package; each sample is self-contained. The repository does have one top-level, standard-library-only static validation command:

python3 scripts/validate_repository.py

Run it after every repository change, along with tests or validation owned by the sample you edit. External Markdown links use a separate network-dependent command:

python3 scripts/check_external_links.py

Use --no-ignore to audit the narrowly documented domain ignore list before changing it. Real broken links must be fixed rather than ignored.

Find samples

Start with the task paths and repository map in README.md, then use the nearest area or collection README. Its index table is complete for the scope it declares. Search tracked paths or contents directly when you need implementation or support files that are intentionally excluded from user-selectable sample tables.

Where things live

deadline-cloud-samples/
├── cloudformation/             CloudFormation farm and infrastructure templates
├── terraform/                  Terraform farm and infrastructure templates
├── cdk/                        AWS CDK farm apps (TypeScript)
├── job_bundles/                OpenJD job bundles (template.yaml plus assets)
├── conda_recipes/              DCC and application Conda recipes
├── containers/                 Worker-compatible and application containers
├── queue_environments/         Session software environments (Conda, Rez, and pip)
├── host_configuration_scripts/ Privileged service-managed fleet setup scripts
├── submission_hooks/           Pre-submission Deadline Cloud CLI hooks
├── utility_scripts/            Standalone workflow helpers
├── skills/                     Task-specific guides for coding agents
├── docs/                       Contributor guidance and documentation starting points
└── scripts/                    Standard-library repository validation

Read the relevant sample README.md before modifying its files. Use docs/SAMPLE_README_TEMPLATE.md as an adaptable starting point when adding a nontrivial sample.

Before implementing a sample, inspect skills/ for a matching SKILL.md guide.

Repository conventions

  • Use inclusive language: prefer primary/replica and allowlist/denylist.
  • Use pip install ... in cross-platform Python instructions; avoid pip3 unless the sample is explicitly Linux/macOS-only.
  • Job bundles live under job_bundles/<name>/ with a template.yaml, optional parameter_values.yaml, and a README.md for nontrivial samples.
  • Conda recipes live under conda_recipes/<package>-<version>/ with a recipe/ directory and deadline-cloud.yaml.
  • For OpenJD templates, run openjd check and openjd run --tasks <one> to verify a representative task locally before submitting the full parameter range when possible.
  • Add, rename, move, or delete a sample in the nearest category table. Change root navigation only when a recommended path changes.
  • Keep indexes in Markdown. Do not add catalog or metadata-generation machinery.
  • Do not add third-party runtime dependencies to repository validation.

Pre-PR checklist

  • Run python3 scripts/validate_repository.py successfully (unit tests and static local-link checks).
  • Run python3 scripts/check_external_links.py successfully when Markdown links change.
  • Run the affected sample's own relevant tests or static checks.
  • Update the sample README when behavior, prerequisites, parameters, outputs, or risks change.
  • Update the nearest category table when a sample is added, renamed, moved, or deleted.
  • Update root task paths only when a recommended starting point changes.
  • Use a conventional commit title.
  • Sign off every commit under the Developer Certificate of Origin.
  • Check changed content for inclusive language.

See CONTRIBUTING.md for the full contribution and licensing workflow.

External references