datadog-java-test-optimization-onboarding

name	datadog-java-test-optimization-onboarding
description	Use when instrumenting a Bazel Java repository or monorepo with Datadog Test Optimization. Applies to Bzlmod and WORKSPACE consumers, direct java_test targets, repository-owned Java/JUnit wrapper macros, doctor/uploader validation, and RFC-safe setup that avoids manual tracer payload wiring, DD_GIT_* test environment variables, uploader credentials in test sandboxes, and missing remote outputs.

Datadog Java Test Optimization Onboarding

Use this skill when you need to instrument a Bazel Java repository with Datadog Test Optimization. The skill is intentionally project-neutral: it is stored in this repository as a Codex-compatible skill, but any agent can read it as a normal implementation guide.

Non-Negotiable Contract

Keep the RFC contract intact:

• Tests write JSON payloads to TEST_UNDECLARED_OUTPUTS_DIR.
• Bazel collects those files under bazel-testlogs/<target>/test.outputs/.
• The doctor validates local files after bazel test.
• The uploader runs after the doctor with bazel run.
• Do not add payload proxies or upload-from-test-sandbox paths.
• Do not manually set manifest or payload-in-files environment variables in consumer test rules; dd_topt_java_test owns that wiring.
• Do not pass DD_GIT_* through --test_env; use --repo_env for sync metadata.
• Do not pass uploader credentials or upload endpoints into the test sandbox.
• Use --remote_download_outputs=all when remote execution or remote cache can leave test outputs remote-only.

First Actions

1. Read the consumer repository's Bazel shape before editing:
   • Does it use MODULE.bazel, WORKSPACE, or both?
   • What command does the repository use for Bazel: bazel, bazelw, bzl, or a repo-local wrapper?
   • What is the Bazel repository name for rules_java?
   • What Java version and toolchain does Bazel use?
   • Which repository owns Java dependencies, Maven artifacts, and lockfiles?
   • Where is the dd-java-agent JAR already declared, or how should the consumer repository source it?
   • Does the repository already have a Java/JUnit test wrapper macro?
   • Which lightweight package should own the logical doctor/uploader pair (for example //tools/test_optimization)?
   • Does fetching this rules repository require SSH git or authenticated archive access?
   • Which runtime test targets should emit payloads?
   • Which build-only or analysis-only targets should not be expected to emit payloads?
   • Is FETCH_SALT absent from the normal test, doctor, and uploader flow?
2. Read this repository's current docs when details are needed:
   • README.md for quickstart and current command flow.
   • docs/Language_Onboarding.md for language-specific Java guidance.
   • docs/Installation_Reference.md for helper APIs and pinning.
   • docs/Uploader_Reference.md for doctor, dry-run, and upload behavior.
   • docs/Troubleshooting.md for failure diagnosis.
3. Pick the correct path:
   • Bzlmod repo: follow bzlmod-onboarding.md.
   • WORKSPACE repo: follow workspace-onboarding.md.
   • Validation and debugging: follow validation-checklist.md and troubleshooting.md.

Universal Shape

Every successful Java onboarding should end with these pieces:

• Repository or module resolution fetches Test Optimization metadata.
• The consumer repository owns rules_java, Java toolchains, test framework dependencies, and the dd-java-agent artifact.
• Java tests use dd_topt_java_test directly or through a repo-local wrapper.
• Existing repository wrapper policy stays in the consumer repository; the Datadog macro wraps the raw Java test rule or wrapper rule and injects the Java agent plus Test Optimization runtime files.
• Java onboarding explicitly sets stage_sources = True in the direct dd_topt_java_test call or in the repo-local wrapper's call into dd_topt_java_test, unless the repository intentionally opts out after accepting that source location tags may be missing.
• The workspace has exactly one logical doctor/uploader pair. In monorepos, place it in a lightweight package such as //tools/test_optimization; root labels are still fine for small repositories.
• .bazelrc or CLI commands provide sync metadata with --repo_env.
• Test commands use a named config such as --config=test-optimization.
• Remote-output-sensitive test configs include --remote_download_outputs=all.
• FETCH_SALT is used only for a separate, explicit bazel sync --only=<repo> --repo_env=FETCH_SALT="$(date +%s)" refresh, never as part of normal test, doctor, or uploader commands.
• Real upload happens only after tests, doctor, and dry-run enrichment pass.

Use the consumer's existing Bazel entrypoint in all commands. Do not switch a repository from bzl or bazelw to raw bazel just because examples use the generic binary name.

Branch And PR Hygiene

Before making changes in a real repository, confirm whether to use the current branch or create a new branch from the latest default branch. Keep onboarding changes reviewable:

• Put reusable rule changes in rules_test_optimization, not in a consumer repository workaround.
• Put consumer-specific scheduling, Docker, tag, flaky, and wrapper policy in the consumer repository.
• If an issue requires changing this rule repository, add matching fixture coverage in rules_test_optimization_tests before declaring it solved.

Stop Conditions

Stop and escalate instead of guessing when:

• The repository requires a new public rule behavior not covered by current docs.
• A target produces no JSON payloads after the Java test process ran.
• The doctor reports missing Git metadata after sync metadata was configured.
• The doctor reports missing Bazel metadata.
• The only available fix would manually set manifest or payload-in-files env vars in consumer tests.
• The only available fix would put DD_GIT_*, credentials, or upload endpoints into the test sandbox.
• The only tried doctor/uploader placement is the root package in a large monorepo and no lightweight package placement has been attempted.
• A private repository fetch returns 404 and SSH/authenticated archive mode has not been confirmed.
• Validation requires secrets that are not already available in the environment.