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guided-experience-service-parallel-tests

Use this when working in the guided-experience-service repository and the user wants to run all unit tests and integration tests in parallel. Prefer 2 parallel workers when subagents are explicitly authorized: one worker for the unit suite and one worker for the integration suite, each using 10 pyte

Install

mkdir -p .claude/skills/guided-experience-service-parallel-tests && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/16924" && unzip -o skill.zip -d .claude/skills/guided-experience-service-parallel-tests && rm skill.zip

Installs to .claude/skills/guided-experience-service-parallel-tests

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Use this when working in the guided-experience-service repository and the user wants to run all unit tests and integration tests in parallel. Prefer 2 parallel workers when subagents are explicitly authorized: one worker for the unit suite and one worker for the integration suite, each using 10 pytest workers. Run make install-splunk-app-deps immediately before the integration suite. After the parallel test runs complete, use repository-technical-analysis together with guided-experience-service-technical-analysis to review failures, suspicious errors, missing prerequisites, or likely root causes. Covers the repo-specific uv and pytest commands, when to prefer direct pytest over Makefile targets, optional Weaviate setup, and how to report results separately for unit and integration suites.
799 chars✓ has a “when” triggerlonger than Claude Code's old 250-char listing cap (fine on current versions)

About this skill

Guided Experience Service Parallel Tests

Use this skill for broad test execution in ../../../../guided-experience-service when the goal is to run both the unit and integration suites in parallel. Prefer 2 parallel workers when subagents are available and explicitly authorized by the user, then review any failures or suspicious errors with repository-technical-analysis plus guided-experience-service-technical-analysis.

When to Use

Use this skill when the user wants:

  • both unit and integration suites run for guided-experience-service
  • broad parallel validation in this repository
  • follow-up failure analysis after broad test execution

When Not to Use

Do not use this skill when:

  • the task only needs a narrow targeted test command
  • the task is outside the guided-experience-service repository
  • the user has not authorized subagents and broad parallelization is the main reason to invoke this skill

First Read

  • Read ../../../../guided-experience-service/AGENTS.md before running commands.
  • Run commands from the repository root: ../../../../guided-experience-service.
  • Use uv for Python commands and scripts.
  • If the user provides a local artifact such as task_<issue>.md, review_mr_<MR>.md, or analysis_mr_<MR>.md, read it first and reuse its repo context, assumptions, validation plan, and open questions before running broad test suites.

Workflow

  1. Start in ../../../../guided-experience-service.
  2. Ensure dependencies are installed with uv sync when needed.
  3. If subagents are explicitly authorized, use 2 parallel workers:
    • Worker 1 owns unit test execution only.
    • Worker 2 owns integration test execution only.
  4. Run the unit suite with 10 pytest workers:
uv run pytest -v -m "not integration and not functional" -n 10
  1. Just before the integration suite, run:
make install-splunk-app-deps
  1. Run the integration suite with 10 pytest workers:
uv run pytest -v -m "integration and not skip_ci" -n 10
  1. Before running the integration suite, fail fast if IAC_TOKEN is not set.
  2. Wait for both suites to finish.
  3. If the integration suite depends on production Weaviate behavior, source cicd/scripts/set_weaviate_config.sh before running the relevant commands.
  4. If failures suggest extra local infrastructure is needed, use the repo Makefile helpers such as make test-db-start and make test-db-stop.
  5. After both suites finish, use repository-technical-analysis together with guided-experience-service-technical-analysis to review any failing tests, suspicious errors, environment gaps, or likely root causes.
  6. Report the raw test outcomes first, then the follow-up technical analysis.
  7. When rerunning similar broad test execution, preserve durable learned sections such as Frequent Failure Clusters, Known Prerequisite Gaps, Fastest Reliable Suite Order, and Flaky or Low-Signal Checks when they still match current test output and environment state.

Parallel Worker Template

When subagents are allowed, use a 2-worker split like this:

Spawn 2 parallel worker agents with fork_context: true.

Worker 1:
- own unit test execution only
- run from ../../../../guided-experience-service
- run: uv run pytest -v -m "not integration and not functional" -n 10
- return: summary, failing tests, and validation run

Worker 2:
- own integration test execution only
- run from ../../../../guided-experience-service
- fail immediately with a clear error if IAC_TOKEN is unset
- run: make install-splunk-app-deps
- run: uv run pytest -v -m "integration and not skip_ci" -n 10
- source cicd/scripts/set_weaviate_config.sh first when needed
- return: summary, failing tests, and validation run

After both workers finish:
- use repository-technical-analysis plus guided-experience-service-technical-analysis to review failures or suspicious errors
- report raw test results separately from the follow-up analysis

Do not wait immediately after spawning. Wait after both workers have started or when results are needed.

Command Selection Rules

  • Prefer the direct uv run pytest ... -n 10 commands above because the current Makefile test targets do not expose worker count.
  • Prefer 2 parallel workers over a single sequential run when subagents are explicitly authorized.
  • Prefer repo targets such as make test-db-start and make test-db-stop for database lifecycle instead of ad hoc docker commands.
  • Run make install-splunk-app-deps immediately before the integration suite.
  • For integration execution, check IAC_TOKEN first and stop with a clear error if it is missing.
  • After the unit and integration runs complete, use repository-technical-analysis with guided-experience-service-technical-analysis to analyze failures, suspicious stack traces, environment problems, and likely root causes.
  • Do not run functional tests unless the user asks for them; this skill is for unit and integration suites.
  • When a suite, prerequisite step, or helper command proves flaky or low-signal, record it once in Flaky or Low-Signal Checks or Known Prerequisite Gaps with the shortest useful explanation.

Validation

  • Run the exact unit and integration commands documented by this skill unless the repo workflow has changed.
  • Fail fast on missing prerequisites such as IAC_TOKEN before expensive integration execution.
  • Report raw suite outcomes separately from follow-up technical analysis.
  • Prefer repo-native helpers for environment lifecycle when they exist.

Reporting

When summarizing results:

  1. State the exact commands run.
  2. Separate unit and integration outcomes.
  3. List failing tests or modules clearly.
  4. Call out missing environment or auth prerequisites explicitly.
  5. Add a separate technical-analysis section that summarizes likely root causes, failure groupings, and next debugging steps.

Outputs / Artifacts

This skill should provide:

  • unit suite outcome
  • integration suite outcome
  • exact commands run
  • missing prerequisite or environment notes
  • follow-up technical analysis when failures occur

Artifact-Aware Behavior

When a local workflow artifact is provided:

  • read it first for scope, validation intent, and known risks
  • reuse its links, assumptions, and open questions when reporting failures
  • still treat current test output as the source of truth for pass/fail state
  • preserve the shared core sections from ../ARTIFACTS.md when updating the same artifact or reporting back into it

This is additive only and does not replace the existing unit/integration test workflow.

Companion Skills

Common pairings:

  • repository-technical-analysis for failure investigation after test execution
  • guided-experience-service-technical-analysis for repo-specific failure analysis
  • multi-spawn-agent only when subagents are explicitly authorized

Self-Improving Behavior

When rerunning broad unit and integration execution for the same repository state or failure family:

  • read any existing task or analysis artifact first
  • preserve durable learned sections such as ## Frequent Failure Clusters, ## Known Prerequisite Gaps, ## Fastest Reliable Suite Order, and ## Flaky or Low-Signal Checks when they still match current output and environment state
  • refresh conclusions against the live unit output, integration output, dependency state, and environment checks before reusing them
  • promote repeated confirmed observations into short operational heuristics, preferably phrased like when suite X fails with Y, check Z first
  • demote, mark stale, or remove heuristics contradicted by new output or environment evidence

This keeps broad test-run artifacts useful across reruns without replacing the normal execution and follow-up analysis workflow.

Safety Notes

  • Do not run functional tests unless the user asks for them.
  • Do not treat missing prerequisites as flaky failures; report them explicitly.
  • Keep unit and integration results separated in both execution and reporting.

Useful Repo Anchors

  • ../../../../guided-experience-service/AGENTS.md for repo workflow rules
  • ../../../../guided-experience-service/Makefile for test and database helper targets
  • ../../../../guided-experience-service/pyproject.toml for pytest markers and dev dependencies
  • ../../../../guided-experience-service/cicd/scripts/set_weaviate_config.sh for production Weaviate settings

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