guided-experience-service-parallel-tests
A specialized runner for executing comprehensive test suites in parallel for the guided-experience-service repository.
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.zipInstalls 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.Key capabilities
- →Run both unit and integration test suites in parallel
- →Execute unit tests with 10 pytest workers
- →Execute integration tests with 10 pytest workers
- →Install Splunk app dependencies before integration suite
- →Review failures and suspicious errors using technical analysis tools
- →Report raw test outcomes separately for unit and integration suites
How it works
The skill orchestrates parallel execution of unit and integration test suites using `uv run pytest` with specified worker counts. It includes a prerequisite step for installing Splunk app dependencies and a post-execution analysis phase for reviewing failures.
Inputs & outputs
When to use guided-experience-service-parallel-tests
- →Run parallel unit and integration tests
- →Analyze CI failure root causes
- →Validate repository test coverage
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-servicerepository - the user has not authorized subagents and broad parallelization is the main reason to invoke this skill
First Read
- Read
../../../../guided-experience-service/AGENTS.mdbefore running commands. - Run commands from the repository root:
../../../../guided-experience-service. - Use
uvfor Python commands and scripts. - If the user provides a local artifact such as
task_<issue>.md,review_mr_<MR>.md, oranalysis_mr_<MR>.md, read it first and reuse its repo context, assumptions, validation plan, and open questions before running broad test suites.
Workflow
- Start in
../../../../guided-experience-service. - Ensure dependencies are installed with
uv syncwhen needed. - If subagents are explicitly authorized, use 2 parallel workers:
- Worker 1 owns unit test execution only.
- Worker 2 owns integration test execution only.
- Run the unit suite with 10 pytest workers:
uv run pytest -v -m "not integration and not functional" -n 10
- Just before the integration suite, run:
make install-splunk-app-deps
- Run the integration suite with 10 pytest workers:
uv run pytest -v -m "integration and not skip_ci" -n 10
- Before running the integration suite, fail fast if
IAC_TOKENis not set. - Wait for both suites to finish.
- If the integration suite depends on production Weaviate behavior, source
cicd/scripts/set_weaviate_config.shbefore running the relevant commands. - If failures suggest extra local infrastructure is needed, use the repo Makefile helpers such as
make test-db-startandmake test-db-stop. - After both suites finish, use
repository-technical-analysistogether withguided-experience-service-technical-analysisto review any failing tests, suspicious errors, environment gaps, or likely root causes. - Report the raw test outcomes first, then the follow-up technical analysis.
- When rerunning similar broad test execution, preserve durable learned sections such as
Frequent Failure Clusters,Known Prerequisite Gaps,Fastest Reliable Suite Order, andFlaky or Low-Signal Checkswhen 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 10commands above because the currentMakefiletest 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-startandmake test-db-stopfor database lifecycle instead of ad hoc docker commands. - Run
make install-splunk-app-depsimmediately before the integration suite. - For integration execution, check
IAC_TOKENfirst and stop with a clear error if it is missing. - After the unit and integration runs complete, use
repository-technical-analysiswithguided-experience-service-technical-analysisto 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 ChecksorKnown Prerequisite Gapswith 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_TOKENbefore 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:
- State the exact commands run.
- Separate unit and integration outcomes.
- List failing tests or modules clearly.
- Call out missing environment or auth prerequisites explicitly.
- 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.mdwhen 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-analysisfor failure investigation after test executionguided-experience-service-technical-analysisfor repo-specific failure analysismulti-spawn-agentonly 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 Checkswhen 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.mdfor repo workflow rules../../../../guided-experience-service/Makefilefor test and database helper targets../../../../guided-experience-service/pyproject.tomlfor pytest markers and dev dependencies../../../../guided-experience-service/cicd/scripts/set_weaviate_config.shfor production Weaviate settings
When not to use it
- →When only a narrow targeted test command is needed
- →When the task is outside the `guided-experience-service` repository
- →When subagents are not authorized and broad parallelization is the main reason to invoke this skill
Limitations
- →The skill is specific to the `guided-experience-service` repository.
- →The skill prefers 2 parallel workers when subagents are explicitly authorized.
- →The skill requires `IAC_TOKEN` to be set for the integration suite.
How it compares
This skill provides a specific workflow for parallel execution of unit and integration tests within the `guided-experience-service` repository, including pre-test setup and post-test analysis, unlike general test execution commands.
Compared to similar skills
guided-experience-service-parallel-tests side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| guided-experience-service-parallel-tests (this skill) | 0 | 3mo | Review | Intermediate |
| code-change-verification | 4 | 5mo | Review | Beginner |
| discovering-make-commands | 3 | 5mo | No flags | Beginner |
| dev | 2 | 6mo | Review | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
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