orchestrating-test-execution
Manages parallel test distribution, worker allocation, and result aggregation.
Install
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Activation
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Test coordinate parallel test execution across multiple environmentsKey capabilities
- →Coordinate parallel test execution
- →Manage test splitting across workers
- →Aggregate test results
- →Implement intelligent retry logic
- →Configure CI pipeline for parallel tests
- →Identify slowest tests for optimization
How it works
The skill analyzes test suites, classifies tests into execution tiers, configures parallel execution for each tier, and aggregates results into a single report.
Inputs & outputs
When to use orchestrating-test-execution
- →Parallelizing unit tests
- →Sharding end-to-end browser tests
- →Managing multi-environment test runs
- →Optimizing CI test performance
About this skill
Test Orchestrator
Overview
Coordinate parallel test execution across multiple test suites, frameworks, and environments. Manages test splitting, worker allocation, result aggregation, and intelligent retry strategies.
Prerequisites
- Test runner with parallel execution support (Jest, Vitest, pytest-xdist, Playwright, or JUnit 5)
- CI/CD platform configured (GitHub Actions, GitLab CI, CircleCI, or Jenkins)
- Test suite with consistent pass rates (flaky tests identified and tagged)
- Sufficient CI runner resources for parallel worker count
- Test result reporting tool (JUnit XML, Allure, or equivalent)
Instructions
- Analyze the existing test suite using Grep and Glob to catalog all test files, their framework, approximate run time, and dependency requirements.
- Classify tests into execution tiers:
- Tier 1 (Fast): Unit tests with no I/O -- target under 30 seconds total.
- Tier 2 (Medium): Integration tests requiring local services -- target under 3 minutes.
- Tier 3 (Slow): E2E and browser tests -- target under 10 minutes.
- Configure parallel execution for each tier:
- Split unit tests across N workers using
jest --shard=i/Norpytest -n auto. - Shard E2E tests by test file using Playwright
--shard=i/Nor Cypress parallelization. - Assign heavier integration tests to dedicated workers with more resources.
- Split unit tests across N workers using
- Create a CI pipeline configuration that runs tiers in parallel:
- Tier 1 and Tier 2 run concurrently on separate jobs.
- Tier 3 runs after a fast pre-check gate passes.
- Each tier reports results to a unified collection step.
- Implement intelligent retry logic for flaky tests:
- Tag known flaky tests with
@flakyor equivalent marker. - Retry failed tests up to 2 times before marking as failed.
- Track flaky test frequency in a log file for triage.
- Tag known flaky tests with
- Aggregate results from all parallel workers into a single report:
- Merge JUnit XML files from each shard.
- Calculate total pass/fail/skip counts and execution time.
- Identify the slowest tests for optimization targets.
- Write the orchestration configuration to the project's CI config file and validate it with a dry run.
Output
- CI pipeline configuration file (
.github/workflows/test.yml,.gitlab-ci.yml, or equivalent) - Test sharding configuration with worker count and split strategy
- Merged test result report in JUnit XML or JSON format
- Execution timeline showing parallel job durations and bottlenecks
- Flaky test inventory with retry counts and failure patterns
Error Handling
| Error | Cause | Solution |
|---|---|---|
| Shard produces zero tests | Uneven test distribution or incorrect shard index | Verify shard count matches actual test file count; use file-based splitting |
| Worker out of memory | Too many parallel processes on one runner | Reduce --maxWorkers or -n count; increase runner memory; use --workerIdleMemoryLimit |
| Test ordering dependency | Tests pass in isolation but fail in specific shard order | Add --randomize flag; fix shared state leaks; enforce test independence |
| Result aggregation mismatch | Missing shard results due to job timeout | Set job-level timeouts higher than test timeouts; add result upload as a separate step |
| CI cache miss slowing startup | Dependencies not cached between parallel jobs | Configure dependency caching per lockfile hash; use a shared setup job |
Examples
GitHub Actions matrix strategy for Jest sharding:
jobs:
test:
strategy:
matrix:
shard: [1, 2, 3, 4]
steps:
- run: npx jest --shard=${{ matrix.shard }}/4 --ci --reporters=jest-junit
- uses: actions/upload-artifact@v4
with:
name: results-${{ matrix.shard }}
path: junit.xml
merge:
needs: test
steps:
- uses: actions/download-artifact@v4
- run: npx junit-merge -d results-* -o merged-results.xml
pytest-xdist parallel execution:
pytest -n auto --dist worksteal -q --junitxml=results.xml
Playwright sharded execution:
npx playwright test --shard=1/3 --reporter=junit
Resources
- Jest sharding: https://jestjs.io/docs/cli#--shardshardindex-shardcount
- pytest-xdist: https://pytest-xdist.readthedocs.io/
- Playwright test sharding: https://playwright.dev/docs/test-sharding
- GitHub Actions matrix strategy: https://docs.github.com/en/actions/using-jobs/using-a-matrix-for-your-jobs
- JUnit XML merge tools:
Prerequisites
Limitations
- →Shard produces zero tests due to uneven distribution
- →Worker out of memory from too many parallel processes
- →Test ordering dependency causing failures
How it compares
This skill orchestrates parallel test execution across multiple frameworks and environments, managing test splitting and result aggregation, which differs from running tests sequentially or manually configuring individual test jobs.
Compared to similar skills
orchestrating-test-execution side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| orchestrating-test-execution (this skill) | 1 | 25d | Review | Advanced |
| e2e-testing-patterns | 8 | 2mo | No flags | Intermediate |
| testing-workflow | 16 | 9mo | Review | Intermediate |
| perf-lighthouse | 13 | 5mo | Review | Intermediate |
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