### Test Flakiness
Works with
description: "Detect non-deterministic (flaky) tests by reading CI run logs or test result history. Aggregates pass rates per test, identifies intermittent failures, recommends quarantine or fix, and
argument-hint: "[ci-log-path | scan | registry]"
allowed-tools: Read, Glob, Grep, Write, Edit, Bash
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiontest-flakinessExecute the skills CLI command in your project's root directory to begin installation:
Fetches test-flakiness from Donchitos/Claude-Code-Game-Studios and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate test-flakiness. Access via /test-flakiness in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
0
total installs
0
this week
10.7K
GitHub stars
0
upvotes
Run in your terminal
0
installs
0
this week
10.7K
stars
| name | test-flakiness |
| description | "Detect non-deterministic (flaky) tests by reading CI run logs or test result history. Aggregates pass rates per test, identifies intermittent failures, recommends quarantine or fix, and maintains a flaky test registry. Best run during Polish phase or after multiple CI runs." |
| argument-hint | "[ci-log-path | scan | registry]" |
| user-invocable | true |
| allowed-tools | Read, Glob, Grep, Write, Edit, Bash |
A flaky test is one that sometimes passes and sometimes fails without any code change. Flaky tests are worse than no tests in some ways — they train the team to ignore red CI runs, masking genuine failures. This skill identifies them, explains likely causes, and recommends whether to quarantine or fix each one.
Output: Updated tests/regression-suite.md quarantine section + optional
production/qa/flakiness-report-[date].md
When to run:
/regression-suite identifies quarantined tests that need diagnosisModes:
/test-flakiness [ci-log-path] — analyse a specific CI run log file/test-flakiness scan — scan all available CI logs in .github/ or
standard log output directories/test-flakiness registry — read existing regression-suite.md quarantine
section and provide remediation guidance for already-known flaky testsscan if CI logs are accessible, else
registryCheck for test result artifacts:
ls -t .github/ 2>/dev/null
ls -t test-results/ 2>/dev/null
For Godot projects: GdUnit4 outputs XML results compatible with JUnit format.
Check test-results/ for .xml files.
For Unity projects: game-ci test runner outputs NUnit XML to test-results/
by default.
For Unreal projects: automation logs go to Saved/Logs/. Grep for
Result: Success and Result: Fail patterns.
If a path argument is provided, read that file directly.
If no logs found:
"No CI log data found. To detect flaky tests, this skill needs test result history from multiple runs. Options:
- Run the test suite at least 3 times and collect the output logs
- Check CI pipeline output and save a log to
test-results/- Run
/test-flakiness registryto review tests already flagged as flaky intests/regression-suite.md"
Stop and ask the user which option to pursue.
For each CI log or result file found, parse:
JUnit XML format (GdUnit4 / Unity):
<testcase name= to get test names<failure or <error to identify failuresclassname and name attributes for full test identifiersPlain text logs:
PASSED / FAILED adjacent to test namesResult: Success / Result: FailTest passed / Test failedBuild a table: test_id → [run1_result, run2_result, run3_result, ...]
A test is flaky if it appears in the result history with both PASS and FAIL outcomes across runs with no code changes between them.
Flakiness thresholds:
For each flaky test, classify the likely cause:
| Cause | Symptoms | Fix direction |
|---|---|---|
| Timing / async | Fails after awaiting signals or timers; pass rate correlates with system load | Add explicit await/synchronisation; avoid time-based delays |
| Order dependency | Fails when run after specific other tests; passes in isolation | Add proper setup/teardown; ensure test isolation |
| Random seed | Fails intermittently with no pattern; involves RNG | Pass explicit seed; don't use randf() in tests |
| Resource leak | Fails more often later in a test run | Fix cleanup in teardown; check orphan nodes (Godot) or object disposal (Unity) |
| External state | Fails when a file, scene, or global exists from a prior test | Isolate test from file system; use in-memory mocks |
| Floating point | Fails on comparisons like == 0.5 | Use epsilon comparison (is_equal_approx, Assert.AreApproximately) |
| Scene/prefab load race | Fails when scenes are not yet ready | Await one frame after instantiation; use await get_tree().process_frame |
Use Grep to check the test file for timing calls, randf, global state access, or equality comparisons on floats to narrow down the cause.
For each flaky test:
Quarantine (High flakiness):
"Quarantine this test immediately. Disable it in CI by adding
@pytest.mark.skip/[Ignore]/GdUnitSkipannotation. Log it intests/regression-suite.mdquarantine section. The test is now opt-in only. Fix the root cause before removing quarantine."
Investigate and fix soon (Moderate):
"This test is intermittently unreliable. Root cause appears to be [cause]. Suggested fix: [specific fix based on cause classification]. Do not quarantine yet — fix the test directly."
Monitor (Low/suspected):
"This test shows suspected flakiness. Collect more run data before quarantining. Note it as 'suspected' in the regression suite."
## Flakiness Detection Results
**Runs analysed**: [N]
**Tests tracked**: [N]
### Flaky Tests Found
| Test | System | Fail Rate | Likely Cause | Recommendation |
|------|--------|-----------|--------------|----------------|
| [test_name] | [system] | [N]% | Timing | Quarantine + fix async |
| [test_name] | [system] | [N]% | Float comparison | Fix: use epsilon compare |
| [test_name] | [system] | [N]% | Order dependency | Investigate teardown |
### Clean Tests (no flakiness detected)
[N] tests ran across [N] runs with consistent results — no flakiness detected.
### Data Limitations
[Note if fewer than 5 runs were available — fewer runs = less statistical confidence]
Ask: "May I update the quarantine section of tests/regression-suite.md
with the flaky tests found?"
If yes: use Edit to append entries to the Quarantined Tests table.
Never remove existing quarantine entries — only add new ones.
Ask (separately): "May I write a full flakiness report to
production/qa/flakiness-report-[date].md?"
The full report includes per-test analysis with cause details and engine-specific fix snippets.
After writing:
is_equal_approx."Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
JuliusBrussee/caveman
JuliusBrussee/caveman
whyashthakker/agent-skills-marketing
JuliusBrussee/caveman
whyashthakker/agent-skills-marketing
vercel-labs/skills
I recommend test-flakiness for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: test-flakiness is the kind of skill you can hand to a new teammate without a long onboarding doc.
test-flakiness reduced setup friction for our internal harness; good balance of opinion and flexibility.
Useful defaults in test-flakiness — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for test-flakiness matched our evaluation — installs cleanly and behaves as described in the markdown.
test-flakiness fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for test-flakiness matched our evaluation — installs cleanly and behaves as described in the markdown.
test-flakiness has been reliable in day-to-day use. Documentation quality is above average for community skills.
We added test-flakiness from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
test-flakiness fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
showing 1-10 of 58