compare-test-case
Skill35 starsChanged 8 months agoMITView on GitHub
Compares a single test case's behavior across two branches, analyzing pass/fail status, duration, flakiness, and failure details. Useful for investigating test regressions introduced by a feature branch.
2 other projects use a version of this.
Adopted by
Steady: 0 new projects took it in the last 4 weeks, none the 4 weeks before.thin data
Most recently taken up by
- tuist/agent-pluginword for word, first seen Nov 8, 2026
- tuist/tuistword for word, first seen Oct 8, 2026
Other versions2 projects use a version of this.
Word for word · 2
- tuist/tuist★ 5.9k100% same
- tuist/agent-plugin★ 0100% same
Edited · 0
None found.
Wrote their own version · 0
None found.
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository11 other files it gives its agents.
The file
We split this skill into the rules it gives an agent
11 rules. About 0 are common, 8 are distinctive, and 3 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈8
Distinctive · estimate
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 12
Run tuist test case show <id-or-identifier> --json to get the test case metrics.
- Very specific: line 13
Run tuist test case run list <identifier> --json to see runs across branches.
- Distinctive: line 64
If no base branch is provided, use the project's default branch (usually main).
- Distinctive: line 65
If no head branch is provided, detect the current git branch.
- Distinctive: line 78
Newly flaky: No flaky runs on base, flaky runs on head
- Distinctive: line 104
Check commits between base and head branches for changes to the test file or the code under test.
- Distinctive: line 105
Look at the failure message for clues about what changed.
- Very specific: line 109
Check if repetitions show intermittent pass/fail patterns.
- Distinctive: line 113
Check if setup/teardown time increased.
- Distinctive: line 114
Check if the test is doing more work (new assertions, larger data sets).
- Distinctive: line 115
Check if a dependency became slower.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Compare Test Case
Quick Start
You'll typically receive a test case identifier and two branches. Follow these steps:
- Run
tuist test case show <id-or-identifier> --jsonto get the test case metrics. - Run
tuist test case run list <identifier> --jsonto see runs across branches. - Compare behavior between base and head branches.
- Inspect failures with
tuist test case run show <run-id> --json. - Summarize findings with root cause analysis.
Step 1: Resolve the Test Case
By ID or dashboard URL
tuist test case show <test-case-id> --jsonBy identifier (Module/Suite/TestCase)
tuist test case show Module/Suite/TestCase --jsonIf no test case is provided
Discover flaky or failing tests to investigate:
tuist test case list --flaky --json --page-size 10Key fields from the response:
id-- unique identifier for subsequent commandsname,module_name,suite_name-- the test identityreliability_rate-- percentage of successful runsflakiness_rate-- percentage of flaky runs in the last 30 daystotal_runs/failed_runs-- volume contextis_flaky/is_quarantined-- current flags
Step 2: Get Runs on Each Branch
List test case runs filtered by the test case, and look at the git_branch field:
tuist test case run list <identifier> --json --page-size 20Separate runs by branch. For each branch, compute:
- Pass rate:
passed_runs / total_runs * 100 - Average duration
- Flaky run count
- Most recent status
Defaults
- If no base branch is provided, use the project's default branch (usually
main). - If no head branch is provided, detect the current git branch.
Step 3: Compare Branch Behavior
| Metric | Base branch | Head branch | Verdict |
| Pass rate | e.g. 100% | e.g. 60% | REGRESSION |
| Avg duration | e.g. 0.5s | e.g. 2.1s | REGRESSION |
| Flaky runs | 0 | 3 | NEW FLAKINESS |
| Last status | success | failure | REGRESSION |
Classify the change:
- Newly failing: 100% pass rate on base, <100% on head
- Newly flaky: No flaky runs on base, flaky runs on head
- Duration regression: >50% increase in average duration
- Fixed: Failing on base, passing on head
- Stable: Same behavior on both branches
Step 4: Inspect Failures
For each failing run on the head branch:
tuist test case run show <test-case-run-id> --jsonExamine:
failures[].message-- the assertion or error messagefailures[].path-- source file pathfailures[].line_number-- exact line of failurefailures[].issue_type-- type of issuerepetitions-- shows retry behavior (e.g., pass-fail-pass means flaky)crash_report-- crash data if the test runner crashed
Step 5: Identify Root Cause
Based on the comparison:
Newly failing
- Check commits between base and head branches for changes to the test file or the code under test.
- Look at the failure message for clues about what changed.
Newly flaky
- Common patterns: timing/async issues, shared state, environment dependencies.
- Check if
repetitionsshow intermittent pass/fail patterns. - See the fix-flaky-tests skill for detailed flaky test analysis patterns.
Duration regression
- Check if setup/teardown time increased.
- Check if the test is doing more work (new assertions, larger data sets).
- Check if a dependency became slower.
Summary Format
Produce a summary with:
- Test case info: Name, module, suite, overall reliability.
- Base branch behavior: Pass rate, avg duration, flaky count.
- Head branch behavior: Pass rate, avg duration, flaky count.
- Verdict: What changed and classification.
- Root cause: Hypothesis based on failure analysis.
- Recommendations: Specific file paths, line numbers, and fix suggestions.
Example:
Test Case Comparison: AuthModuleTests/LoginTests/test_login_with_expired_token
Overall reliability: 85% (was 100% before head branch)
Base (main):
Pass rate: 100% (15/15 runs)
Avg duration: 0.3s
Flaky: No
Head (feature/auth-refactor):
Pass rate: 60% (3/5 runs)
Avg duration: 0.5s
Flaky: Yes (2 flaky runs)
Verdict: NEWLY FLAKY -- test was stable on main but intermittently fails on feature branch
Root cause: The auth refactor introduced an async token refresh that races with the
test's synchronous assertion. Failures show "Expected status 401, got nil" at
Tests/AuthModuleTests/LoginTests.swift:42, suggesting the response arrives before
the token refresh completes.
Recommendations:
- Add an await/expectation before the assertion at LoginTests.swift:42
- Consider mocking the token refresh to make the test deterministicDone Checklist
- Resolved the test case identity
- Gathered runs on both branches
- Compared pass rates, durations, and flakiness
- Inspected failure details for failing runs
- Identified root cause with file paths and line numbers
- Provided actionable fix recommendations
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

