Analyze CI history and code patterns to identify flaky tests.
npx skills add https://github.com/robit-man/transcribe-cli --skill flaky-detect-robit-man
Please help me install this Agent Skill. Skill: flaky-detect Source: https://github.com/robit-man/transcribe-cli/tree/main/.claude/skills/flaky-detect Command: npx skills add https://github.com/robit-man/transcribe-cli --skill flaky-detect-robit-man
This Skill identifies and helps to resolve flaky tests – those that pass and fail intermittently – which undermine CI/CD pipeline stability and developer productivity.
Analyze CI history for flaky test patterns over the last 30 days.
High-intent search queries and answers about installing and using this skill.
To identify flaky tests, you can analyze your CI history to detect inconsistent test results across multiple runs. This approach pinpoints tests that pass and fail intermittently, exposing specific tests causing pipeline instability and reducing developer productivity.
Intermittent test failures often stem from timing issues or environment dependencies. You can find the root cause by scanning your code for common anti-patterns that lead to flakiness, which helps address the underlying issues causing unreliable test results.
The best way to confirm if a test is flaky is by performing a systematic re-run analysis. Facilitating systematic re-running of tests validates whether the intermittent failures are genuine flakiness rather than consistent code defects.
Yes, you can use Python scripts for historical data parsing to detect test reliability issues. The scripts parse CI history, perform pattern matching against known anti-patterns, and aggregate results to highlight unreliable tests.
Yes, this approach to flaky test detection works without external dependencies. It operates autonomously using internal scripts and references to parse CI history and scan code for anti-patterns, requiring no additional packages.
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