ENGINEERING
On-Demand Flake Classifier via PR Comment
Triggered by a slash-command comment on a pull request, this agent pulls the failing job's logs.
How it runs
The automated pipeline, trigger to output.
- TriggerPR comment '/classify-flake'GitHub
- ActionFetch failing logs and run historyGitHub
- ActionClassify against flake patternsOpenAI
- LogicDecide flaky / real / inconclusive
- OutputReply on PR with verdict and suggestionGitHub
What it does
This workflow gives engineers an instant second opinion on a red check. When someone comments a command like /classify-flake on a PR, the agent fetches the failing job logs, matches them against known flake signatures (timeouts, race conditions, network blips) and the test's recent history, then replies in-thread with a confidence-scored verdict and, if flaky, a ready-to-apply quarantine suggestion.
When to use it
Use this when developers need a fast, evidence-based call on whether a failure is worth investigating or just noise, without leaving the pull request.
How it works
- 1A GitHub issue_comment containing the slash command triggers the flow.
- 2An action retrieves the failing check's logs and the test's recent run history.
- 3An agent classifies the failure against flake patterns and assigns a confidence score.
- 4A logic step decides flaky, real, or inconclusive.
- 5The output replies on the PR with the verdict and a suggested quarantine action.
Set it up
What you configure once, before turning it on.
- 1Connect GitHubRepos, issues, pull requests, actions.
- 2Connect OpenAIModels, embeddings, files.
- 3Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 4Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 5Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run it inside a business
This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.

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