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Analyser un journal CI

Collez un journal CI en échec et obtenez la cause, plus un résumé à coller à votre agent. Gratuit, sans compte, rien n’est envoyé.

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Comment analyser un journal CI

  1. Paste the log

    Drop in a failed run from GitHub Actions, GitLab, tsc, gcc, rustc, pytest, vitest or jest, go test, maven or gradle. Or open a .log file, or pick a real sample.

  2. Autopsy

    A deterministic rule engine strips timestamps, progress bars, and URL noise, then clusters the remaining error signals by type and weight. No LLM, no guessing.

  3. Fix or hand off

    Read the ranked root-cause lines with file and line numbers, or copy the agent-ready summary straight into your coding agent.

Fonctions

  • Built for 4,000-line logs

    Job and step tracking, noise classification, and signature clustering turn a wall of text into a ranked shortlist of the lines that actually explain the failure.

  • Deterministic, not AI

    The same log always produces the same analysis. Rules are regex and table driven across the major CI ecosystems — no model calls, no drift.

  • Agent-ready summary

    One copy gives you a markdown summary with verdict, stats, flaky signals, and the top root-cause lines, formatted to paste back into a coding agent.

  • Honest when it doesn't know

    If nothing matches, it says so and shows the most frequent error-ish words — it never invents a diagnosis for a log it can't parse.

Questions fréquentes

Which CI systems and tools does it understand?

GitHub Actions and GitLab job structure and annotations, plus the error formats of tsc, gcc, rustc, pytest, vitest, jest, go test, maven, gradle, npm, pip, cargo, and more. Unknown formats fall through to an honest no-match card.

Is my log uploaded anywhere?

No. The analysis runs entirely in this browser tab, and the site's content security policy blocks outbound requests as a guarantee. Close the tab and the log is gone.

How big a log can it handle?

Up to 60 MB dropped as a file, though runs over about 20 MB get slow in the browser. For huge logs, trim to the failing region first — that is also faster to read.

What is the agent-ready summary?

A compact markdown block with the verdict, signal counts, flaky flags, and the top root-cause lines. Paste it to Claude Code, Codex, Cursor, or any coding agent as instant context.

Can it detect flaky tests?

Yes. It recognizes failed-then-passed runs, exhausted retries, and explicit flaky markers, and flags them separately from hard failures.

Is this free?

Yes — like every kitwisp tool: no sign-up, no limits on runs, no watermark on the output.