The pipeline

One pass, ten stages, no loops. Each stage narrows the log; the last four are the only ones that ever see fewer lines than the job actually printed.

  1. 1Acquireproviders/

    Fetch the failed run and its job logs over the provider API, or read a saved log from disk.

    tuned by provider, gitlab, github

  2. 2Segmentproviders/*/segmenter.py

    Cut the raw log into named sections. Each CI system frames a job differently; this is the last place that difference is visible.

    tuned by —

  3. 3Map phasescore/phases.py

    Label each section with a phase: provision, prepare, fetch, script, post.

    tuned by phases

  4. 4Attributecore/attribution.py

    Decide which phase actually failed, from job metadata and log structure. Pure, deterministic, no model.

    tuned by — (deliberately)

  5. 5Denoisecore/denoise.py

    Strip ANSI colour, collapse progress bars, fold repeated lines, drop configured noise.

    tuned by denoise

  6. 6Extractcore/extract.py

    Run every matcher over the blamed section and keep the windows they anchor. Every gap is marked.

    tuned by extraction.matchers

  7. 7Budgetcore/budget.py

    Shed the lowest-priority windows, then trim what survives to the token budget.

    tuned by llm.max_input_tokens

  8. 8Explainllm/

    One optional model call, given only the budgeted evidence. Answers why, never where.

    tuned by llm

  9. 9Redactcore/redact.py

    Scrub secrets. Runs twice — on the prompt before it leaves the process, and on the finished report.

    tuned by redaction

  10. 10Renderrender/

    Terminal, report.md, report.json, and optionally one idempotent note on the MR/PR.

    tuned by output

Stage 4 is the one that matters. Attribution runs before any model is called and has no config at all, which is what makes the verdict reproducible. Everything after it only ever operates on the section it blamed.Sections & attribution →

Stages 5–7 are how a 40,000-line log becomes a few dozen lines worth reading —Evidence  covers those. To see the same ten stages as a dataflow graph, with the module boundaries drawn in, go back tothe Concepts overview .

You can watch the whole thing run with ci-doctor analyze -v: it logs the section count, the attribution verdict and rule, and adenoise → extract → fit line with the surviving line count at each step.