PipelineSpend
GitHub Actions benchmark methodology

GitHub Actions benchmarks without fake sample size or freshness

PipelineSpend can measure operational GitHub Actions evidence, but no public aggregate industry benchmark is published from customer-derived data until privacy, representativeness, and evidence-quality gates pass.

Read-only GitHub Audit No Audit-time CI mutation Unknown ≠ $0 Verified only from observed outcomes

Metrics that can be defined from observed CI evidence

A benchmark must start with a precise source contract. Candidate operational metrics include jobs per run, completed job duration, observed queue wait where timestamps support it, retry incidence, runner-label distribution, workflow-event distribution, job fan-out, history coverage, and evidence quality.

A metric name must not imply stronger semantics than its source fields support. Missing timestamps or bounded history remain null/unknown rather than being filled with synthetic values.

Why there are no customer-derived cohort pages yet

Training eligibility is not public publication authority. Before an aggregate benchmark can become indexable, PipelineSpend requires a separate publication policy, privacy filtering, deterministic cohort classification, minimum independent organization/repository counts, contribution-dominance limits, and enough materially distinct metrics to make the page useful.

If those thresholds are not met, the correct publication state is blocked—not a thin page populated with generated filler.

Freshness means material change

Recomputing the same metrics tomorrow should not make a page look newly updated. Future benchmark snapshots separate computation time from the last material content change, and sitemap freshness should change only when public data actually changes.

Need repository-specific evidence now?

Use the read-only Audit for your own authorized repository instead of waiting for generalized benchmark cohorts that have not yet earned publication eligibility.

Audit a GitHub repository