ENGINEERING
Track README example health trends in BigQuery
Daily, runs every documented code example, records the pass/fail result per snippet as a timestamped row in BigQuery.
How it runs
The automated pipeline, trigger to output.
- TriggerDaily schedule fires
- ActionInstall current version, run README code blocksShell
- ActionAssemble per-snippet results with version and errorsShell
- ActionAppend timestamped rows to BigQuery history tableBigQuery
- LogicCompute pass rate, compare to threshold
- OutputPage on-call when pass rate drops below thresholdPagerDuty
What it does
This workflow turns example validation into a measurable health metric over time. Each day it runs every README code block, then writes one timestamped row per snippet to a BigQuery table capturing whether it passed, the package version tested, and the error if it failed. The accumulated history lets you chart documentation health and spot regressions introduced by specific releases.
When to use it
Use it when you want long-running visibility into documentation quality, not just a one-off alert, and you report on docs health in a dashboard. Best for teams that already centralize engineering metrics in BigQuery.
How it works
- 1A daily schedule triggers the run.
- 2A shell step installs the current version and runs every README code block.
- 3A shell step assembles per-snippet results: pass or fail, version, and any error text.
- 4A BigQuery step appends one timestamped row per snippet to the history table.
- 5A logic step computes today's overall pass rate and compares it to the threshold.
- 6An output step pages on-call through PagerDuty when the pass rate falls below the threshold.
Set it up
What you configure once, before turning it on.
- 1Connect ShellRun sandboxed commands inside the workspace.
- 2Connect BigQueryDatasets, queries, schemas.
- 3Connect PagerDutyIncidents, on-call, escalations.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run this workflow in your colony.
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