SuperAPI AI Directory

traffic-anomaly

Testing & quality

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Updated 10-16 05:54

About this skill

Detect unusual traffic movement

Use traffic-anomaly when a dashboard looks different and you need a repeatable check against recent history. It compares a recent finalized GSC window with an observed baseline for clicks and impressions. Statistical unusualness helps decide whether deeper investigation is warranted. It does not identify a cause, diagnose a site defect, or prove that an external event affected the property.

Run the report

With MCP, call seo_list_reports with {"category":"diagnosis"}. Call seo_describe_report with {"id":"traffic-anomaly"} before choosing windows. Then call seo_run_report, for example {"id":"traffic-anomaly","params":{"site":"sc-domain:example.com","days":90,"recentDays":7,"refresh":false}}.

CLI parity uses the same report definition:

seo reports describe traffic-anomaly --json
seo reports run traffic-anomaly --params '{"site":"sc-domain:example.com","days":90,"recentDays":7,"refresh":false}' --json

The discovery schema currently exposes broad numeric types for these window fields. Use sensible positive whole-day values even though all bounds are not expressed in JSON Schema.

Interpret the output

Read coverage first. Confirm the requested dates, expected and observed days, missing days, invalid rows, duplicate rows, and status. Partial coverage can change the baseline and must stay in the conclusion. rows is not a quality verdict.

Each entry in anomalies names the metric, baseline and comparison dates, means and totals, percentChange, zScore, significanceMethod, direction, and significant. A null percentage from a zero baseline is different from zero change. outside-flat-baseline handles movement where an observed baseline has no variance; it still describes only the supplied data.

Act safely

If movement is significant and coverage is adequate, run segment-impact for pages and queries, then check site changes, seasonality, indexing, and tracking. If it is normal, keep monitoring rather than forcing a story. Never label the result a penalty, algorithm update, recovery, or causal diagnosis without independent evidence.