Building AI skills like checklists

Every weekday morning, someone on the AI team at incident.io kicks off our spend report skill. Claude pulls a dozen BigQuery queries, computes a 7-day baseline range for the KPIs we track, hunts for anomalies (runaway processors, mis-tagged spend, onboarding spikes) and posts a structured report into our #ai-costs-pulse channel. The skill exists because AI inference costs are growing fast and don’t reduce neatly to a single graph. Spend by feature, by prompt, by org, by initial-turn vs reprocessing, by internal vs customer — there are too many dimensions to dashboard sensibly, and even with the right charts the more useful question is usually why a number moved rather than what the number was. The skill produces the kind of analysis someone on the team would have written by hand if they had a spare half-hour each morning. I built it on a Wednesday in April, and I want to walk through how, because I think the process matters more than the artefact you end up with. Why first drafts fail…