Question
You're directing an AI agent to write a Python ingestion function that parses incoming timestamp strings from multiple third-party feeds (some with offsets, some without, some in 'YYYY-MM-DD HH:MM:SS' local form) into a single canonical instant for an analytics warehouse. Write the spec that makes parsing and normalization unambiguous and auditable. What rules for offset-naive inputs, canonical storage, and edge cases do you mandate? Then describe what a naive prompt ('parse the timestamps and store them') gets wrong.
Treat the AI’s output as a draft to verify, not an answer to trust. Name the specific flaw and the input that triggers it, say how you’d catch it — tests, edge cases, reading critically — and how you’d re-prompt or decompose to get it right.
Vibe coding: describe the solution in plain language (or narrate it) and the coach grades your approach. Generating runnable code from your description is coming next.