Scope creep in AI-generated diff
You asked an AI agent to "add input validation" to a Bash deployment script, and it returned a 60-line diff that — beyond adding validation — also refactored the argument parsing, renamed two variables, and added a `set -euo pipefail` at the top. It all looks fine and the script still runs. You're opening the PR. What's the responsible way to describe and scope this, and what's the risk in just titling it "add input validation" and moving on?
summarize_diff_scope(hunk_kinds: list[str], behavior_changing: list[bool], requested_kind: str) → list[str][["validation","validation","arg-parsing","rename","strict-mode"],[true,true,false,false,true],"validation"]out["behavior-change","arg-parsing","rename","strict-mode"][["validation"],[true],"validation"]out[][[],[],"validation"]out[]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 & agentic: describe the solution in plain language (or narrate it) and the coach grades your approach.