review-docslisted

Review a front-door doc (default `README.md`; honor a path the user names) the way real readers would — not the way the author does. The author knows what every line means; a first-time reader does not. The job is to surface where a specific audience gets confused, under-served, or bounces, score it, and propose concrete fixes. The method is **fan-out**: spawn one subagent PER PERSONA, all in parallel, each doing a cold read as that reader. Different readers catch different things; running them concurrently is faster and keeps each read uncontaminated by the others. ## How to run it ### 1. Read the target doc yourself first Read the doc in full and skim the docs it links to (so "promises depth the linked doc doesn't deliver" is checkable). You need this to judge the personas' findings and to apply fixes later. ### 2. Fan out one subagent per persona — in parallel Spawn the personas below as subagents **in a single message** (multiple Agent/Task calls at once) so they run concurrently. Use a fast, cheap model (Sonnet or Haiku) — this is a reading/judgment task, not heavy synthesis — and say so in one line when you launch them. Give EACH subagent the same rubric, only the persona changes: > You are **\<PERSONA\>**. Read `<path>` (ignore any HTML comment block at the top — > that's internal authoring notes, not user-facing copy). Do a COLD read as this > reader: adopt their goals, vocabulary, and patience. Be a harsh grader — most > READMEs are a 3; 5/5 means you'd genui