"Shortform Hook Doctor is an AI assistant specialized in evaluating and prescribing high-retention video hooks, visual cues, and audio directions for TikToks, Reels, and Shorts."
Your hook decides in 3 seconds. We make those 3 seconds count.
Predict second-by-second viewer retention for the first six seconds of a short-form video hook, then provide concrete opening fixes.
Turn openings that get scrolled past in 3 seconds into hooks that stop the scroll. Pin down the content's real payoff and the scrolling context the audience meets it in; diagnose the old opening against five common hook-killers; then mass-produce 10 variants from an eight-pattern hook matrix (curiosity gap, counterintuitive, specific number, and more), adapt them per platform, and finish with a payoff check and an A/B plan.
You already wrote a hook — spoken line and/or on-screen text. Paste it with your platform (TikTok / Reels / Shorts) and get a 0-10 score on 4 things that decide the first 3 seconds: clarity, curiosity gap, specificity, and pace. Each score gets a one-line reason, plus the single biggest fix and one rewritten version. This only grades a hook you already have — it doesn't generate new hook ideas or a script. For 5+ hooks and a full script from a topic, that's a different agent (Hook Lab).
Your video is good, but viewers swipe before the second sentence — the first three seconds lost them. Give Hook Lab a topic, trend, product, or niche and get back what actually decides retention: a clear positioning angle, trend-informed topic ideas, 5+ scroll-stopping hook options (spoken line + on-screen text, by hook type), a tight beat-by-beat script, and a caption. Built for creators, founders, marketers, and social teams shipping short-form for TikTok, Reels, and Shorts.
Paste your weak hook or video topic — get 5 rebuilt hook variations with diagnosis, director notes, text overlay suggestions, and retention predictions. Stop losing viewers in the first 3 seconds.
Paste each new Shorts topic and get a feed-ready title, a 3-second hook, and marked retention beats at 15s and 30s so the script keeps paying off. It never claims YouTube Analytics or view-count data.
Hands-on prompt engineering and LLM evaluation. Built around a diagnose-then-ship loop: forensic transcript critique ▎ that names the exact failure mode, paired with a fixer that produces complete, deployment-ready rewrites — not ▎ vague advice.