Skill detail

ad-hook-analyzer

Score and force-rank the openings of candidate video ads - from a script, transcript, storyboard, shot list, or a description of a finished cut - to decide which hooks deserve test budget, returning a ranked shortlist within the batch rather than an absolute performance prediction. Use whenever the…

Why we recommend it: Scores and ranks video ad hooks; adjacent to copywriting, ends at ranking.

MatchPossibleRecommended for Copywriting
Sourcembfinotti/​advertising-skillsExternal source
Reported installs6,069Popularity signal only

Inspect before use

Read the source instructions and check the requirements before you install this skill.

Saved source preview

SKILL.md

The saved excerpt is a snapshot from when this skill was listed. The external source remains the complete and most current version.

---
name: ad-hook-analyzer
description: "Score and force-rank the openings of candidate video ads - from a script, transcript, storyboard, shot list, or a description of a finished cut - to decide which hooks deserve test budget, returning a ranked shortlist within the batch rather than an absolute performance prediction. Use whenever the user mentions a hook, the first 3 seconds, an ad opening, hook rate, thumbstop, attention scoring, or asks which hook to test or whether a hook works - even if they never say 'hook analyzer'. Covers B2B and B2C on any platform. Ends at the ranking: writing the scripts themselves is mbfinotti/advertising-skills@ugc-ad-scripts and static copy is mbfinotti/advertising-skills@ad-copy-variants."
license: MIT
metadata:
  author: Maya-Beth Finotti
  version: "1.3.2"
---

# Hook Analyzer

You are a performance-creative analyst. Read a batch of candidate video-ad openings and decide
which ones are worth spending test budget on. The output is a relative ranking within one batch

- a shortlisting device.

The market is the judge; this skill only decides which candidates get to face it.

The method is built on honesty about what a pre-launch hook score can and cannot do:

- "Hook rate" is a practitioner-invented derived ratio, not a native platform metric, and its
  denominator differs across dashboards.
- One agency's analysis of the same 11 brands (drawn from 3,859 ads) found hook rate correlated
  **-0.19 with ROAS** and hold rate -0.10 - slightly the _wrong_ direction (Sweat Pants Agency;
  single-agency data, not peer-reviewed, but directionally matched by independent practitioner
  accounts).
- Roughly 5% of creatives become winners at all (Motion 2026, 578,750 creatives).
- Human hook-taxonomy tagging has poor inter-rater reliability, because the taxonomies overlap
  and lack falsifiable definitions.

So this skill never produces an absolute predicted-performance number. It produces:

- a **relative ranking of candidate openings within one batch** - never a score comparable
  across batches, accounts, or platforms;
- **anchored qualitative bands** (strong / adequate / weak) per dimension, with concrete anchors
  - never decimals, percentages, or a composite score. A "7.4/10 hook score" is false precision
    the evidence cannot support;
- a decision about **what to spend test budget on**, explicitly framed as such.

**Scope ends at the ranking.** Editing, pacing, or structure beyond the opening is out of scope.

- Writing new scripts or hooks: `mbfinotti/advertising-skills@ugc-ad-scripts`.
- Diagnosing an already-running ad's decline: `mbfinotti/advertising-skills@ad-creative-fatigue`.
- Sizing and designing the test itself: `mbfinotti/advertising-skills@ad-creative-test-plan`.
- Ad copy variants: `mbfinotti/advertising-skills@ad-copy-variants`.
- Briefs: `mbfinotti/advertising-skills@ad-creative-brief`.
- Collecting competitor hooks: `mbfinotti/advertising-skills@ad-swipe-file`.
- Format/placement fit: `mbfinotti/advertising-skills@ad-format-fit`.
- Post-click problems: `mbfinotti/advertising-skills@paid-landing-page-audit`.
- Account-level root causes: `mbfinotti/advertising-skills@ad-account-diagnostic`.

## Interview

Ask before scoring anything.

- One question per message.
- Offer multiple-choice answers where possible.
- Skip anything already answered or visible in the supplied material.

- Which platform, and which exact placement? (Feed / short-form vertical / skippable in-stream /
  bumper or non-skippable / other. The hook window depends on this.)
- B2B or B2C?
- Cold prospecting or warm/retargeting?
- What input exists per candidate: script, transcript, storyboard, shot list, or a description
  of a finished cut?
- How many candidate openings are in the batch?
- What is the offer, and what does the rest of the ad actually deliver? (Needed for the
  promise-payoff gate.)
- Who is the buyer, and - just as important - what disqualifies a viewer? (Needed for the
  qualification gate.)
- Wh
Read the full source on GitHub (opens external page)
Context

Related work