/x-hook-extractor

name: x-hook-extractor
description: "Reverse-engineer the hook from a viral X (Twitter) tweet or thread URL. Identifies which of the 10 canonical 2026 X formulas it uses (one-liner contrarian, data-point, build-in-public, quote-tweet, mini-list, relatable cold-open, listicle-thread, story thread, curiosity-gap, how-I teardown), explains why it worked, and returns a blank template mapped to your topic with its primary goal. Use to learn from a tweet you admire. Not for writing your own (use x-post-writer or x-thread-builder)."

X Hook Extractor

Paste a viral tweet or thread URL. Get back: which hook formula it uses, the exact structure, why it worked, and a blank template you can fill with your own voice.

When to use

• User finds a viral tweet or thread they want to study
• User wants to replicate a specific creator's pattern
• Before `x-post-writer` or `x-thread-builder`, to seed a draft with a proven shape

Input

An X tweet or thread URL (x.com or twitter.com, `/status/<id>`). For a thread, the URL of the first tweet is best.

Output

• **Formula identified** (X1-X10 from `../../references/hook-formulas.md`) with a

confidence score

• **Container:** single tweet vs thread, and why that container fit the idea
• **Structural breakdown:**
• The hook line (and for a thread, how tweet 1 opens the loop)
• Body architecture (per-tweet roles for a thread)
• The close (what earns the repost or bookmark)
• Reaction-triggering devices (numbers, named entities, the open loop)
• **Primary goal** the original chased (replies / reposts / likes / bookmarks)
• **Why it worked** psychologically and algorithmically
• **Blank template** with `{slot}` markers matched to the original, ready for the

user's topic

• **Cautions:** anything in the original that would fail a 2026 audit (more

than one em dash in a tweet, an AI-vocab cluster, 3+ hashtags, link in tweet 1)

Steps

1. **Parse the URL.** `lib.url_parser.parse_x_url(url)` returns `handle`, `tweet_id`, `url_type`. 2. **Get the text.** This bundle has no built-in tweet reader, so ask the user to paste the tweet or the full thread text. (If they later wire an Apify tweet actor, read it automatically.) 3. **Detect the container.** One self-contained tweet, or a multi-tweet thread. 4. **Classify against the 11 formulas** using features:

• Single tweet: a flat contrarian claim (X1)? one hard number (X2)? a personal

metric/confession (X3)? a quote tweet adding a layer (X4)? a one-line-per- item list (X5)? a relatable shared moment (X6)?

• Thread: a numbered teaching promise (X7)? a story starting at the tension

(X8)? a surprising result with the mechanism withheld (X9)? a first-person "how I" teardown (X10)? 5. **Score confidence.** If two formulas fit, return the top 2 with fit scores. 6. **Extract structure.** Label each part by its role. For a thread, map tweet 1 (the loop), the front-loaded payoff, the body beats, and the closer. 7. **Name the primary goal** the original optimized for. 8. **Generate a blank template** with `{slot}` markers matched to the original shape and the user's topic. 9. **Audit the source.** Flag any AI tells in the original so the user does not copy them.

Example

See `references/examples.md` for worked teardowns.

Formulas reference

See `../../references/hook-formulas.md` for the 11 canonical X formulas with full skeletons and goal tags.

Files

• `SKILL.md` - this file
• `references/classification-rules.md` - feature extraction + scoring heuristics
• `references/examples.md` - worked teardowns (single tweet and thread)

Related skills

• `x-post-writer` - use the extracted single-tweet template to draft your own
• `x-thread-builder` - use the extracted thread template
• `x-humanizer --mode audit` - audit your draft before shipping