/threads-hook-extractor

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

Threads Hook Extractor

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

Input

A Threads post or thread URL (`threads.net` or `threads.com`, `/@handle/post/`). For a thread, the URL of the first post is best.

Output

• **Formula identified** (T1-T10 from `../../references/hook-formulas.md`) with a

confidence score

• **Container:** single post vs thread, and why that container fit the idea
• **Structural breakdown:**
• The hook line (and for a thread, how post 1 opens the loop)
• Body architecture (per-post roles for a thread)
• The close (what earns the repost or the reply)
• Reaction-triggering devices (numbers, named entities, the open loop, the warm

invite)

• **Primary goal** the original chased (replies / reposts / likes / quotes)
• **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 post, an AI-vocab cluster, 2+ hashtags, link in post 1, a cold X tone)

Steps

1. **Parse the URL.** `lib.url_parser.parse_threads_url(url)` returns `handle`, `post_id`, `url_type`. 2. **Get the text.** This bundle has no built-in post reader, so ask the user to paste the post or the full thread text. (If they later wire an Apify post-read actor, read it automatically.) 3. **Detect the container.** One self-contained post, or a multi-post thread. 4. **Classify against the 13 formulas** using features:

• Single post: a warm contrarian claim (T1)? one hard number (T2)? a personal

metric/confession (T3)? a quote post adding a layer (T4)? a one-line-per-item list (T5)? a relatable shared moment (T6)?

• Thread: a numbered teaching promise (T7)? a story starting at the tension

(T8)? a surprising result with the mechanism withheld (T9)? a first-person "how I" teardown (T10)? 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 post 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 13 canonical Threads 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 post and thread)

Related skills

• `threads-post-writer` - use the extracted template to draft your own post or thread
• `threads-humanizer --mode audit` - audit your draft before shipping