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 (em dashes, AI vocab, 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 10 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 10 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