Ready to supercharge your AI agents?

Install any skill in seconds. Free to start, no credit card required.

LinkedIn Hook Extractor — measured AI agent skill | DecimalAI

sergebulaev/linkedin-hook-extractor

NewSkillScoreMaintained

Reverse-engineer the hook formula from a viral LinkedIn post URL. Returns which of the 16 canonical 2026 formulas it uses (anaphora, R.I.P., year-pivot, time-anchor, curiosity-gap, contrarian, comment-gate, emotional cold-open, named-gratitude, and 7 more), why it worked, and a blank template. Use to learn from a competitor's post, not to write your own (use linkedin-post-writer).

marketingby sergebulaev

Links this skill to your agents and keeps it current with the author’s updates.

Automatic

Marketing & Content pack

1 · One-time — install the CLI (docs ↗)

pip install decimalai

2 · Get the skill — writes .claude/skills/sergebulaev-linkedin-hook-extractor/SKILL.md

decimalai skills pull sergebulaev-linkedin-hook-extractor --out .claude/skills/

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/sergebulaev-linkedin-hook-extractor/SKILL.md https://app.decimal.ai/s/sergebulaev-linkedin-hook-extractor/SKILL.md

Raw file: /s/sergebulaev-linkedin-hook-extractor/SKILL.md ↗ · machine-readable summary at /s/sergebulaev-linkedin-hook-extractor.json ↗

In productionGlobal

how this skill behaves on real traffic · last 30 days

No production data for this skill yet — usage numbers appear here once agents start running it.

Anonymized aggregate across every org using this skill — proof from real production use, not a demo.

Does this skill help? · Benchmark: with vs. withoutsign in to run this on your agents

25 test cases from the publisher’s eval.yaml · each run twice on gemini-3.6-flash — with the skill injected, then without · last run 2h ago

Yes — +28% lift · at higher cost+43% tokens0% turns84% of whole cases pass with skill (21/25)

Test case	Without → With	Effect	Δ tokens	Δ turns
case-01	✗→✓	▲ Improved	51%	0%
case-02	✗→✓	▲ Improved	51%	0%
case-04	✗→✓	▲ Improved	65%	0%
case-05	✗→✓	▲ Improved	35%	0%
case-06	✗→✓	▲ Improved	87%	0%

Showing 5 of 25 cases

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

sergebulaev / linkedin-skills

skills/linkedin-hook-extractor/SKILL.md

View on GitHub ↗

License MITImported 8/3/2026Version v1Bundled 2 files

Skill contents

references/

SKILL.md

LinkedIn Hook Extractor

Paste a viral LinkedIn post URL. Get back: which hook formula it uses, the exact structure, why it worked, and a blank template mapped to your topic.

When to use

User finds a viral post they want to study
User wants to replicate a specific creator's pattern
Before linkedin-post-writer to seed a draft with a proven structure

Input

A LinkedIn post URL (any type: activity, share, ugcPost).

Output

Formula identified (F1-F16 from ../../references/hook-formulas.md) with confidence score
Structural breakdown:
Hook lines (first 210 chars)
Body architecture (sections + what each does)
Close pattern
Reaction-triggering devices (numbers, named entities, vulnerabilities)
Why it worked psychologically
Blank template filled with slot markers matched to the original, ready for the user's voice
Cautions: anything in the original post that would fail 2026 audit (em dashes, AI vocab, outdated tactics)

Steps

Parse URL. lib.url_parser.parse_linkedin_url → post_urn.
Fetch post body. If APIFY_TOKEN is set, call lib.ApifyClient.fetch_post(url). Otherwise ask the user to paste the text.
Classify. Match against the 16 formulas using features:

First 2 lines: anaphoric? question? confession? number-led?
Body: numbered list? dated receipts? ledger? teardown?
Close: mirror question? identity reframe? commitment?
F11-F16 cues: in-medias-res emotional scene with no setup (F11 Emotional Cold-Open); "I don't know who needs to hear this" reassurance (F12 Permission Slip); fake-bad-news that resolves positive (F13 Bait-and-Switch); a roll-call of named people thanked (F14 Named Gratitude); "{jargon} explained to kids" glossary (F15 Explain-to-Kids); "outside I'm called X, at home none of it survives" (F16 Status-Strip).

Score confidence. If multiple formulas fit, return top 2 with fit scores.
Extract structure. Pull each logical section and label it by formula role.
Generate blank template. Replace specifics with {slot} markers that match the user's topic.
Audit the source. Flag any AI tells in the original so the user doesn't copy them.

Example

See references/examples.md for worked examples.

Formulas reference

See ../../references/hook-formulas.md for the 16 canonical formulas with full skeletons.

Files

SKILL.md — this file
references/classification-rules.md — feature extraction + scoring heuristics

Related skills

linkedin-post-writer — use the extracted template to draft your own
linkedin-humanizer --mode audit — audit your draft before shipping

Want to see if sergebulaev/linkedin-hook-extractor would activate on your phrasing? Try it before you install — no LLM call, no signup.

Other measured skills in the registry, with their headline benchmark lift.