/linkedin-hook-extractor

Hook Formula Classification Rules

Features extracted from a post and how they map to formulas.

Feature extraction

Hook features (first 2 lines)

• anaphora_count: number of parallel "X can Y" style lines at the top
• leads_with_number: does line 1 start with a dollar figure or stat?
• question_hook: is line 1 a question?
• confession_phrase: "I stopped", "I was wrong", "for years I"
• obituary_phrase: "R.I.P.", "dying since", "cause of death"
• time_anchor: "{N} {days|months|years} ago"
• year_over_year: "In {2024|2025}, I ... In {2025|2026}, I'm"
• curiosity_gap: short incomplete tease (<8 words, no noun specified)
• free_reversal: "I charge X. Today it's free."
• public_commitment: "For the next 24 hours, I will"

Body features

• has_numbered_list: 1., 2., 3., ... with ≥4 items
• has_dated_receipts: multiple "{Month Year} — {event}" lines
• has_ledger: line-item dollar amounts (non-rounded)
• has_teardown: screenshot references or annotations
• has_checklist: named steps with instructions

Close features

• mirror_question: "What's your {last→this} pivot?"
• identity_reframe: "If you're X, you already lost"
• commitment_close: "If I'm wrong, I owe you a post"
• soft_offer: "Connect + DM me for X"
• comment_gate: "Comment KEYWORD below"

Mapping features → formulas

FORMULA_RULES = {
    "F1_anaphora": {
        "required": ["anaphora_count >= 3"],
        "boost": ["has_numbered_list", "metaphor_close"],
    },
    "F2_rip_obituary": {
        "required": ["obituary_phrase"],
        "boost": ["has_numbered_list", "identity_reframe"],
    },
    "F3_year_over_year": {
        "required": ["year_over_year"],
        "boost": ["mirror_question"],
    },
    "F4_time_anchor_confession": {
        "required": ["time_anchor OR confession_phrase"],
        "boost": ["mirror_question"],
    },
    "F5_self_proving_meta": {
        "required": ["public_commitment"],
        "boost": ["commitment_close", "has_numbered_list"],
    },
    "F6_comment_gate": {
        "required": ["comment_gate"],
        "boost": ["has_numbered_list"],
    },
    "F7_odd_precision_money": {
        "required": ["leads_with_number", "has_ledger"],
        "boost": ["identity_reframe"],
    },
    "F8_paid_vs_free_reversal": {
        "required": ["free_reversal"],
        "boost": ["has_checklist", "soft_offer"],
    },
    "F9_curiosity_gap": {
        "required": ["curiosity_gap"],
        "boost": [],
    },
    "F10_contrarian_historical": {
        "required": ["has_dated_receipts"],
        "boost": ["identity_reframe"],
    },
}

Confidence scoring

def score_formula(post_features: dict, rules: dict) -> float:
    required_met = sum(1 for r in rules["required"] if eval_feature(post_features, r))
    if required_met < len(rules["required"]):
        return 0.0
    boost = sum(1 for b in rules["boost"] if post_features.get(b))
    return 1.0 + 0.15 * boost  # cap at 1.6

Return top 2 formulas with score > 0.8.

Edge cases

• Hybrid hooks: when a post mixes two formulas (e.g., F4 confession + F3 year-over-year), return both with split confidence.
• Narrative-only posts: if no structural hook fires, classify as "free-form narrative" and skip formula assignment.
• Non-English: skip classification, return structural breakdown only.