build-hook-library

Build a LinkedIn hook library by generating 50-100 hooks across nine formulas using seed data like voice profiles and past posts. The output is a structured YAML payload containing proven hooks, fresh hooks, and pillar distribution for downstream content planning.

Effective LinkedIn hook formulas include contrarian-reframe, specific-stakes, named-mechanism, before-after, list-promise, pattern-break, loop-open, question-stake, and insider-secret. Using these nine structures ensures varied, high-engagement openings calibrated to audience intent.

Generate voice-matched LinkedIn hooks by seeding the process with voice transcripts, ICP pain-language, and belief audits. This ensures the resulting hook library aligns with the specific client's tone and content pillar strategy rather than relying on generic templates.

Track hook decay risk and pillar distribution by outputting a structured YAML section that tags each hook's decay probability and categorizes its content pillar. This validation gate approach maintains hook freshness and prevents audience fatigue across the content engine.

Yes, you can populate a company YAML file directly. The generation process outputs a hook_library YAML section containing proven_hooks, fresh_hooks, pillar distribution, and validation gate counts, ready for immediate integration into your content workflow.

To avoid generic templates, you need seed data such as voice transcripts, ICP pain-language, belief audits, audience comments, client slug, and past posts. These inputs calibrate the hooks to actual audience intent and specific voice profiles.