hook-writer
Generate scroll-stopping hooks — the first line of a post, thread, video, or email that decides whether anyone keeps reading. Use when asked to write a hook, an opener, a first line, a thread starter, a video cold-open, or to make something more clickable. Produces multiple distinct hook options across proven angles (curiosity, contrarian, result, story, stakes), each labelled with why it works and which platform it fits.
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills /tmp/hook-writer && cp -r /tmp/hook-writer/plugins/pm-creator/skills/hook-writer ~/.claude/skills/hook-writerSKILL.md
# Hook Writer Skill The hook is 80% of the result. A brilliant post with a flat first line dies; a mediocre post with a great hook travels. This skill writes hooks the way top creators do — multiple angles, each engineered to stop the scroll — so you can pick the one that fits. ## Working from a brief Given just a topic or a finished piece, **generate the hooks anyway**. Infer the audience and the payoff, and never return a single safe option — the value is in the *range*. Mark any invented number *(assumed — use a real one)* because specific numbers are part of what makes hooks land. ## Required Inputs Ask for (if not already provided): - **The topic / the content** the hook is for - **Platform & format** (X, LinkedIn, YouTube title, Reel cold-open, email subject) - **Audience** and the **payoff** (what they get if they keep reading) ## Output Format Give **8–12 hooks grouped by angle**, each with a one-line *why it works* and the format it suits: - **Curiosity gap** — open a loop the reader needs closed - **Contrarian / pattern-break** — challenge a common belief - **Specific result** — a concrete, numeric outcome - **Story / in-media-res** — drop them into a moment - **High stakes / cost of inaction** — what they lose by ignoring it - **Listicle / promise** — a clear, scannable payoff - **Question** — a sharp, non-obvious question (used sparingly) Then: - **🏆 Top 3 picks** — the strongest for the stated platform, ranked, with why. - **Hook teardown** — one line on the *mechanism* the best hook uses, so the user can write their own next time. Keep each hook in the platform's natural length (a YouTube title ≤60 chars; an email subject ≤50; a Reel cold-open speakable in 2–3s). ## Quality Checks - [ ] Multiple genuinely different angles, not variations of one line - [ ] Each hook is specific (names, numbers, stakes), not vague - [ ] Top picks match the platform's length and norms - [ ] No clickbait that the content can't pay off — the hook must be honest - [ ] The teardown gives a reusable mechanism ## Anti-Patterns - "Here's everything you need to know about X" (zero tension) - Ten rewrites of the same hook - Clickbait the body betrays (kills trust + reach long-term) - Hooks too long for the platform (a 90-char YouTube title, a 3-line "first line")
Conduct a structured ethical review of an AI or ML feature, model, or product. Use when preparing to deploy an AI system, assessing algorithmic risk, auditing a model for bias, or producing a responsible AI impact assessment. Produces a structured ethics review covering fairness, transparency, privacy, safety, accountability, and societal impact with a risk tier score, pre-deployment checklist, and prioritised mitigations.
Structure AI and ML product decisions with the rigour of any product decision. Use when building AI-powered features, evaluating LLM integrations, designing AI products, or assessing AI readiness. Produces a complete AI product canvas covering problem definition, model approach, data requirements, evaluation framework, UX design, responsible AI checklist, and launch monitoring plan.
Transform feature briefs into structured design briefs that give designers the context they need before opening Figma. Use when asked to write a design brief, create a design handoff, brief a designer on a new feature, or translate a PRD into design requirements. Produces a brief with user goal, emotional context, success criteria, constraints, edge cases, and out-of-scope boundaries.
Design statistically rigorous A/B tests and interpret experiment results. Use when asked to design an experiment, run an A/B test, calculate sample size, interpret test results, or assess whether an experiment was successful. Produces a complete experiment design with hypothesis, sample size, run time, success criteria, and risk flags — or a results interpretation with ship/iterate/kill recommendation.
Synthesises user signals from multiple research sources into a unified, weighted insight brief. Use when you have data from interviews, support tickets, NPS verbatims, app reviews, or sales calls and need to reconcile contradictions, surface the underlying need behind requests, or answer 'what are users really telling us'. Produces ranked insights with confidence ratings, source weighting rationale, divergent signal analysis by user segment, and a research gap identification section.
Structure a product data analysis, metric deep-dive, funnel analysis, or cohort study. Use when asked to analyse product metrics, investigate a drop in conversion, explain a data change to stakeholders, or find the root cause of a metric movement. Produces a structured analysis with question, root cause, confidence level, and recommended action.
Interpret product metrics against goals and surface actionable signals. Use when asked to analyse product health, review key metrics, investigate a performance issue, produce a health report, or assess product-market fit signals. Produces a structured health report with RAG status, trend analysis, root cause hypotheses, and prioritised actions.
Structure a retention analysis, churn investigation, or engagement deep-dive for any product team. Use when asked to analyse user retention, investigate churn, measure DAU/MAU, or build a retention improvement plan. Produces a retention snapshot with root cause hypotheses, aha-moment correlation, and prioritised interventions.