--- name: product-experiment-hypothesis-generator description: "Generates 12 well-formed product experiment hypotheses across 6 intervention levers, each with a stated mechanism, primary and guardrail metrics, expected effect size, sample-size feasibility, and a prioritized test sequence. Use when planning A/B tests, building an experiment backlog, deciding what to test next to move a target metric, or converting a vague product idea into a testable hypothesis." argument-hint: "[target-metric] [product-context]" allowed-tools: ["Read"] --- # Product Experiment Hypothesis Generator ## Purpose Turn a target metric and product context into a backlog of 12 rigorously structured experiment hypotheses — each with a causal mechanism, defined success and guardrail metrics, and a feasibility check — so the team tests high-leverage ideas in the right order instead of shipping random tweaks and hoping the number moves. ## When to Use Invoke this skill when the user asks to: - Generate hypotheses or ideas for A/B tests to move a specific metric - Build or refill an experiment backlog for the next quarter or sprint - Decide what to test next when a KPI (activation, conversion, retention, ARPU) is stuck - Convert a rough product idea into a properly formed, testable hypothesis - Get expected mechanisms and success criteria for an experiment they already have in mind - Prioritize a list of competing test ideas by expected impact and effort ## Inputs Required Before starting, collect: - **Target metric**: The single primary metric to move (e.g., signup-to-activation rate, checkout conversion, D7 retention, trial-to-paid) - **Current baseline**: The metric's current value and, if known, its trend - **Product context**: What the product is, the surface being tested (onboarding, pricing page, checkout, feature X) - **Audience/segment**: Who the experiment targets (new users, free tier, mobile, specific cohort) - **Traffic volume**: Approximate weekly users or sessions on the tested surface (drives sample-size feasibility) - **Known constraints**: Engineering effort ceiling, brand/legal guardrails, or ideas already tried and failed - **Guardrail metrics to protect**: Metrics that must not degrade (e.g., revenue, support tickets, unsubscribe rate) If baseline or traffic volume is missing, estimate a plausible range, state the assumption, and flag which hypotheses become infeasible at low traffic. ## Instructions ### Generate 12 Hypotheses Across 6 Intervention Levers Produce 2 hypotheses per lever. Every hypothesis must follow this exact structure: - **Hypothesis statement**: "If we [specific change], then [target metric] will [direction] because [causal mechanism]." — one sentence, no hedging. - **Mechanism**: The behavioral or psychological reason the change should work (e.g., reduced cognitive load, loss aversion, faster time-to-value). 1–2 sentences. - **Primary metric**: The exact metric this test moves, matching the target. - **Guardrail metrics**: 1–2 metrics that must not regress. - **Expected effect size**: A realistic relative lift range (e.g., +3–7% relative), grounded in the size of the change — not wishful. - **Feasibility flag**: Given traffic volume, note whether the sample size is achievable in a reasonable window (2–4 weeks) or whether the effect is too small to detect at current traffic. - **Effort**: Low / Medium / High engineering effort. --- **Lever 1 — Friction Reduction** Remove or shorten steps between intent and outcome. - Target: fewer form fields, fewer clicks, removed gates, autofill, progressive disclosure - Strong for activation and conversion metrics where drop-off is step-based - Mechanism basis: cognitive load, effort-to-reward ratio **Lever 2 — Motivation & Value Framing** Change what the user believes they get, not what they do. - Target: headline copy, benefit emphasis, social proof placement, outcome-first messaging - Strong when drop-off happens before action, not during - Mechanism basis: perceived value, relevance, aspiration **Lever 3 — Timing & Sequencing** Change when a prompt, ask, or step appears. - Target: moving a paywall, delaying an upsell, front-loading the aha-moment, re-ordering onboarding - Strong for retention and monetization metrics - Mechanism basis: readiness, momentum, time-to-value **Lever 4 — Incentive & Commitment Mechanics** Add a reason to act now or a small commitment that compounds. - Target: limited offers, progress bars, streaks, tiered rewards, trial extensions - Strong for conversion and repeat-engagement metrics - Mechanism basis: loss aversion, commitment consistency, goal gradient **Lever 5 — Personalization & Relevance** Make the experience match the user's context or segment. - Target: segment-specific defaults, recommended paths, tailored copy, use-case selection - Strong when a broad audience has divergent needs - Mechanism basis: relevance, self-selection, reduced choice overload **Lever 6 — Trust & Risk Reduction** Lower the perceived cost of proceeding. - Target: guarantees, security signals, transparent pricing, reversibility ("cancel anytime"), reviews - Strong for high-consideration or payment-adjacent steps - Mechanism basis: risk aversion, uncertainty reduction --- ### Prioritize the Backlog (ICE + Feasibility) Score each of the 12 hypotheses and rank them: - **Impact**: expected effect size on the target metric (1–5) - **Confidence**: strength of the mechanism and any prior evidence (1–5) - **Effort**: inverse of engineering cost (1–5, where Low effort = 5) Compute an ICE score, then apply a feasibility override: any hypothesis flagged as undetectable at current traffic drops in rank regardless of ICE, with a note on the traffic needed to make it viable. Present the top 5 as a recommended test sequence, noting which tests can run concurrently (non-overlapping surfaces) and which must run sequentially (shared surface or interacting effects). ## Output Format - **Structure**: 6 lever sections (2 hypotheses each, fully structured) → ICE-ranked table of all 12 → top-5 recommended test sequence - **Length**: 1,000–1,400 words - **Format**: Structured bullets per hypothesis; a ranking table; a numbered sequence for the top 5 - **Sections**: Assumptions/inputs recap → 6 levers with 12 hypotheses → prioritization table → recommended test sequence - **Tone**: Senior PM — precise, mechanism-driven, skeptical of vague wins; no hype, no "should improve engagement" hand-waving ## Quality Checklist Before delivering output, verify: - [ ] Exactly 12 hypotheses generated — 2 per lever (count them) - [ ] Every hypothesis uses the "If we X, then metric Y will Z because [mechanism]" form with a stated causal mechanism - [ ] Every hypothesis names a primary metric that matches the target and at least one guardrail metric - [ ] Expected effect sizes are ranges grounded in the change, not single optimistic numbers - [ ] Feasibility flags reflect the traffic volume provided — small-effect tests at low traffic are marked undetectable - [ ] Top 5 sequence notes which tests can run concurrently vs. must run sequentially ## Notes - The most common failure is a hypothesis with no mechanism — "change the button color" is not a hypothesis, "increase contrast to make the primary action more salient and reduce decision hesitation" is. - Guardrail metrics matter most for incentive and pricing tests, where the target metric can improve while revenue or trust quietly degrades. - At low traffic, prefer high-impact, structural changes (friction removal, timing) over micro-copy tweaks — small effects are unmeasurable and waste test slots. - Encourage running one hypothesis per surface at a time unless the tests are genuinely independent; overlapping tests contaminate attribution. --- ## How to use this skill This file is a Claude Code skill in [Anthropic's standard format](https://docs.anthropic.com/en/docs/claude-code/skills) — markdown with YAML frontmatter at the top. **Installing in Claude Code:** - `~/.claude/skills//SKILL.md` — auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects) - `/.claude/skills//SKILL.md` — auto-loads only when working in that project (recommended for project-specific skills) Restart Claude Code or open a new session to register. **Using in Claude.ai, ChatGPT, Gemini, or other AI tools:** 1. 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