Frame an epic as a testable hypothesis with target user, expected outcome, and validation method. Use when defining a major initiative before roadmap, discovery, or delivery planning.

Purpose Frame epics as testable hypotheses using an if/then structure that articulates the action or solution, the target beneficiary, the expected outcome, and how you'll validate success. Use this to manage uncertainty in product development by making assumptions explicit, defining lightweight experiments ("tiny acts of discovery"), and establishing measurable success criteria before committing to full build out. This is not a requirements spec—it's a hypothesis you're testing, not a feature you're committed to shipping. Input Works best with: The initiative or epic idea, in whatever form it currently exists — a sentence is enough. Also useful: The target user, the outcome you expect, and how you might measure it (the skill helps you sharpen all three). Anything supplied with the invocation itself — text after the skill name, a pasted context dump, or an appended ARGUMENTS: line — counts as answers already given. Use it and skip whatever it covers; don't re ask. Arriving empty handed? That works too. The skill asks what the initiative is and who it's for, then builds the if/then hypothesis with you. Example invocation: Frame as an epic hypothesis: adding usage based alerts so account admins catch overages before invoice shock. Key Concepts The Epic Hypothesis Framework Inspired by Tim Herbig's Lean UX hypothesis format, the structure is: If/Then Hypothesis: If we [action or solution on behalf of target persona] for [target persona] Then we will [attain or achieve a desirable outcome or job to be done] Tiny Acts of Discovery Experiments: We will test our assumption by: [Experiment 1] [Experiment 2] [Add more as necessary] Validation Measures: We know our hypothesis is valid if within [timeframe] we observe: [Quantitative measurable outcome] [Qualitative measurable outcome] [Add more as necessary] Why This Structure Works Hypothesis driven: Forces you to state what you believe (and could be wrong about) Outcome focused: "Then we will" emphasizes user benefit, not feature output Experiment first: Encourages lightweight validation before full build Falsifiable: Clear success criteria make it possible to kill bad ideas early Risk management: Treats epics as bets, not commitments Anti Patterns (What This Is NOT) Not a feature spec: "Build a dashboard with 5 charts" is a feature, not a hypothesis Not a guaranteed commitment: Hypotheses can (and should) be invalidated Not output focused: "Ship feature X by Q2" misses the point—did it achieve the outcome? Not experiment free: If you skip experiments and go straight to build, you're not testing a hypothesis When to Use This Early stage feature exploration (before committing to full roadmap) Validating product market fit for new capabilities Prioritizing backlog (epics with validated hypotheses get higher priority) Managing stakeholder expectations (frame work as experiments, not promises) When NOT to Use This For well validated features (if you've already proven demand, skip straight to user stories) For trivial features (don't over engineer small tweaks) When experiments aren't feasible (rare, but sometimes you must commit before testing) Application Use template.md for the full fill in structure. Step 1: Gather Context Before drafting an epic hypothesis, ensure you have: Problem understanding: What user problem does this address? (reference skills/problem statement/SKILL.md ) Target persona: Who benefits? (reference skills/proto persona/SKILL.md ) Jobs to be Done: What outcome are they trying to achieve? (reference skills/jobs to be done/SKILL.md ) Current alternatives: What do users do today? (competitors, workarounds, doing nothing) If missing context: Run discovery interviews or problem validation work first. Step 2: Draft the If/Then Hypothesis Fill in the template: Quality checks: "If we" is specific: Not "improve the product" but "add one click Slack notifications when tasks are assigned" "For" is a clear persona: Not "users" but "remote project managers juggling 3+ distributed teams" (reference skills/proto persona/SKILL.md ) "Then we will" is an outcome: Not "users will have notifications" but "users will respond to task assignments 50% faster" Examples: ✅ "If we add one click Google Calendar integration for trial users, then we will increase activation rates by 20% within 30 days" ✅ "If we provide bulk delete functionality for power users managing 1000+ items, then we will reduce time spent on cleanup tasks by 70%" ❌ "If we build a dashboard, then users will use it" (vague, not measurable) Step 3: Design Tiny Acts of Discovery Experiments Before building the full epic, define lightweight experiments to test the hypothesis: Experiment types: Prototype + user testing: Fake the feature with a clickable prototype, test with 5 10 users Concierge test: Manually perform the feature for a few users, see if they value it Landing page test: Describe the feature, measure sign ups or interest Wizard of Oz test: Present the feature as if it's automated, but do it manually behind the scenes A/B test (if feasible): Test a lightweight version vs. control Quality checks: Fast: Experiments should take days/weeks, not months Cheap: Avoid full engineering builds—use prototypes, manual processes, or existing tools Falsifiable: Design experiments that could prove you wrong Examples: "Create a Figma prototype of the bulk delete flow and test with 5 power users" "Manually send Slack notifications to 10 trial users and track response time" "Add a 'Request this feature' button to the UI and measure click through rate" Step 4: Define Validation Measures Specify what success looks like and the timeframe for evaluation: Quality checks: Timeframe is realistic: Not "within 6 months" (too slow) or "within 3 days" (too fast) Quantitative measures are specific: Not "more users" but "20% increase in activation rate" Qualitative measures are observable: Not "users like it" but "8 out of 10 users say they'd pay for this feature" Examples: ✅ "Within 4 weeks, we observe:" "Activation rate increases from 40% to 50% (quantitative)" "75% of surveyed trial users say the integration saved them time (qualitative)" ❌ "Within 1 year, we observe:" "Revenue goes up" (too vague, too long) Step 5: Run Experiments and Evaluate Execute experiments: Build prototypes, run tests, gather data Measure results: Did you hit the validation measures? Decision point: ✅ Hypothesis validated: Proceed to building user stories and adding to roadmap ❌ Hypothesis invalidated: Kill the epic or pivot to a different hypothesis ⚠️ Inconclusive: Run additional experiments or tighten validation measures Step 6: Convert to User Stories (If Validated) Once the hypothesis is validated, break the epic into user stories: Examples See examples/sample.md for full epic hypothesis examples. Mini example excerpt: Common Pitfalls Pitfall 1: Hypothesis is a Feature, Not an Outcome Symptom: "If we build a dashboard, then we will have a dashboard" Consequence: You're describing output, not outcome. This doesn't test anything. Fix: Focus on the user outcome: "If we build a dashboard showing real time task status, then PMs will spend 50% less time asking for status updates." Pitfall 2: Skipping Experiments Symptom: "We'll test our assumption by building the full feature" Consequence: You've committed to building before validating. Not a hypothesis—it's a feature commitment. Fix: Design lightweight experiments (prototypes, concierge tests, landing pages) that take days/weeks, not months. Pitfall 3: Vague Validation Measures Symptom: "We know it's valid if users are happy" Consequence: Success criteria are subjective and unmeasurable. Fix: Define specific, falsifiable metrics: "80% of surveyed users rate the feature 4+ out of 5" or "Response time drops by 50%." Pitfall 4: Unrealistic Timeframes Symptom: "We know it's valid if within 6 months revenue increases" Consequence: Too slow to inform decisions. By then, you've already built it. Fix: Aim for 2 4 week validation cycles. If you can't measure in that timeframe, choose a leading indicator (e.g., activation rate, not annual revenue). Pitfall 5: Treating Epics as Commitments Symptom: "We already told the CEO we're shipping this, so we have to validate it" Consequence: Experiments are theater—you're going to build it regardless of results. Fix: Frame epics as hypotheses before making commitments. If stakeholders need certainty, explain the risk of building unvalidated features. References Related Skills skills/problem statement/SKILL.md — Hypothesis should address a validated problem skills/proto persona/SKILL.md — Defines the "for [persona]" section skills/jobs to be done/SKILL.md — Informs the "then we will" outcome skills/user story/SKILL.md — Validated epics decompose into user stories skills/user story splitting/SKILL.md — How to break validated epics into stories External Frameworks Tim Herbig, Lean UX Hypothesis Statement — Origin of if/then hypothesis format Jeff Gothelf & Josh Seiden, Lean UX (2013) — Hypothesis driven product development Alberto Savoia, Pretotype It (2011) — Lightweight experiments to validate ideas Eric Ries, The Lean Startup (2011) — Build Measure Learn cycle Dean's Work Backlog Epic Hypothesis Prompt (inspired by Tim Herbig's framework) Provenance Adapted from prompts/backlog epic hypothesis.md in the https://github.com/deanpeters/product manager prompts repo. Skill type: Component Suggested filename: epic hypothesis.md Suggested placement: /skills/components/ Dependencies: References skills/problem statement/SKILL.md , skills/proto persona/SKILL.md , skills/jobs to be done/SKILL.md Used by: skills/user story/SKILL.md , skills/user story splitting/SKILL.md

epic-hypothesis