--- name: innovation-experiment-design-framework description: "Structures a rigorous learning experiment to test a new nonprofit program, intervention, or operating model before full investment — defining the riskiest assumption, a falsifiable hypothesis, a minimum viable test, success and kill metrics, and a learning capture plan. Use when designing a pilot, testing a new program idea, de-risking an intervention, or setting up a structured experiment before scaling." argument-hint: "[program-idea] [target-population]" allowed-tools: ["Read"] --- # Innovation Experiment Design Framework ## Purpose Turn a promising-but-unproven program idea into a designed learning experiment — one that tests the riskiest assumption cheaply, sets clear thresholds for continue/pivot/kill decisions, and captures what was learned in a reusable format. Prevents the common nonprofit failure mode of scaling an untested intervention or piloting for months without a decision rule. ## When to Use Invoke this skill when the user asks to: - Design a pilot or test for a new program, service, or intervention - De-risk a new idea before committing budget or board approval - Set up success metrics and a decision rule for an experiment - Test a new operating model, delivery channel, or partnership approach - Structure a lean/MVP experiment for a program concept - Figure out the cheapest, fastest way to validate whether an idea will work ## Inputs Required Before starting, collect: - **Program idea**: 2–3 sentence description of the new program, intervention, or operating model being tested - **Target population**: Who the program is meant to serve, and roughly how many - **Intended outcome**: The change the program is supposed to create (behavioral, social, service-access, etc.) - **Riskiest belief**: What has to be true for this to work that you're least sure about (if unknown, the skill will identify it) - **Constraints**: Timeline, budget available for the test, and staff capacity - **Existing evidence**: Any prior data, similar programs, or anecdotes that inform the idea - **Decision context**: What decision this experiment is meant to inform (fund it, scale it, pitch to board/funder, kill it) If the riskiest belief is not provided, identify it in Step 1 and confirm it before proceeding. If budget or timeline is missing, assume a low-cost 6–8 week test and note the assumption. ## Instructions ### Step 1: Identify the Riskiest Assumption Every program idea rests on a stack of assumptions. Do not test the whole idea — test the one assumption that, if false, sinks everything. Categorize the underlying assumptions into four types: - **Desirability**: Does the target population actually want or engage with this? - **Feasibility**: Can we deliver it with our capacity, skills, and infrastructure? - **Viability**: Can it be sustained financially or operationally at scale? - **Effectiveness**: Does it actually produce the intended outcome? Rank the assumptions by two factors: how uncertain it is, and how catastrophic it is if wrong. The riskiest assumption is the one that is both highly uncertain and highly consequential. Name it explicitly — this is what the experiment tests. Everything else is deferred. ### Step 2: Write a Falsifiable Hypothesis Convert the riskiest assumption into a testable statement using this structure: > "We believe that [specific population] will [observable behavior or outcome] when we [specific intervention]. We will know we're right if [measurable signal] reaches [specific threshold] within [timeframe]." Requirements: - The hypothesis must be **falsifiable** — there must be a result that proves it wrong - Use observable behavior, not intentions ("50 caregivers complete the intake" beats "caregivers are interested") - State a specific threshold number, not a direction ("40% enrollment," not "high enrollment") ### Step 3: Design the Minimum Viable Test Select the cheapest, fastest test that can produce a real signal. Match the test type to the assumption category: - **For desirability**: Concierge test (deliver the service manually to a few people), landing-page/sign-up test, or fake-door test to measure real demand - **For feasibility**: Prototype a single delivery cycle with real staff and real participants at small scale - **For viability**: Model the unit economics with real cost data from a small run, or test whether a funder/partner will commit - **For effectiveness**: Small cohort with a clear before/after measure, ideally with a comparison group if capacity allows Specify: - **Sample size**: The minimum number of participants needed to trust the signal (be honest about statistical limits at pilot scale) - **Duration**: How long the test runs - **What's real vs. faked**: Which parts are genuinely built and which are simulated manually to save cost - **What you're deliberately NOT building yet** ### Step 4: Define Success, Pivot, and Kill Metrics Set three thresholds before running the test — not after, when bias creeps in: - **Success (green)**: The result that says "continue and invest more." Tie it to the hypothesis threshold. - **Pivot (yellow)**: The result that says "there's a signal, but the current design is wrong — change one variable and retest." - **Kill (red)**: The result that says "stop — this assumption is false." State this explicitly; a test with no kill condition is not a test. Include one **leading indicator** (early signal visible within days) and one **lagging indicator** (the true outcome measure), so the team isn't flying blind for weeks. ### Step 5: Plan the Learning Capture Design how insight gets documented so it survives beyond the pilot team: - **Baseline snapshot**: What to record before the test starts - **Data collection method**: How each metric is captured (survey, observation, admin data, interviews) and who owns it - **Learning log cadence**: Weekly check-in questions the team answers during the run - **Decision debrief template**: A one-page format capturing the hypothesis, the result vs. thresholds, the decision made (continue/pivot/kill), and the single most important thing learned ### Step 6: Produce the One-Page Experiment Brief Assemble the full experiment into a single scannable brief the team can execute from and a funder or board could review. ## Output Format - **Structure**: Six labeled sections following the steps, ending with a consolidated one-page experiment brief - **Length**: 900–1,300 words - **Format**: Structured document — prose for reasoning sections, a metrics table for the success/pivot/kill thresholds, and the final brief as a scannable one-page summary - **Sections**: Riskiest Assumption → Falsifiable Hypothesis → Minimum Viable Test → Success/Pivot/Kill Metrics (table) → Learning Capture Plan → One-Page Experiment Brief - **Tone**: Practical innovation director — evidence-minded, decisive about scope, honest about what a small pilot can and cannot prove ## Quality Checklist Before delivering output, verify: - [ ] Exactly one riskiest assumption is named and justified by both uncertainty and consequence - [ ] The hypothesis is falsifiable and states a specific numeric threshold and timeframe - [ ] The test type matches the assumption category (desirability, feasibility, viability, or effectiveness) - [ ] All three metric thresholds are present — success, pivot, AND an explicit kill condition - [ ] Both a leading and a lagging indicator are specified - [ ] The one-page brief is genuinely one page and executable without further editing ## Notes - The most common mistake is testing the whole program instead of the single riskiest assumption — resist scope creep in the test design - At pilot scale, most tests won't reach statistical significance; treat results as directional signal, not proof, and say so plainly to funders - A kill condition feels uncomfortable to write but is the entire point — it protects the organization's resources and credibility - Concierge and fake-door tests often reveal in two weeks what a six-month pilot would reveal at 20x the cost - When results land in the "pivot" zone, change only one variable before retesting, or you won't know what moved the outcome --- ## 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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