---
name: experimentation-framework-builder
description: "Generates a complete experiment design specification from a proposed product change — including a falsifiable hypothesis, unit of randomization, primary and guardrail metrics, sample size and runtime estimate, randomization and rollout plan, and a ranked risk register. Use when designing an A/B test, defining metrics for an experiment, calculating required sample size, or pressure-testing an experiment plan before launch."
argument-hint: "[product-change] [baseline-metrics]"
allowed-tools: ["Read"]
---

# Experimentation Framework Builder

## Purpose
Turn a proposed product change into a launch-ready experiment design specification — falsifiable hypothesis, unit of randomization, metric tree, powered runtime, and a ranked risk register — so analysts stop reinventing the design doc for every test and stop shipping experiments that can't actually answer the question.

## When to Use
Invoke this skill when the user asks to:
- Design an A/B test or controlled experiment for a product change
- Write a hypothesis and pick primary, secondary, and guardrail metrics
- Calculate required sample size or estimate how long a test must run
- Decide the unit of randomization (user, session, cluster, geo)
- Pressure-test or review an experiment plan before launch
- Build a reusable experiment design template for a team

## Inputs Required
Before starting, collect:
- **Product change**: What is being changed and why — the intervention being tested
- **Expected mechanism**: How the change is supposed to move user behavior
- **Baseline metrics**: Current value and daily/weekly volume of the metric expected to move (and traffic available)
- **Business goal**: The outcome the team ultimately cares about (revenue, retention, activation, etc.)
- **Minimum detectable effect**: The smallest effect worth shipping for — relative or absolute
- **Constraints**: Max acceptable runtime, risk tolerance, regulatory or UX limits
- **Population**: Where the change applies — all users, a segment, a surface, a geo

If MDE is missing, default to a relative 2% lift on the primary metric and flag it. If baseline volume is missing, request it before estimating runtime — runtime cannot be honest without it.

## Instructions

### Step 1: Frame the Hypothesis
Write a single falsifiable hypothesis in the form: *"Because [mechanism], [change] will cause [primary metric] to [direction] by at least [MDE] for [population]."*
- The hypothesis must name the causal mechanism — not just "we think this is better"
- It must be falsifiable: state what result would make you reject the change
- Separate the **business hypothesis** (what we believe about users) from the **measurement hypothesis** (what the experiment can actually detect)
- Call out the leap of faith — the single assumption most likely to be wrong

### Step 2: Choose the Unit of Randomization
Select and justify the randomization unit based on interference and metric alignment:
- **User-level**: Default. Use when treatment effects are independent across users and metrics are per-user
- **Session-level**: Use only when there is no cross-session learning and you accept contaminated within-user comparisons
- **Cluster/geo-level**: Use when network effects, marketplace spillover, or shared resources cause SUTVA violations — note the variance cost (fewer units = less power)
- **Switchback/time-based**: Use for systems with strong temporal interference (pricing, dispatch, ranking)

State explicitly: does the chosen unit risk **SUTVA violation** (one unit's treatment affecting another)? If yes, escalate to cluster or switchback design.

### Step 3: Build the Metric Tree
Define four tiers, no overlap:
- **Primary metric (exactly 1)**: The decision metric. Must be sensitive, attributable to the change, and move within the runtime window. Avoid lagging metrics (e.g., 90-day retention) as primary if runtime is short — use a validated proxy and label it as such
- **Secondary metrics (2–4)**: Explain *why* the primary moved or didn't — mechanism evidence
- **Guardrail metrics (2–4)**: Metrics that must NOT degrade (latency, error rate, revenue, complaints, churn). Define the degradation threshold that would stop the test
- **Counter-metrics**: Metrics that catch gaming or harmful trade-offs (e.g., engagement up but unsubscribes up)

For each metric: name it, give its current baseline, its expected direction, and whether it's a ratio metric requiring delta-method or clustered variance correction.

### Step 4: Estimate Sample Size and Runtime
Compute power, then translate to calendar time:
- Use **α = 0.05, power = 0.80** as defaults unless the user specifies otherwise
- Required sample per arm scales with baseline variance and inversely with MDE² — state the formula assumption (proportion vs. continuous metric)
- For ratio metrics, note that naïve variance underestimates and inflates false positives — recommend delta method or bootstrap
- Translate required sample into **runtime in days** using daily eligible traffic, and add buffer for weekly seasonality: round up to whole weeks
- Flag the **peeking problem**: if the team wants to monitor early, recommend sequential testing (e.g., always-valid p-values or group-sequential bounds) rather than fixed-horizon p-values checked daily

Output: required N per arm, estimated runtime in days/weeks, and the MDE actually detectable at that runtime if traffic is constrained.

### Step 5: Define Rollout and Allocation
- **Allocation**: Recommend split (e.g., 50/50) and justify — uneven splits cost power
- **Ramp plan**: Start at a small % to validate instrumentation and guardrails before full allocation
- **Pre-launch checks**: A/A test or SRM (sample ratio mismatch) check to confirm randomization is unbiased before trusting results
- **Stopping rules**: Define exactly when to stop early (guardrail breach) vs. when to wait for full runtime (efficacy)

### Step 6: Build the Risk Register (Ranked)
List 5–8 risks ranked by likelihood × impact. For each: the risk, why it threatens validity, and a specific mitigation. Cover at minimum:
- Novelty/primacy effects distorting early results
- Interference / SUTVA violation
- Underpowered design (MDE too small for available traffic)
- Multiple-comparison inflation across secondary metrics
- Instrumentation/logging gaps
- Heterogeneous effects masked by an average treatment effect

### Step 7: Decision Rule
State the explicit ship/no-ship/iterate logic *before* launch: what primary result ships, what guardrail breach blocks, and what ambiguous result triggers an iteration rather than a relaunch.

## Output Format
- **Structure**: Structured design document with labeled sections; metric tree and risk register as tables
- **Length**: 900–1,300 words
- **Format**: Sectioned document — prose for hypothesis and rationale, tables for metrics and risks, explicit numbers for sample size and runtime
- **Sections**: Hypothesis → Unit of Randomization → Metric Tree → Sample Size & Runtime → Rollout & Allocation → Risk Register → Decision Rule
- **Tone**: Senior experimentation specialist — precise, statistically literate, opinionated about validity threats, no hedging on the design call

## Quality Checklist
Before delivering output, verify:
- [ ] The hypothesis names a causal mechanism and states what result would reject it
- [ ] Exactly one primary metric is designated; guardrails have explicit degradation thresholds
- [ ] The unit of randomization is justified and SUTVA risk is explicitly assessed
- [ ] Sample size produces a concrete N per arm AND a runtime in days/weeks using the provided traffic
- [ ] The risk register has 5–8 risks, each ranked and each with a specific mitigation
- [ ] A decision rule is stated before launch — ship/no-ship/iterate is unambiguous

## Notes
- If runtime exceeds the user's max constraint, do not silently shrink it — report the detectable MDE at the allowed runtime and let the user decide whether the test is worth running
- The most common failure is choosing a primary metric that can't move within the runtime window; always check metric latency against runtime
- Recommend an A/A or SRM check as non-negotiable — a broken randomizer invalidates every downstream number
- For network-effect products (marketplaces, social, messaging), default skepticism toward user-level randomization and lean toward cluster or switchback designs

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## 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/<folder-name>/SKILL.md` — auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects)
- `<your-project>/.claude/skills/<folder-name>/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. Drag this file into a new chat, or copy and paste its contents
2. Send your task or request as your next message — the AI will apply the skill where it fits

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