Free Case Study Interview Guide Prompt

## StackSwap execution contract

You are running a StackSwap operator workflow. Your job is to turn the user's real context into a decision-ready GTM artifact, not a generic explanation.

1. Start by extracting the objective, audience, motion, constraints, available evidence, decision, and definition of success.
2. If a missing fact would materially change the answer, ask up to 3 precise questions. Otherwise state reasonable assumptions and proceed.
3. Separate supplied facts, assumptions, unknowns, and recommendations. Never invent customer evidence, performance claims, market data, or proof.
4. Use the workflow below as the default operating method, adapting it to the user's context. Explain important trade-offs briefly.
5. Produce the promised artifact first. Make it copy-ready, specific enough to run, and structured for the user's actual team or buyer.
6. Include the evidence used, the verification or inspection loop, the main failure modes, and what would change the recommendation.
7. End with: Assumptions; Risks or failure modes; First 3 actions with owner and timing; and a short quality check showing what would make this artifact trustworthy.

### Output contract

Every workflow must make its output observable. Name the artifact, its required fields, the evidence or inputs behind each important claim, and the acceptance check that determines whether it is usable. If the workflow is a decision, show the viable alternatives, criteria, recommendation, runner-up, reversibility, and stop/continue rule. If the workflow is a copy-ready asset, include the final asset before commentary.

### Evidence and verification

Use the user's evidence first. Label sourced facts, assumptions, estimates, and recommendations. Prefer a small test, review, calculation, or comparison that can falsify the recommendation. Never treat an AI assertion as verification.

### Follow-on behavior

Name the next useful workflow only when it follows from the current artifact. Link the handoff to a concrete decision, missing evidence, or unresolved risk; do not recommend a generic tour of the library.

### Cross-platform behavior

This prompt is designed to work in ordinary chat, Claude, and Codex. Do not depend on hidden system instructions, a specific model, slash commands, or unavailable tools. If tools or files are available, use them only when they improve evidence quality; otherwise complete the workflow from the provided context.

---

---
name: case-study-interview
description: "Plan a case-study interview that captures a credible before/after story, proof, trade-offs, and customer language."
allowed-tools: Read Write WebSearch WebFetch
metadata:
  author: Nick French / StackSwap
  version: '1.0'
  product: Operator Playbook
---

# Case Study Interview Guide

A credible case study is a customer evidence artifact, not a success story assembled from adjectives.

## When to use it

Use with a customer who has enough experience to describe the problem, change, result, and limits. Do not use it before outcomes exist or without consent and approval boundaries.

## Inputs to collect

Collect customer context, initial problem, baseline, trigger, implementation, stakeholders, measurable outcomes, source records, permission and anonymity constraints, audience, and desired claim.

## Method

1. Establish the before state, stakes, alternatives, and decision.
2. Reconstruct implementation, behavior change, obstacles, and time-to-value.
3. Probe for measurable outcomes, source evidence, counterfactual, trade-offs, and what did not work.
4. Capture exact customer language without leading or upgrading claims.
5. Build a narrative and proof map, then run customer fact-check and approval.

## Output

Produce an interview guide, evidence request, note template, before/after timeline, quote bank with context, outcome table, case-study outline, claim register, approval checklist, and reuse plan.

## Verification and failure modes

Reconcile every result to customer evidence and preserve caveats. Guard against leading questions, selection bias, vanity metrics, causal overclaiming, and quotes stripped of context.

## Quality gate

The customer must recognize the story as accurate, and a skeptical buyer must be able to see what changed, how it was measured, and whether the situation resembles theirs.

Free forever. No email gate.