git:20260326.a6cd2e8 to git:20260707.40eb9b3

---

name: kai-case-study

description: Produce customer case studies from interviews or data — Problem, Solution, Results structure with perception engineering and quality gates. Use when "case study", "customer story", "testimonial", "success story", "client results", or any request to document a customer win.

---

# Kai Case Study Skill

Produce compelling customer case studies using a Problem, Solution, Results structure with perception engineering layers.

---

## Phase 0: Load Product Context

Check if `MARKETING.md` exists in the **project root** (same directory as CLAUDE.md, README.md, package.json).

**If it exists:** Read it — skip product discovery questions. It has the product name, ICP, value prop, monetization, brand voice, current channels, and competitive landscape.

**If it does NOT exist:** Auto-explore the codebase to create it in the **project root** (next to CLAUDE.md). Do NOT ask the user what the product is. Read CLAUDE.md, README.md, PROJECT.md, package.json, landing pages, and any project files. Search for email/ad/analytics config. Then create `MARKETING.md` using the template from `/kai-email-system`. Present draft to user for confirmation.

---

## Phase 1: Discovery

Read from `MARKETING.md`. Only ask about things not covered there:

1. **Customer info** — Company name, industry, size, role of contact

2. **Source material** — Interview transcript, survey responses, data points, screenshots

3. **The problem** — What was broken before? Quantify the pain.

4. **The solution** — What did we do? Be specific about the product/service.

5. **The results** — Hard numbers. Revenue, time saved, conversion lift, cost reduction.

7. **Permission** — Does the customer approve named use? Or anonymized?

---

## Phase 2: Plan

Structure the case study:

3. **Define the narrative arc**:

- **Before state** — The specific pain, in the customer's words

- **Turning point** — Why they chose us (decision trigger)

- **After state** — Measurable transformation

4. **Key quote selection** — Pull 2-3 direct quotes that carry emotion + specificity

5. **Proof points** — List every number, metric, and data point available

6. **Distribution plan** — Where will this live? (website, sales deck, email, social)

---

## Phase 3: Produce

Write the case study:

1. **Headline** — Lead with the result, not the company name. Example: "73% Faster Onboarding: How [Company] Rebuilt Their Workflow"

2. **Snapshot box** — Company, industry, challenge, result (scannable summary)

3. **The Challenge** — 2-3 paragraphs. Paint the before state. Use customer language.

4. **The Solution** — 2-3 paragraphs. What we did, how it worked. Be concrete.

5. **The Results** — Lead with the biggest number. Use a data table or callout boxes.

6. **Customer quote** — Close with their strongest testimonial line.

7. **CTA** — What should the reader do next?

Apply perception engineering layers:

- **Perception layer**: Re-index the old way as the problem (not just "less good")

- **Context layer**: Make the new approach feel inevitable

- **Permission layer**: Remove risk from taking action

---

## Phase 4: Quality Gates

Run all gates before delivery:

- Minimum: **12/16** (content threshold)

- Zero Tier 1 violations

3. **AI Slop Check** — No filler phrases ("In conclusion", "It's worth noting that", etc.)

4. **Specificity check** — Every claim has a number or named example. No vague praise.

Max 2 auto-retry cycles. After 2 failures, surface to human with specific failure reasons.

---

## Phase 5: Output

Deliver the final case study package:

- **Full case study** (long-form, 800-1500 words)

- **One-page summary** (for sales team, 250 words max)

- **Pull quotes** (2-3 standalone quotes for social/email use)

- **Headline variants** (3 options for different channels)

- **Four U's scorecard**

- **Gate pass/fail summary**