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Prd V10 Case Study Builder

Community
mattgierhart
prd-v10-case-study-builder

Build customer case studies as marketing and chasm-crossing reference assets during PRD v1.0 Market Adoption. Triggers on requests to build case studies, produce customer stories, create reference content, or when user asks "build a case study", "customer story", "reference account", "case study interview", "before/after story", "social proof page", "logo wall". Outputs CFD-CASE-* evidence entries, GTM-CASE-* marketing assets, and updates ADO-REF-* with story status.

Overview

Publishermattgierhart
RepositoryPRD-driven-context-engineering
Skill nameprd-v10-case-study-builder
Stars
179
Forks
11
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by mattgierhart on GitHub. Read the source before you install it.

Installation

Install the Prd V10 Case Study Builder AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/mattgierhart/PRD-driven-context-engineering.git /tmp/PRD-driven-context-engineering
mkdir -p .claude/skills
cp -r /tmp/PRD-driven-context-engineering/plugins/prd-ce/skills/prd-v10-case-study-builder .claude/skills/prd-v10-case-study-builder
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Prd V10 Case Study Builder in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Prd V10 Case Study Builder on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Prd V10 Case Study Builder is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Case Study Builder

Position in workflow: v1.0 Mom Test Interview → v1.0 Case Study Builder → v1.0 Testimonial Collector, GTM channels

Execution Mode

Default is standard. See .claude/rules/08-skill-execution-modes.md for selection logic.

ModeWhat this skill produces
quickShort-format case (testimonial + 1-paragraph story + logo); single placement
standardFull case study (1,500 words) + short and medium derivatives + customer-approved + 3 channel placements
deepLong-form case (2,500–4,000 words) + multi-format derivatives (PDF, blog, video, conference talk) + measurable outcome quantified + outcome-attribution interview

What This Does

Turns customer success — already documented as CFD-* evidence and ADO-REF-* candidates — into structured case studies. Case studies are the pragmatist buyer's #1 reference signal: they need to see a customer in their segment achieving the outcome they want, with enough detail to make it credible.

This is an operational "doing" skill, not a strategy skill. The strategic question (which segment, what story angle, what outcome to highlight) was answered by prd-v10-chasm-adoption-moore. This skill produces the artifact.

How It Works

  1. Identify candidate customers — Pull from ADO-REF-* candidates and CFD-* entries with strong outcome quantification. Must satisfy:
    • In-beachhead (or in an adjacent segment where the story would still resonate)
    • Has a quantifiable outcome to talk about
    • Willing to be public (consent path is clear)
  2. Run the case-study interview — Extended Mom Test interview (45–60 min), focused on:
    • Before-state (specific past: what life was like, what tools, what cost)
    • Trigger (what changed; why they evaluated alternatives)
    • Evaluation (who they considered; how they chose)
    • Implementation (what happened in onboarding; what was hard)
    • After-state (specific present: quantified outcome, time/money saved, what they can do now they couldn't)
  3. Structure the story — Situation → Complication → Question → Resolution (the McKinsey SCQR pattern):
    • Situation: where they were before
    • Complication: what broke / changed / hit a wall
    • Question: what they needed to figure out
    • Resolution: how they solved it (your product as one part of the answer, ideally credibly)
  4. Quantify outcome — Specific numbers beat vague claims:
    • "Cut tier-selection time from 3 days to 30 minutes" beats "much faster"
    • "Saved $12k/year on tool consolidation" beats "saves money"
    • "85% activation rate (industry average 35%)" beats "great activation"
  5. Get customer review — Send draft to customer for accuracy + tone + legal. Iterate until approved.
  6. Produce in 3 formats:
    • Short (testimonial — 1–2 sentences + name + role + logo): for landing pages, pricing page, ad creative
    • Medium (1-paragraph + 3 bullet outcomes + logo + linked deeper): for "Customers" page, social, email
    • Long (1,500–2,500 word case study): for blog, sales enablement, sales call followup

Example

Customer: Acme Logistics (beachhead segment: freight forwarders, 50-200 employees, US PNW). ADO-REF-002.

Interview: 50 minutes with Acme's VP Operations.

Quantified outcome:

  • Tier-selection time: 3 days → 30 min (90% reduction)
  • Annual savings from tool consolidation: $14k
  • Onboarding time per new hire: 2 weeks → 3 days (after migrating to product)

SCQR:

  • Situation: Acme used 4 disjointed tools to track shipments, with manual export every Friday for ops reporting
  • Complication: Two new hires + a Q3 customer surge meant the manual export bottlenecked the team; ops director was working Saturdays to keep the dashboards current
  • Question: How could Acme consolidate tooling without disrupting an in-flight Q4 customer onboarding?
  • Resolution: Acme migrated to [product] over 3 weeks; consolidated 4 tools to 1; automated the Friday export. VP Operations reclaimed 6 hours/week. New hires onboard in 3 days instead of 2 weeks.

Three formats produced:

  • Short: "We replaced 4 tools with [product] and cut new-hire onboarding from 2 weeks to 3 days." — [Name], VP Ops, Acme Logistics
  • Medium: 1-paragraph + 3 bullet outcomes + Acme logo + "Read the full story →"
  • Long: 1,800-word blog case study with screenshots, quotes, before/after metrics, and an embedded testimonial video.

What You Get Back

  • CFD-CASE-* entries — One per case-study interview; the durable evidence record
  • GTM-CASE-* entries (one per produced format) — Marketing assets with placement plan
  • Updates to ADO-REF-* entries — Reference status: "draft pending" → "approved" → "published"
  • Customer approval record — Signed approval + version-controlled draft history

When to Use It

TriggerMode
First reference customer in beachhead is ready to talkstandard
Chasm-crossing push needs 3+ in-segment case studiesdeep
Pricing-page logo wall expansionquick (short format only)
Sales enablement asset for new segmentstandard
Investor / partner updatestandard
Customer success milestone (e.g., 1-year anniversary)quick

Do not use for: customers outside beachhead during chasm crossing (their story doesn't resonate with pragmatists); customers without consent (legal risk); customers without quantifiable outcomes (story will feel vague).

Consumes

  • CFD-* customer evidence (Mom Test discipline) — Source for candidate selection
  • ADO-REF-* candidates (from prd-v10-chasm-adoption-moore) — Cultivated reference targets
  • ADO-BEACHHEAD-* — Defines "in-segment" for case priority
  • KPI-* outcomes — Defines what "outcome" means; case quantification must speak to these
  • GTM-* positioning + offer — Tone, voice, and value claims must match positioning
  • BR-POS-* — Constraints (e.g., "no enterprise procurement language")

Produces

  • CFD-CASE-* entries in SoT/SoT.customer_feedback.md
  • GTM-CASE-* entries (with Type=Case-Asset) for each produced format
  • ADO-REF-* updates — Status, placement, consent tracking
  • CFD-* gaps surfaced — If outcome can't be quantified, log as research gap

Output Template

CFD-CASE-XXX: Case Study — [Customer Name]
Type: Case-Study
Date: YYYY-MM-DD
Customer: [Logo / company name]
Customer fit: [In-beachhead | Adjacent segment]
Interview length: [minutes]
Interviewer: [Name]

Quantified outcome (the proof):
  Before: [Specific number / state]
  After: [Specific number / state]
  Delta: [%change or $ value]
  Time horizon: [over what period]

Story (SCQR):
  Situation: [Where they were before]
  Complication: [What changed / broke / hit a wall]
  Question: [What they needed to figure out]
  Resolution: [How it got solved — your product as part of the answer]

Quotes (verbatim, approved):
  - "[Quote]" — [Name, Role]
  - "[Quote]" — [Name, Role]

Customer approval:
  Approved by: [Name, Role]
  Approved version: vX
  Date: YYYY-MM-DD
  Scope: [logo only | quote | full case study | on-stage]

Linked formats:
  Short: GTM-CASE-AAA
  Medium: GTM-CASE-BBB
  Long: GTM-CASE-CCC

Linked IDs: ADO-REF-XXX (cultivation), PER-YYY (segment fit), KPI-ZZZ (outcome), CFD-AAA (interview source)
GTM-CASE-XXX: Case-Study Asset — [Format]
Type: Case-Asset
Format: [Short | Medium | Long]
Channel: [Landing page | Pricing page | Blog | Sales deck | Email | Ad creative]
Owner: [Person / role]
Status: [Draft | In review | Approved | Live]

Content: [The actual asset content — quote, paragraph, full case]

Placement plan:
  - [URL or section, e.g., "/pricing — Acme logo wall, position 3"]

Attribution: utm_campaign=case-<customer>

Linked IDs: CFD-CASE-AAA (parent case), ADO-REF-BBB (reference account), GTM-YYY (positioning)

Anti-Patterns

PatternSignalFix
No quantified outcome"Customer loves us!"Specific numbers or skip; vague cases don't move pragmatists
Wrong segmentFeaturing a startup case to a pragmatist enterprise audienceIn-segment cases only during chasm crossing
No customer approval on recordPublished with assumed approvalAlways get signed approval; preserve version history
Marketing voice overrideCustomer's actual words rewritten into your marketing toneTheir voice; light edits for clarity only
Single-format publishLong-form blog onlyThree formats: short for placement, medium for browsing, long for depth
One-and-doneCase study published, never updatedRefresh quarterly with new outcome data; cases stale fast
Featuring failures-rebranded-as-wins"We rebuilt their entire infrastructure" hides "we replaced a failed implementation"Don't dress up bad stories; pragmatists smell it

Quality Gates

Before publishing:

  • Customer in-beachhead or rationale for cross-segment placement
  • Outcome quantified with specific numbers
  • SCQR structure complete (Situation, Complication, Question, Resolution)
  • Verbatim quotes with name + role
  • Customer approval on record (scope explicit)
  • Three formats produced (short, medium, long) — or rationale for fewer
  • Placement plan per format
  • Voice matches GTM- positioning (and customer's actual words)
  • BR-POS-* constraints honored

Downstream Connections

ConsumerWhat it usesExample
Launch Channels (ORB)Cases become Owned and Borrowed channel contentBlog case = Owned; co-marketing case = Borrowed
Alternatives PagesCases anchor "when to pick us" sections with real proofPage references Acme case for migration-from-X story
Cold Outreach (Tiered)Cases referenced in Touch 3 of sequencesTier 1 outreach mentions in-segment case study
Testimonial CollectorShort-format derivatives are testimonial-shapedAuto-eligible for testimonial walls
AEO AuditCases become AI-citation sourcesHigh-traffic case pages cited by AI search
Sales / InvestorDirect asset for sales calls and investor updates"Here's how Acme uses [product]"

Detailed References

  • BrianRWagner's case-study-builder skill (ai-marketing-claude-code-skills)
  • Joe Pulizzi, Epic Content Marketing (case study as content)
  • Annette Franz, Customer Understanding (interview discipline)
  • McKinsey's SCQR (Situation/Complication/Question/Resolution) framework
  • (No bundled references/ — the case study itself is the artifact)

Frequently asked questions

What does the Prd V10 Case Study Builder AI skill do?

Build customer case studies as marketing and chasm-crossing reference assets during PRD v1.0 Market Adoption. Triggers on requests to build case studies, produce customer stories, create reference content, or when user asks "build a case study", "customer story", "reference account", "case study interview", "before/after story", "social proof page", "logo wall". Outputs CFD-CASE-* evidence entries, GTM-CASE-* marketing assets, and updates ADO-REF-* with story status.

Why use Prd V10 Case Study Builder on TypingMind?

Because you install it once and use it with any model. Prd V10 Case Study Builder is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Prd V10 Case Study Builder in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mattgierhart/PRD-driven-context-engineering/tree/main/plugins/prd-ce/skills/prd-v10-case-study-builder. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Prd V10 Case Study Builder?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Prd V10 Case Study Builder?

As many as you like. As long as a model supports skills, you can use Prd V10 Case Study Builder with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Prd V10 Case Study Builder AI skill free?

Yes. It is published on GitHub by mattgierhart under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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