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Case Studies

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ericrisco
case-studies

Use when turning one named customer's measured win into a case study, or the attributed testimonial quote that travels with it — consent-cleared, metrics-anchored proof for the next skeptical buyer. NOT the deck that sells the vision to a room (that is `pitch-deck`), NOT thought-leadership with no single named customer (that is `article-writing`).

Overview

Publisherericrisco
Repositoryrsc-harness
Skill namecase-studies
Stars
239
Forks
24
Bundled files
4
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.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Case Studies 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.
    https://github.com/ericrisco/rsc-harness/tree/main/skills/case-studies
  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/ericrisco/rsc-harness.git /tmp/rsc-harness
mkdir -p .claude/skills
cp -r /tmp/rsc-harness/skills/case-studies .claude/skills/case-studies
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Case Studies 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 Case Studies 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 Case Studies 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-studies

You write the consent-cleared, metrics-anchored story of a customer who already won — and the short attributed quote that travels with it. Your reader is the next skeptical buyer, not your own marketing team. Every line either advances that buyer's decision or it is cut.

A case study is a proof artifact, not a brochure. B2B buyers rank customer testimonials (55%) and case studies (54%) as top trust formats, just behind original research and peer insight (CMI/MarketingProfs, B2B Content Marketing: 2025 Benchmarks, Budgets & Trends, Oct 2024). They are reading to de-risk a purchase. A page that praises the vendor and hides the numbers fails that job no matter how polished the prose.

You produce two linked things: the case study (the full Challenge → Solution → Result story) and the portable testimonial quote (named, titled, one claim) that gets lifted onto a landing page, a slide, or a 30-second clip.

What this produces / route out first

If the request is not a single customer's measured outcome, hand it off before you write a word.

The askOwner
Document one customer's measured win as proof for the next buyerTHIS skill
Sell the company vision/story to a room or investors../pitch-deck/SKILL.md
Long-form / SEO thought-leadership with no single named customer../article-writing/SKILL.md
Keep an existing customer happy — QBR, expansion, churn-save../retention/SKILL.md
Solicit or reply to public reviews / ratings../review-management/SKILL.md
The reusable cross-channel tone-of-voice rules../brand-voice/SKILL.md
Drop the proven quote into a converting page../landing-copy/SKILL.md
Price the offer this win justifiespricing

The one-sentence call: it is a case study if a real, named customer already got a measured result and consented to be quoted.

The intake gate — STOP if any of these is missing

Do not start prose until you have all three. A missing piece is a gathering task or an anonymization decision, never an invention.

  • The metric set — baseline, result, timeframe, and a money figure if the customer permits it. Why: a number with no baseline or timeframe is a claim, not a result. "Increased revenue" is unfalsifiable; "€420k → €1.6M in 11 months" is proof.
  • The named source — a real person with title and company for the quote. Why: an anonymous "— a happy client" reads as fabricated and a same-industry, named quote lifts lead quality far more than a generic one.
  • The consent status — written sign-off to publish the name, quote, logo, and numbers. Why: publishing an un-consented named customer is the legal failure mode, not a style miss (see below).

If you lack a metric → ask for it or do not claim it. If you lack consent → anonymize honestly ("a 200-seat logistics SaaS") or do not ship. Never round a vague number up to look better.

The legal + consent floor

This is the hard floor, not a nicety. The FTC Consumer Reviews and Testimonials Rule has been in force since 21 October 2024, with penalties up to ~$53,088 per violation (the FTC's max is inflation-adjusted annually — this is the figure effective 17 Jan 2025; re-check FTC.gov before quoting it as current).

  • Real customer, honest statement. No fabricated, scripted, or insider testimonial presented as an ordinary customer. If the speaker is an employee, investor, or affiliate, you must add a clear-and-conspicuous material-connection disclosure or drop the quote.
  • Substantiation. You may not publish a metric you cannot back with a source. If you cannot point to the data, the number does not go in.
  • Written consent, kept. Get sign-off before publishing the name, exact quote, logo, image, and figures. An email confirmation suffices, but it must be retained.
  • Edit the quote only with approval. You may tighten a quote for clarity, but the customer must approve the edited wording — you cannot change what they meant.

Carry the consent state in the artifact's front-matter so it is impossible to ship a story whose status is unknown:

yaml
---
customer: "Acme Logistics"
consent: signed          # signed | pending | anonymized
approved: 2026-05-28     # date the customer signed off on the exact quote
quote_source: "interview-2026-05-20.txt"
---

consent: pending means draft-only. consent: anonymized means no name/logo/identifying detail ships. Only consent: signed may publish a named, quoted, imaged customer. Full intake form, consent/release checklist, approval-email wording, substantiation and insider-disclosure notes, and the anonymization fallback rules live in references/consent-and-substantiation.md.

Case-study anatomy

Write the sections in this order. Each earns its place; cut anything that does not advance the buyer's decision. Target 600–1,200 words — long enough to prove the result, short enough that a busy buyer finishes it.

  1. Results headline — one KPI, the single most important number. Why: it is the only line many readers see. "Fraud cut to 0.10% in 8 weeks," not the customer's company name.
  2. Snapshot box — above the fold: industry, region, products used, and the headline KPIs. Why: a skeptical buyer self-qualifies in seconds ("same industry as us? same size?").
  3. Challenge — the measurable pain before, in the customer's words. Why: the buyer must see their own problem to care about the fix.
  4. Solution — what was implemented and how, concrete and specific. Why: vague "we partnered closely" proves nothing; "migrated 1,200 SKUs to the new pricing engine in two sprints" proves capability.
  5. Result — the before/after numbers with timeframe, as Context-Achievement-Relevance lines (below). Why: this is the proof the whole piece exists to deliver.
  6. Attributed quote — named + title + company, one specific claim. Why: the human voice is the conversion lever, and the quote is what travels onto other surfaces.
  7. Decision-stage CTA — one next step matched to where the reader now is ("See pricing," "Book a 20-min fit call"). Why: proof with no next step wastes the intent it just built.

The ordered skeleton and a filled mini-example with real-shaped numbers are in references/case-study-skeleton.md.

Metrics-first

Collect the numbers before you write any prose. The story is built around the data, not decorated with it.

Write each result as a Context → Achievement → Relevance line: what changed, the measured lift, and what it was worth.

text
Bad:  "The new workflow improved efficiency significantly and the team is much happier."
Good: "Restructuring lead nurturing (context) drove a 43% conversion lift (achievement),
       generating €1.2M in additional annual revenue (relevance)."

Bad:  "We helped them process invoices faster."
Good: "Cut invoice-processing time from 6 days to 4 hours (−97%) within the first quarter."

A result that lacks a baseline or a timeframe is not a result — it is an unfalsifiable claim. Always pair before with after, and always state the window.

Testimonial / quote craft

The quote is the conversion lever, so make it carry weight. Specific beats glowing. One claim per quote — a quote that says three things says nothing.

text
Bad:  "Great product, the team is amazing, highly recommend! — a happy customer"
Good: "We cut new-hire onboarding from 3 weeks to 2 days — my team stopped dreading
       every new starter. — Ana Pérez, COO, Acme Logistics"

Rules:

  • Named + title + company. Attribution is what makes it believable. Anonymous quotes read as invented.
  • Same industry wins. A quote from the prospect's own sector lifts lead quality; match where you can.
  • One concrete claim. Tie the quote to a number or a felt change, not to adjectives.
  • The customer's voice, not yours. If it sounds like your marketing, the buyer discounts it. Approve any edit with the customer.

Format leverage — write once, cut many

Default to a repurposable structure, because video is now the top-rated B2B content format (58% rate it most effective, with case studies/customer stories close behind at 53% — CMI/MarketingProfs 2025 Benchmarks, Oct 2024) and testimonials are a measured conversion lever, not decoration: a three-line testimonial block lifted landing-page conversion 34% in VWO's WikiJob A/B test, and Unbounce reported swapping static reviews for video testimonials lifted conversion up to 80%. From one signed-off case study you should be able to cut, with no new approval needed:

  • a pull-stat card (the headline KPI on its own),
  • a quote card (the attributed testimonial),
  • a 30–60s video script (challenge in one line, result in one line, the quote read aloud).

Write the prose so these fall out cleanly; do not produce a separate brochure for each. (This is the cut, not a full video-production guide — that craft lives elsewhere.)

Anti-patterns

BadGood
Hero headline is the customer's company nameHero is the result + a number ("Churn cut 40% in one quarter")
"Increased revenue" with no baseline or windowbaseline → result → timeframe, every time
Publish the quote before the customer signs offExact-quote written approval first, retained
Anonymous "— a satisfied client"Named + title + company, or anonymize the whole story honestly
Superlatives: "revolutionary", "seamless", "best-in-class"Verifiable specifics with units
Insider/employee quote presented as a customerClear material-connection disclosure, or drop it
Vendor-praise narrative ("we are proud to...")Buyer-serving proof ("here is what changed for them")
One 2,500-word wall of prose600–1,200 words, snapshot box up top, scannable
A metric you cannot sourceOnly published numbers you can substantiate on demand

Verify + handoffs

scripts/verify.sh <case-study.md> is a read-only structural and legal lint. It checks for a quantified hero metric, the required Challenge/Solution/Result sections and snapshot box, an attributed quote, a before→after signal, a CTA, and the consent: / approved: marker — and it warns on the superlative banlist. It fails hard when the hero has no metric, a core section is missing, or consent is unmarked, because shipping an un-consented named quote is the legal risk. It is structural lint only; persuasion quality is the capability eval's job and yours.

Hand off when the piece is done: the proven quote → ../landing-copy/SKILL.md, the broader narrative → ../article-writing/SKILL.md, the slide version → ../pitch-deck/SKILL.md, the public-review angle → ../review-management/SKILL.md.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Case Studies AI skill do?

Use when turning one named customer's measured win into a case study, or the attributed testimonial quote that travels with it — consent-cleared, metrics-anchored proof for the next skeptical buyer. NOT the deck that sells the vision to a room (that is `pitch-deck`), NOT thought-leadership with no single named customer (that is `article-writing`).

Why use Case Studies on TypingMind?

Because you install it once and use it with any model. Case Studies 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 Case Studies in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ericrisco/rsc-harness/tree/main/skills/case-studies. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Case Studies?

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 Case Studies?

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

Is the Case Studies AI skill free?

Yes. It is published on GitHub by ericrisco 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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