Evidence-Based Customer Case Study Composer
Create credible customer case studies from interviews, metrics, timelines, and approved evidence without inventing outcomes, quotations, or causation.
Evidence-Based Customer Case Study Composer is a free AI skill for writing studio. Create credible customer case studies from interviews, metrics, timelines, and approved evidence without inventing outcomes, quotations, or causation. It works with Claude, ChatGPT and is ready to use out of the box.
About this skill
Transform raw customer evidence into a clear case study with problem, context, solution, implementation, results, limitations, and approved quotations. The skill separates measured outcomes from interpretation and produces both long-form and concise versions.
What it does
It audits the evidence, structures the story, identifies unsupported claims, drafts a credible narrative, and creates an approval checklist.
What is included
- Evidence inventory
- Claim-to-source matrix
- Customer context and challenge
- Solution and implementation narrative
- Results and limitations
- Quotation review
- Long-form case study
- Short promotional summary
- Approval checklist
- Repurposing ideas
How to use it
1. Download the evidence-based-customer-case-study-composer-SKILL.md file 2. Upload it to Claude, ChatGPT, or your content workspace 3. Provide interview notes, approved metrics, timeline, implementation details, and quotation permissions 4. Remove confidential customer information that is not needed 5. Obtain customer and internal approval before publication
Examples
Use the supplied interview transcript, onboarding timeline, and verified support metrics to draft a B2B SaaS case study. Do not claim revenue impact because no revenue data was measured.
An evidence map, unsupported-claim warnings, a long-form case study, approved quotation placeholders, measurable results, limitations, a short summary, and an approval checklist.
FAQ
Can this skill invent a customer quote to improve the story?
Can it write a case study without numeric results?
How are unsupported claims handled?
Can it anonymize the customer?
Does it distinguish correlation from causation?
How is this different from a testimonial writer?
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