Customer Case Study Story Editor
Turn customer interviews and project evidence into credible case studies with problem, approach, outcomes, proof, quotations, and approval controls.
Customer Case Study Story Editor is a free AI skill for client workflows. Turn customer interviews and project evidence into credible case studies with problem, approach, outcomes, proof, quotations, and approval controls. It works with Claude, ChatGPT and is ready to use out of the box.
About this skill
Turn customer interviews and project evidence into credible case studies with problem, approach, outcomes, proof, quotations, and approval controls. It applies a structured editorial workflow, preserves source integrity, and produces a review-ready deliverable with explicit risks and verification needs.
What it does
The skill analyzes the source material, audience, purpose, evidence, structure, tone, terminology, and publication constraints; identifies high-impact editorial issues; makes traceable revisions; and produces a professional output with approval and verification notes.
What is included
- Case study context
- Customer and audience
- Problem framing
- Approach and implementation
- Outcome evidence
- Quotation selection
- Narrative draft
- Proof and approval gaps
How to use it
1. Download the customer-case-study-story-editor-SKILL.md file 2. Upload it to your writing or editorial workspace 3. Provide the source material, audience, purpose, and constraints 4. Add protected wording, evidence, approvals, and publication requirements 5. Use the final output for review, approval, or publication
Examples
Create a case study from project notes and a customer interview without exaggerating the results or inventing metrics.
A complete professional editorial deliverable with revised content, issue priorities, source and evidence safeguards, approval needs, change rationale, and final review criteria.
FAQ
What is this skill for?
Will it invent facts, quotations, or results?
Can it preserve required wording?
Does it explain the changes?
Can it work with long documents?
How is this different from generic rewriting?
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