case-study-extractor

name	case-study-extractor
description	Extract and structure credible customer case studies from raw notes, transcripts, and outcome data. Use when asked to turn messy source material into customer stories, proof points, or enablement-ready case studies. Route broad feedback synthesis to field-feedback-synthesizer and slide packaging to one-pager-and-slide-outline.

Case Study Extractor

Convert raw customer evidence into a concise customer story that revenue teams can reuse confidently.

Confirm Inputs First

• If a current revenue-enablement-context exists, use it first for ICP framing, persona naming, and proof-quality expectations.
• Confirm customer scope and permission constraints.
• Confirm source materials available.
• Confirm measurable outcomes and baseline context.
• Confirm primary audience and usage context.
• Confirm whether anonymization is required.
• If inputs are incomplete, ask for only the minimum missing items or proceed with labeled assumptions.

Read The Right Reference

• Read references/case-study-template.md for structure, evidence grading, and quote handling.

Default Workflow

1. Check the source and permission. Make sure the material is usable and approved for the intended audience.
2. Pull the story arc. Capture the starting point, challenge, approach, results, and why it mattered.
3. Separate proof from claims. Keep measured outcomes distinct from directional language.
4. Add the context that makes the result believable. Include implementation details, timeline, and any adoption or rollout notes that matter.
5. Grade the evidence. Keep strong proof points and remove weak or unsupported claims.
6. Set the privacy mode. Decide whether the story is named, anonymized, or internal-only.
7. Draft the usable versions. Produce a short proof snippet and a fuller case summary.
8. Route adjacent work: For cross-account pattern synthesis, use field-feedback-synthesizer. For one-pager or slide packaging, use one-pager-and-slide-outline.

Tool Notes

• Optional tools may improve data quality: CRM exports, product analytics, customer success notes, and transcript tools.
• If tool access is unavailable, proceed with explicit evidence grading.

Output Contract

• Case narrative with clear before-after structure.
• Outcome table with metric, baseline, current value, and timeframe.
• Quote shortlist with context notes.
• Evidence-grade notes for each major claim.
• Publication status: draft, approved, restricted.
• Anonymization profile and disclosure constraints.
• Assumptions list when source inputs were incomplete.

What To Avoid

• Treating directional anecdotes as quantified outcomes.
• Publishing customer evidence before approval status is explicit.
• Dropping anonymization details that affect safe reuse.

Quality Bar

• Story is outcome-led and commercially relevant.
• Claims are traceable to source evidence.
• Baseline and timeframe are explicit for quantified results.
• Weak claims are downgraded or removed.
• Narrative is reusable by sellers without major edits.