Extract Jobs-to-be-Done statements from research data to uncover innovation opportunities.
4-6 hrs → 30 min
Compared to doing it manually
/jtbd-extractorType this in Claude to run the skill
Feature requests pile up, but they're solutions — not problems. Without extracting the underlying jobs, you build what users ask for, not what they need.
This skill is part of 3 workflows that automate multiple steps together:
.claude/skills/ folder in your project/jtbd-extractor in Claude to run the skillTransform interview transcripts into structured insights with quotes, patterns, and recommendations.
Analyze and categorize customer feedback into actionable themes using affinity mapping.
Extract themes, complaints, and feature requests from app reviews at scale.
Turn scattered feature requests into a prioritized list based on actual demand.
JTBD is a framework that focuses on the underlying goal customers are trying to achieve, rather than their demographic or the features they request. It helps you understand the "job" your product is "hired" to do.
Personas describe WHO your users are (demographics, behaviors). JTBD describes WHAT they're trying to accomplish. Both are useful — personas help you empathize, JTBD helps you prioritize what to build.
A job statement follows this format: "When [situation], I want to [motivation], so I can [expected outcome]." For example: "When I'm preparing for a stakeholder meeting, I want to summarize project status quickly, so I can appear prepared and earn trust."
Functional jobs are practical tasks (e.g., "send a message"). Emotional jobs are how users want to feel (e.g., "feel confident"). Social jobs are how users want to be perceived (e.g., "appear professional"). Great products address all three.
Download this skill and drop it in your .claude/skills/ folder.
This skill + 70+ more, context files, and agent workflows — $499