jobs-to-be-done
This Claude Code skill maps user Jobs-to-Be-Done across functional, emotional, and social dimensions to uncover deeper motivations behind user behavior. Use it when analyzing interview data or product context to reframe design decisions around what users are fundamentally trying to accomplish rather than focusing on product features alone.
git clone --depth 1 https://github.com/Owl-Listener/designer-skills /tmp/jobs-to-be-done && cp -r /tmp/jobs-to-be-done/design-research/skills/jobs-to-be-done ~/.claude/skills/jobs-to-be-doneSKILL.md
# Jobs-to-Be-Done Map user Jobs-to-Be-Done to understand the deeper motivations behind user behavior. ## Context You are a UX researcher applying the JTBD framework for $ARGUMENTS. If the user provides files (interview data, product context), read them first. ## Domain Context - JTBD (Clayton Christensen, Tony Ulwick): People hire products to get a job done — focus on the job, not the product. - Three dimensions: Functional (practical task), Emotional (how they want to feel), Social (how they want to be perceived). - Job statements follow the format: When [situation], I want to [motivation], so I can [expected outcome]. ## Instructions 1. **Identify the core job**: What is the user fundamentally trying to accomplish? 2. **Map the job dimensions**: - **Functional**: The practical task or outcome - **Emotional**: The feeling they seek or want to avoid - **Social**: How they want to be perceived by others 3. **Define job stages**: Map the full job lifecycle (define, locate, prepare, confirm, execute, monitor, modify, conclude). 4. **Identify outcome expectations**: What does success look like for each dimension? 5. **Map current solutions**: How do users currently "hire" products for this job? 6. **Find opportunities**: Where are current solutions underserving the job? 7. Present JTBD mapping in a structured format with clear design implications.
Design an A/B experiment — hypothesis, variants, primary metric, and sample size. Use when a change can be measured quantitatively at scale. For observing behaviour qualitatively, use `test-scenario`.
Audit an existing interface against WCAG, producing findings with severity ratings and remediation steps. Use when you have a design or build to assess now. Not for planning future sessions with assistive-technology users — use `accessibility-test-plan` (prototyping-testing).
Plan accessibility testing — assistive technologies, participant criteria, WCAG coverage, and session protocol. Use when scheduling testing with real AT users. Not for evaluating a design yourself — use `accessibility-audit` (design-systems).
Apply the Aesthetic-Usability Effect — polished, consistent interfaces are perceived as more usable and forgive minor friction. Use when justifying visual polish or diagnosing why a functional design tests badly. For emotional resonance specifically, use `interfaces-that-feel` (interaction-design).
Cluster many qualitative data points into themes and insight statements. Use when synthesising across multiple sessions or sources. For a single transcript use `summarize-interview`; for one segment's inner state use `empathy-map`.
Apply animation principles — easing, staging, follow-through — to one specific UI motion. Use when tuning how an animation feels. For product-wide duration and easing tokens use `motion-system` (design-systems); for a full interaction spec use `micro-interaction-spec`.
A practitioner's toolkit for thinking and communicating as a designer in a business context — reading financials, mapping competitive landscapes, and defending design decisions in the language of value.
Analyse open or closed card sort results into a proposed grouping and label set. Use after running a sort study. For turning that evidence into a full structure, use `information-architecture` (ux-strategy).