JTBD methodology for extracting real jobs behind feature requests — job statements, abstraction layers, first-principles extraction, ODI outcome statements, and opportunity scoring
npx skills add https://github.com/nWave-ai/nWave --skill nw-jtbd-analysis
A job is the progress a person is trying to make in a particular circumstance. Jobs are stable over time — technology changes, jobs don't. People hire products to make progress; they fire them when they fail.
The trap: Feature requests describe a proposed solution, not the underlying job. Always extract the job first.
When [situation/trigger], I want to [motivation/action], so I can [expected outcome]
Examples (good):
| Type | Question | Example |
|------|----------|---------|
| Functional | What task is the user trying to accomplish? | Find someone with complementary expertise |
| Emotional | How does the user want to feel? | Feel confident approaching strangers |
| Social | How does the user want to be perceived? | Appear professional and well-connected |
Jobs usually live at strategic or physical level, not tactical.
| Layer | Question | Example |
|-------|----------|---------|
| Tactical | How do we improve this interaction? | Better drag-and-drop for notes |
| Operational | Why does this workflow exist? | Why do we need a facilitator? |
| Strategic | What decision is being pursued? | How do we reduce direction uncertainty? |
| Physical | What's the irreducible function? | Input → Synthesis → Convergence |
Navigation rules:
When a feature request is presented as a job, apply this:
Disruption check: Is there a higher-level job that would make this entire job unnecessary?
Format: [Direction] + [Metric] + [Object] + [Context]
Direction: Always "Minimize" (95% of time). "Maximize" only when more is genuinely better.
Metrics priority:
the time it takes to — speed/efficiency (preferred)the likelihood of — avoiding occurrencesthe likelihood that — avoiding resultsthe number of — quantity reductionthe effort required to — easeGood vs bad examples:
| Bad | Problem | Good |
|-----|---------|------|
| "I want easy video calls" | Ambiguous + solution | "Minimize the time it takes to start a conversation with a specific person" |
| "Manage my network effectively" | Vague verb + ambiguous | "Minimize the time it takes to identify who can help with a specific need" |
| "Use breakout rooms to talk privately" | Solution embedded | "Minimize the likelihood of conversations being overheard by unintended parties" |
| "Don't miss important people" | Negative framing | "Minimize the likelihood of failing to connect with relevant attendees" |
Forbidden words: easy, reliable, good, better, effective, efficient, manage, handle, deal with.
Forbidden patterns: solution references ("using AI", "via the app"), compound statements with "and"/"or", demographics.
Formula: Score = Importance + Max(0, Importance - Satisfaction)
Where Importance and Satisfaction are surveyed 1-10.
| Score | Interpretation |
|-------|---------------|
| > 12 | Under-served — high opportunity |
| 10-12 | Appropriately served — maintain |
| < 10 | Over-served — do not invest |
Output format per opportunity:
| Outcome | Importance | Satisfaction | Score | Status |
|---------|------------|--------------|-------|--------|
| Minimize time to find relevant attendees | 9.2 | 4.1 | 14.3 | Under-served |
Produce docs/feature/{feature-id}/diverge/job-analysis.md with:
Gate: Job must be at strategic or physical abstraction level. Tactical-level jobs are not acceptable input for brainstorming — elevate first.
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Take nwave-ai/nw-jtbd-analysis from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.