Jobs-To-Be-Done — Customer Discovery Framework
SkillMonitoring & opsCustomer discovery framework using Jobs-To-Be-Done theory — uncover the functional, social, and emotional jobs customers hire products to do. Produces JTBD canvases with job statements, outcome metrics, and competing solutions. Use alongside cm-brainstorm-idea for evidence-based product decisions.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Jobs-To-Be-Done — Customer Discovery Framework skill
What this skill tells your AI
The instructions your AI receives, as published by tody-agent/codymaster in skills/cm-jtbd/SKILL.md and read by ahel’s review.
Understand the job, not the customer. People don't buy products — they hire them to get a job done.
When to Use
- Before designing a new feature or product
- When existing features aren't converting or being used
- Alongside
cm-brainstorm-ideafor deep customer context - User mentions: "customer discovery", "JTBD", "what do customers want", "product-market fit", "why are users churning"
The JTBD Framework
Job Statement Formula
When [SITUATION], I want to [MOTIVATION], so I can [EXPECTED OUTCOME]
Three Job Dimensions
| Dimension | Definition | Example |
|---|---|---|
| Functional | The core task to accomplish | "Get from A to B quickly" |
| Social | How the person wants to be perceived | "Be seen as a reliable professional" |
| Emotional | How the person wants to feel | "Feel confident in my decision" |
Process
Phase 1: Job Discovery (Interviews)
- Recruit 5-8 recent customers (ideally within 90 days of purchase)
- Use the Switch Interview technique — ask about the moment they decided to switch/buy
- Key questions:
- "Walk me through the day you decided to [buy/switch/start using X]"
- "What were you doing before that solution existed?"
- "What was the first thing you tried? Why didn't that work?"
- "What almost stopped you from switching?"
- Record patterns: triggers → anxiety → progress → outcomes
Phase 2: JTBD Canvas
For each major job discovered, complete the canvas:
JOB STATEMENT:
When [situation], I want to [motivation], so I can [outcome]
FUNCTIONAL DIMENSION: [core task]
SOCIAL DIMENSION: [perception goal]
EMOTIONAL DIMENSION: [feeling goal]
FORCES PUSHING TO HIRE:
(+) Push: [what makes them switch from current solution]
(+) Pull: [what attracts them to new solution]
FORCES RESISTING HIRE:
(-) Anxiety: [fears about new solution]
(-) Habit: [attachment to old solution]
COMPETING SOLUTIONS CURRENTLY HIRED:
1. [direct competitor or workaround]
2. [indirect solution]
3. [do-nothing option]
OUTCOME METRICS (how customer measures success):
- Speed: [e.g., "get answer in <5 minutes"]
- Accuracy: [e.g., "zero errors in the output"]
- Effort: [e.g., "no manual steps required"]
Phase 3: Opportunity Scoring
Rate each outcome metric:
- Importance (1-10): How important is this outcome to the customer?
- Satisfaction (1-10): How satisfied are they with current solutions?
- Opportunity score = Importance + max(Importance − Satisfaction, 0)
Scores ≥ 15 = underserved outcomes → highest priority to address.
Output
Save JTBD canvas to docs/jtbd/jtbd-canvas-[date].md.
Integration
| Skill | Relationship |
|---|---|
cm-brainstorm-idea | UPSTREAM: JTBD feeds into strategic analysis |
cm-planning | DOWNSTREAM: Validated jobs inform feature plans |
cm-cro-methodology | COMPLEMENT: JTBD objections → CRO objection handling |
cm-dockit | OUTPUT: JTBD canvases are a document type in DocKit |
Signals
- GitHub stars
- 52
- Forks
- 23
- Last commit
- Aug 2026
Advanced
- Catalog kind
- skill
- Gateway key
cm-jtbd- Source
- github.com/tody-agent/codymaster