Skip to main content
ClaudeWave
Skill963 estrellas del repoactualizado 4d ago

job-story-mapper

Job Story Mapper translates product requirements and user research into Jobs-to-be-Done job stories that map customer needs across functional, emotional, and social dimensions. Use this skill when defining user requirements, conducting JTBD research, or reframing features around customer outcomes rather than feature specifications. It produces structured job story maps with opportunity scoring and pain intensity analysis to identify underserved customer needs and differentiation points.

Instalar en Claude Code
Copiar
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills /tmp/job-story-mapper && cp -r /tmp/job-story-mapper/plugins/pm-discovery/skills/job-story-mapper ~/.claude/skills/job-story-mapper
Después abre una sesión nueva de Claude Code; el skill carga automáticamente.

SKILL.md

# Job Story Mapper Skill

Stop writing features. Start understanding jobs. This skill translates product requirements and user interviews into precise job stories that keep the team focused on outcomes — not outputs.

## Jobs-to-be-Done Fundamentals

A "job" is the progress a customer is trying to make in a given situation. People don't buy products — they hire them to get a job done.

Three dimensions of every job:
- **Functional job:** The practical task ("get from A to B")
- **Emotional job:** How they want to feel ("feel confident I made the right choice")
- **Social job:** How they want to be perceived ("look like a competent professional to my team")

Great products address all three. Most roadmaps only address the functional one.

---

## Job Story Format

**Template:**
> When [situation/trigger], I want to [motivation/goal], so I can [expected outcome].

**Not a user story:**
User stories focus on roles and features: "As a [role] I want [feature] so that [benefit]."
Job stories focus on situations and motivations: "When [I'm in this specific situation] I want [this capability] so I can [achieve this outcome]."

**The situation is the most important part.** "When I'm in the middle of a sprint and my PM asks for an update" is a much richer trigger than "As a developer."

---

## Mapping Process

### Step 1: Identify the main job
One sentence: What is the core job your product is hired for?
> "Help [user type] [accomplish outcome] when [context]."

### Step 2: Break into job steps
What are all the sub-tasks within the main job?
(Use a job map: Define → Locate → Prepare → Confirm → Execute → Monitor → Modify → Conclude)

### Step 3: Identify pain points per step
Where does the job fall down today? Where do customers use workarounds?

### Step 4: Write job stories for each pain point
One job story per distinct situation-motivation pair.

### Step 5: Map to product opportunities
Which job stories are underserved? Which have existing solutions? Where is your differentiation?

---

## Output Format

### Job Story Map — [Product/Feature Area] — [Date]

**Core Job Statement:**
> When [context], [user type] wants to [main job outcome], so they can [ultimate goal].

---

**Job Map:**

| Step | Sub-Job | Current Solution | Pain Points | Underserved? |
|---|---|---|---|---|
| Define | [What user does] | [Tool/method used] | [Frustration] | H/M/L |
| Locate | | | | |
| Prepare | | | | |
| Confirm | | | | |
| Execute | | | | |
| Monitor | | | | |
| Modify | | | | |
| Conclude | | | | |

---

**Job Stories (prioritised by underservice):**

**Job Story 1 — [Situation label]**
> When [specific situation], I want to [motivation], so I can [outcome].

Functional dimension: [What they need to get done]
Emotional dimension: [How they want to feel]
Social dimension: [How they want to be perceived]

Current workaround: [What they do today]
Pain intensity: [High / Medium / Low]
Frequency: [How often this situation occurs]
Product opportunity: [What we could build to address this]

---

Repeat for each major job story.

**Opportunity Scoring:**
Rate each job story on:
- Importance to customer (1–10)
- Satisfaction with current solution (1–10)
- Opportunity score = Importance + max(Importance – Satisfaction, 0)
- Prioritise: Opportunity score > 10

---

## Quality Checks

- [ ] Job stories use the "When / I want to / So I can" format (not user story format)
- [ ] Situation is specific (not "as a user" — a real moment or trigger)
- [ ] All three dimensions covered: functional, emotional, social
- [ ] Opportunity score calculated for each job story
- [ ] Current workaround identified for each high-opportunity story
- [ ] Product opportunity is distinct from "build the feature" (it's an outcome)

## Required Inputs

Ask the user for these if not provided:
- **Product or feature area** to map (e.g. onboarding, checkout, dashboard)
- **User type or persona** (who are we mapping jobs for?)
- **Source material** (user interview notes, support tickets, discovery findings, or describe from memory)
- **Scope** (full product job map vs. a single feature area)

## Anti-Patterns

- [ ] Do not write job stories that describe a feature rather than a situation-motivation pair
- [ ] Do not skip the social and emotional dimensions — mapping only functional jobs misses the most defensible differentiation opportunities
- [ ] Do not define situations too broadly ("as a user who wants to manage their work") — the situation must be a specific moment or trigger
- [ ] Do not conflate opportunity scoring with priority — a high opportunity score still requires feasibility and strategic fit assessment
- [ ] Do not produce a job map without identifying current workarounds — the workaround reveals what the job is worth to the customer

## Guidelines

- Never write a job story for a feature — write it for the situation that makes the feature valuable
- If you can't identify the situation, you don't understand the job yet — go back to user research
- Social and emotional jobs are harder to surface but often the most defensible differentiators
- Recommend sharing job stories with engineering — they make better technical decisions when they understand the "why"
ai-ethics-reviewSkill

Conduct a structured ethical review of an AI or ML feature, model, or product. Use when preparing to deploy an AI system, assessing algorithmic risk, auditing a model for bias, or producing a responsible AI impact assessment. Produces a structured ethics review covering fairness, transparency, privacy, safety, accountability, and societal impact with a risk tier score, pre-deployment checklist, and prioritised mitigations.

ai-product-canvasSkill

Structure AI and ML product decisions with the rigour of any product decision. Use when building AI-powered features, evaluating LLM integrations, designing AI products, or assessing AI readiness. Produces a complete AI product canvas covering problem definition, model approach, data requirements, evaluation framework, UX design, responsible AI checklist, and launch monitoring plan.

design-handoff-briefSkill

Transform feature briefs into structured design briefs that give designers the context they need before opening Figma. Use when asked to write a design brief, create a design handoff, brief a designer on a new feature, or translate a PRD into design requirements. Produces a brief with user goal, emotional context, success criteria, constraints, edge cases, and out-of-scope boundaries.

experiment-designerSkill

Design statistically rigorous A/B tests and interpret experiment results. Use when asked to design an experiment, run an A/B test, calculate sample size, interpret test results, or assess whether an experiment was successful. Produces a complete experiment design with hypothesis, sample size, run time, success criteria, and risk flags — or a results interpretation with ship/iterate/kill recommendation.

multi-source-signal-synthesiserSkill

Synthesises user signals from multiple research sources into a unified, weighted insight brief. Use when you have data from interviews, support tickets, NPS verbatims, app reviews, or sales calls and need to reconcile contradictions, surface the underlying need behind requests, or answer 'what are users really telling us'. Produces ranked insights with confidence ratings, source weighting rationale, divergent signal analysis by user segment, and a research gap identification section.

data-analysis-standardSkill

Structure a product data analysis, metric deep-dive, funnel analysis, or cohort study. Use when asked to analyse product metrics, investigate a drop in conversion, explain a data change to stakeholders, or find the root cause of a metric movement. Produces a structured analysis with question, root cause, confidence level, and recommended action.

product-health-analysisSkill

Interpret product metrics against goals and surface actionable signals. Use when asked to analyse product health, review key metrics, investigate a performance issue, produce a health report, or assess product-market fit signals. Produces a structured health report with RAG status, trend analysis, root cause hypotheses, and prioritised actions.

retention-analysisSkill

Structure a retention analysis, churn investigation, or engagement deep-dive for any product team. Use when asked to analyse user retention, investigate churn, measure DAU/MAU, or build a retention improvement plan. Produces a retention snapshot with root cause hypotheses, aha-moment correlation, and prioritised interventions.