home / skills / abhsin / designskills / user-modeling
This skill helps you create lightweight, behavior-based user personas and usage scenarios to prioritize features and UX decisions.
npx playbooks add skill abhsin/designskills --skill user-modelingReview the files below or copy the command above to add this skill to your agents.
---
name: user-modeling
description: Create lightweight user personas and usage scenarios from problem framing or raw research. Use when a user needs to clarify who they're building for beyond a basic target user description. Outputs practical personas and scenarios that inform feature priorities and UX decisions—not marketing fluff.
---
# User Modeling
Build just enough understanding of your users to make better product decisions.
## Why This Exists
Creates behavior-based user models that reveal what users need and how they'll behave, not marketing personas with stock photos.
## Input Requirements
This skill works best with:
- `problem-framing` output (problem statement, target user, JTBD)
- Any existing research (interviews, surveys, support tickets, Reddit threads, reviews)
Can also work from assumptions if no research exists—but flags that these need validation.
## Workflow
### Step 1: Gather Context
Ingest upstream artifacts or ask:
- Who are you building this for?
- What do you know about them already?
- Have you talked to any potential users?
- Any data sources—reviews, forums, support tickets?
### Step 2: Identify User Segments
Look for meaningful differences in:
- **Goals** — What are they trying to accomplish?
- **Context** — When/where do they encounter the problem?
- **Constraints** — What limits their options?
- **Skill level** — How sophisticated are they?
- **Frequency** — How often do they face this problem?
Not every difference matters. Focus on differences that change *what you'd build*.
### Step 3: Build Personas
For each meaningful segment, create a lightweight persona. Limit to 2-3 personas max—more than that dilutes focus.
### Step 4: Define Scenarios
For each persona, define 2-3 concrete scenarios where they'd use the product. These become the basis for user stories and flows.
### Step 5: Identify Insights
Surface patterns that inform product decisions:
- What do all personas have in common?
- Where do they diverge?
- What would you build differently for each?
**Automatically save the output to `design/02-user-modeling.md` using the Write tool** while presenting it to the user.
## Output Format
```markdown
# User Modeling: [Project Name]
## Context
[Brief summary of the problem space and what we know]
**Research basis:**
- [Source 1: what it told us]
- [Source 2: what it told us]
- [Or: "Based on assumptions—needs validation"]
---
## Personas
### Persona 1: [Name/Label]
*[One-line description of who they are]*
**Goals:**
- [Primary goal]
- [Secondary goal]
**Context:**
- [When they encounter the problem]
- [Where they encounter it]
- [What else is going on]
**Pain points:**
- [Frustration 1]
- [Frustration 2]
**Current behavior:**
- [How they solve this today]
- [Tools they use]
- [Workarounds they've developed]
**Constraints:**
- [Time/budget/skill/access limitations]
**What success looks like:**
- [How they'd know the problem is solved]
**Quote:** *"[Something they might say that captures their mindset]"*
---
### Persona 2: [Name/Label]
*[One-line description]*
[Same structure]
---
### Persona 3: [Name/Label]
*[One-line description]*
[Same structure]
---
## Scenarios
### Persona 1 Scenarios
**Scenario 1.1: [Name]**
- **Situation:** [Context—what's happening]
- **Trigger:** [What prompts them to act]
- **Goal:** [What they're trying to accomplish]
- **Current approach:** [How they handle it today]
- **Frustration:** [What's broken about current approach]
**Scenario 1.2: [Name]**
[Same structure]
### Persona 2 Scenarios
**Scenario 2.1: [Name]**
[Same structure]
---
## Key Insights
### Commonalities
[What all personas share—these are table-stakes features]
- [Insight 1]
- [Insight 2]
### Divergences
[Where personas differ—these inform prioritization]
- [Persona 1] needs [X], while [Persona 2] needs [Y]
- [Persona 1] is [context], while [Persona 2] is [different context]
### Design Implications
[How this should influence what you build]
- [Implication 1]
- [Implication 2]
- [Implication 3]
---
## Validation Needed
[What assumptions need testing]
- [ ] [Assumption to validate]
- [ ] [Assumption to validate]
```
## Adaptation Guidelines
**Minimal (single obvious user type):**
- One persona, 2-3 scenarios
- Skip Divergences section
- 1 page total
**Standard (2-3 user types):**
- Full structure as shown
- 2-3 pages total
**Research-heavy (actual user data):**
- Include research summary
- Add quotes from real users
- Link to source data in appendix
## What Makes a Good Persona
**Good persona:**
- Defined by *goals and behaviors*, not demographics
- Reveals something that changes what you'd build
- Based on patterns, not individuals
- Specific enough to make decisions against
**Bad persona:**
- Stock photo + age + job title + hobbies
- So generic it could be anyone
- Based on one interview or pure assumption
- Doesn't inform any product decisions
## Anti-Patterns to Avoid
- **The Kitchen Sink** — Don't add demographics unless they matter
- **The Clone Army** — If personas don't differ meaningfully, merge them
- **The Wishful Thinker** — Model who users *are*, not who you wish they were
- **The Edge Case Collector** — Focus on primary users, not every possible user
## Handoff
After presenting the personas, ask:
> "Want to move to `/solution-scoping` to prioritize features, or straight to `/prd-generation`?"
**Note:** File is automatically saved to `design/02-user-modeling.md` for context preservation.This skill creates lightweight, behavior-driven user personas and concrete usage scenarios from problem framing or raw research. It focuses on actionable insights that inform feature priorities and UX decisions rather than marketing fluff. Outputs are concise, decision-ready personas and scenarios that product and design teams can act on immediately.
Ingests problem-framing artifacts or research (interviews, support tickets, reviews) and identifies meaningful user segments by goals, context, constraints, skill level, and frequency. For each segment it generates 1–3 personas and 2–3 concrete scenarios, then surfaces common patterns, divergences, and design implications. If no research is available, it builds assumptions and clearly flags which items need validation.
What inputs produce the best results?
Problem-framing artifacts plus primary research (interviews, surveys, support logs, reviews) produce the most reliable personas; without them the skill will generate assumption-based personas and flag validation needs.
How many personas should we create?
Aim for 1–3. More than three usually dilutes focus and makes prioritization harder.