persona-generator
Skill3 starsChanged 4 months agoMITView on GitHub
Create evidence-based user personas focused on behaviors, goals, and pain points — not demographics alone. Use when working on product discovery, PRDs, user stories, OKRs, or backlog planning.
2 other projects use a version of this.
Adopted by
Most recently taken up by
- tommcfarlin/claude-code-persona-generatortheir own version, first seen Apr 16, 2026
- ncklrs/claude-chrome-user-testingtheir own version, first seen Jan 7, 2026
Other versions2 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 2
- ncklrs/claude-chrome-user-testing★ 20same name
- tommcfarlin/claude-code-persona-generator★ 2same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
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The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 14
Create evidence-based user personas focused on behaviors, goals, and pain points — not demographics alone.
- Distinctive: line 112
Use empathy map before writing narrative profile.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Persona Generator
Purpose
Create evidence-based user personas focused on behaviors, goals, and pain points — not demographics alone.
Input: Target user segment, problem statement (optional), research data (optional)
Output: Persona document with empathy map, behavioral traits, and scenario triggers
Examples: See examples.md for worked input/output.
Workflow
Step 1: Define persona scope
Confirm:
- One persona per Skill execution (not persona sets)
- Primary vs secondary persona (if multiple requested, pick primary first)
- Segment boundaries (role, context, experience level)
Step 2: Gather behavioral evidence
Collect or request:
- Observed behaviors (not just stated preferences)
- Goals and motivations
- Pain points and frustrations
- Tools and workflows currently used
- Decision-making triggers
Step 3: Build empathy map
Fill four quadrants:
| Says | Thinks |
| [Quotes or paraphrased statements] | [Unspoken concerns] |
| Does | Feels |
| [Observed actions] | [Emotional states] |
Step 4: Define persona profile
Include:
- Name (fictional label, not a real person claim)
- Role and context
- Goals (primary and secondary)
- Pain points (ranked by severity)
- Success definition (what "winning" looks like for them)
- Anti-goals (what they actively avoid)
Step 5: Add scenario triggers
Describe 2–3 situations that activate the persona's need.
Step 6: Validate
Run Validation checklist.
Decision Rules
| Condition | Action |
| Only demographics provided (age, gender) | Request behavioral and goal data |
| Persona based on assumptions only | Mark all traits as "assumed"; list validation needed |
| Multiple segments in one request | Ask user to pick primary segment |
| Contradictory behaviors in input | Surface contradiction; ask for clarification |
| Research data conflicts with stated segment | Flag conflict; prioritize research data |
Validation
- Exactly one persona produced
- Empathy map has content in all four quadrants
- At least 3 pain points ranked by severity
- Goals are behavioral, not feature wishes
- Assumed traits clearly marked vs evidence-based
- 2–3 scenario triggers defined
- No stereotyping or unsupported demographic generalizations
Anti-patterns
- Demographic persona — "35-year-old male, likes technology" without behaviors.
- Ideal user fantasy — persona with no pain points or trade-offs.
- Feature wishlist persona — goals listed as "wants feature X".
- Multiple personas at once — dilutes focus and validation.
- Presenting assumptions as facts — not marking unvalidated traits.
Best Practices
- Base personas on observed behavior patterns, not market segments alone.
- Use empathy map before writing narrative profile.
- Include anti-goals to prevent scope creep in later Skills.
- Reference problem statement if available from prior Skill.
- Recommend 3–5 user interviews to validate assumed traits.
Output Structure
# Persona: [Name] — [Role]
## Profile
- **Segment:** [description]
- **Context:** [work/life situation]
- **Experience level:** [novice/intermediate/expert]
## Empathy Map
[Four quadrants table]
## Goals
| Priority | Goal |
|----------|------|
## Pain Points
| Severity | Pain Point | Evidence |
|----------|------------|----------|
## Success Definition
[What winning looks like]
## Anti-goals
- [What they avoid]
## Scenario Triggers
1. [Situation] → [need activated]
## Validation Status
| Trait | Status (evidence/assumed) |
|-------|---------------------------|Next Skills
| Outcome | Recommended Skill |
| Persona validated, need vision | product/product-vision-builder |
| Need to re-validate idea | product/idea-validator |
| Ready for problem refinement | product/problem-statement-builder |
| Ready for feature decisions | product/feature-prioritization |
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