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- User Modeling
user-modeling_skill
3
GitHub Stars
1
Bundled Files
2 months ago
Catalog Refreshed
4 months ago
First Indexed
Readme & install
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Installation
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npx veilstrat add skill abhsin/designskills --skill user-modeling- SKILL.md5.4 KB
Overview
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.
How this skill works
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.
When to use it
- You need clarity on who to design for beyond a high-level target user description
- Before prioritizing features or writing user stories
- After initial research (interviews, support tickets, reviews) to synthesize findings
- When product decisions keep reverting to opinions instead of user behavior
- When validating roadmaps or MVP scope against real user needs
Best practices
- Limit personas to 1–3 meaningful segments; avoid kitchen-sink lists
- Define personas by goals and behaviors, not demographics
- Prioritize differences that change what you would build
- Pair each persona with 2–3 concrete scenarios to drive flows and acceptance criteria
- Flag assumptions clearly when research is thin and list validation steps
Example use cases
- Turn interview notes and support tickets into two focused personas that drive an MVP scope
- Convert a problem statement and product hypothesis into persona-led user stories
- Synthesize forum and review data into actionable constraints for onboarding design
- Create quick personas from assumptions as a placeholder before running validation interviews
FAQ
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.