ux-researcher-designer_skill

This skill helps Senior UX Designers generate data-driven personas, map journeys, plan usability tests, and synthesize findings into actionable design

Python

4.6k

GitHub Stars

1

Bundled Files

2 months ago

Catalog Refreshed

3 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill alirezarezvani/claude-skills --skill ux-researcher-designer

SKILL.md11.7 KB

Overview

This skill is a UX research and design toolkit for senior designers and researchers. It produces data-driven personas, visual journey maps, usability test plans, and research synthesis that lead to actionable design recommendations. Use it to speed research workflows and improve confidence in product decisions.

How this skill works

Feed the tool with quantitative and qualitative user data (analytics, surveys, interviews, session recordings). It clusters patterns, generates personas with evidence and confidence levels, maps end-to-end journeys with emotions and opportunities, and produces testable usability plans and synthesis reports. Outputs include human-readable summaries and JSON for integration, plus scoring and prioritization for recommendations.

When to use it

Creating research-backed user personas from mixed data sources
Visualizing end-to-end experiences to find drop-offs and opportunities
Designing and scoping usability tests with clear success metrics
Synthesizing interview and survey data into prioritized findings
Preparing design implications and recommendations for engineering and PMs

Best practices

Combine at least two data sources (qualitative + quantitative) before generating personas
Validate generated personas with 3–5 real users and analytics cross-checks
Define clear, testable research questions before planning usability tests
Use Priority Score = Frequency × Severity × Solvability to rank opportunities
State sample size and confidence level with every persona or segment

Example use cases

Generate a persona set from a 45-user dataset to inform roadmap priorities
Create a journey map for onboarding to identify where users drop off in the first week
Design a moderated remote usability test (5–8 participants) to validate checkout flow changes
Synthesize interviews and support tickets into prioritized product fixes with business impact
Produce JSON persona outputs for integration with product analytics and personalization engines

FAQ

The generator works from structured JSON for individual users and supports human-readable and JSON output modes for integration.

How many users are needed for a reliable persona?

Aim for 31+ for high confidence, 11–30 for directional insight; validate smaller samples qualitatively before production decisions.