Create refined user personas from research data — 3 personas with JTBD, pains, gains, and unexpected insights. Use when building personas from survey data, creating user profiles from research, or segmenting users for product decisions.
From pm-market-researchnpx claudepluginhub tarunccet/pm-skillsThis skill uses the workspace's default tool permissions.
Guides Next.js Cache Components and Partial Prerendering (PPR) with cacheComponents enabled. Implements 'use cache', cacheLife(), cacheTag(), revalidateTag(), static/dynamic optimization, and cache debugging.
Migrates code, prompts, and API calls from Claude Sonnet 4.0/4.5 or Opus 4.1 to Opus 4.5, updating model strings on Anthropic, AWS, GCP, Azure platforms.
Details PluginEval's skill quality evaluation: 3 layers (static, LLM judge), 10 dimensions, rubrics, formulas, anti-patterns, badges. Use to interpret scores, improve triggering, calibrate thresholds.
Use for creating personas from research data (interviews, surveys, usability sessions). For segmenting existing users by behavior or feedback data, or for identifying strategic market segments, use user-segmentation.
Create detailed, actionable user personas from research data that capture the true diversity of your user base. This skill generates research-backed personas with jobs-to-be-done, pain points, desired outcomes, and unexpected behavioral insights to guide product decisions.
You are an experienced product researcher specializing in persona development and user research synthesis.
Your task is to create 3 refined user personas for $ARGUMENTS.
If the user provides CSV, Excel, survey responses, interview transcripts, or other research data files, read and analyze them directly using available tools. Extract key patterns, demographics, motivations, and behaviors.
For each of the 3 personas, provide:
Persona Name & Demographics
Primary Job-to-be-Done
Top 3 Pain Points
Top 3 Desired Gains
One Unexpected Insight
Product Fit Assessment