From ux-superpowers
Whenever the user hands you raw user-research artifacts — interview transcripts, survey results, NPS verbatims, support tickets, app store reviews, usability test notes, forum/community posts, customer emails, sales-call notes, or pasted feedback blobs — trigger this skill immediately, before any downstream synthesis. Always invoke when the user says things like "I have 30 interview transcripts in /tmp/interviews", "here are 200 NPS responses, what are the themes", "analyze these support tickets for patterns", "synthesize these app store reviews", "summarize what these customers said", "extract themes from these usability notes", or "go through these forum posts and find recurring issues". Fire on any mention of directories or file paths like interviews/, surveys/, feedback/, tickets/, reviews/, transcripts/, verbatims/, or pasted qualitative text attributed to users or customers. This skill is the mandatory first stop before persona-builder, jobs-to-be-done, or user-journey — its structured output (themes, pain points, unmet needs, converging/diverging signals, confidence levels) is what those downstream skills consume. Differentiate from generic CSV or data-analysis: this is specifically for QUALITATIVE USER RESEARCH, not sales figures, financial reports, engineering or access logs, or telemetry dumps. Do NOT trigger for code review of research-related files, bug fixes, performance work, or non-user datasets. When in doubt and the data represents real users' words, behaviors, or complaints, invoke this skill rather than ad-hoc analysis.
How this skill is triggered — by the user, by Claude, or both
Slash command
/ux-superpowers:research-intakeThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Transform messy real-world data into structured insights.
Transform messy real-world data into structured insights.
This skill processes ANY user data the user provides. Common types:
When the user provides data, acknowledge what you received and classify it:
"I see you've shared [type of data]. Let me analyze this for patterns. Is there anything specific you want me to look for, or should I do an open analysis?"
For each data source, extract:
## Data Source: [type and brief description]
### Key Themes (frequency-ranked)
1. **[Theme]** — mentioned [N] times
- Supporting evidence: [direct observations, not quotes]
### User Pain Points
- [specific, actionable pain points with severity]
### Unmet Needs
- [things users want but don't have]
### Positive Signals
- [things users value about current solutions — preserve these]
### Behavioral Patterns
- [how users actually behave, especially if different from expectations]
### Surprising Findings
- [anything unexpected or counter-intuitive]
If multiple data sources are provided, identify:
## Research Confidence Assessment
| Insight | Data Sources | Sample Size | Confidence | Action |
|---------|-------------|-------------|------------|--------|
| [insight] | [which sources] | [how much data] | High/Med/Low | [use as-is / validate further / treat as hypothesis] |
Present the synthesis and ask:
"Here's what I'm seeing in the data. Should I use these insights to build personas, or do you want to adjust anything first?"
Structured insights from this skill feed directly into:
persona-builder — behavioral patterns become persona foundationsjobs-to-be-done — unmet needs become job statementsuser-journey — pain points map to journey phasestelemetry-designer — themes suggest what to measurenpx claudepluginhub adnanmir123/ux-superpowers --plugin ux-superpowersProvides behavioral guidelines to reduce common LLM coding mistakes, focusing on simplicity, surgical changes, assumption surfacing, and verifiable success criteria.
Searches, retrieves, and installs Agent Skills from prompts.chat registry using MCP tools like search_skills and get_skill. Activates for finding skills, browsing catalogs, or extending Claude.
Creates, edits, and optimizes skills for Claude Code, including drafting, evaluating with test prompts, iterating on performance, and improving skill descriptions for better triggering accuracy.