You don't need another AI tutorial. You need tools that work the moment you install them.
Claude skills are reusable instruction sets that give Claude deep expertise in specific tasks. Think of them like hiring a specialist contractor: you install the skill once, and every time you hand Claude a relevant task, it knows exactly how to handle it: the methodology, the edge cases, the output format.
For UX researchers, this changes the math on what's worth automating. Not the thinking parts (that's still yours), but the parts that eat your afternoons: cleaning transcripts, tagging highlights, drafting screeners, building readout decks.
Here are 8 skills we've built and tested on real research projects. Each one targets a specific bottleneck in the research workflow, and each one is ready to install.
A skill is a markdown file (SKILL.md) that contains detailed instructions for Claude. When you install one, Claude reads it before handling related tasks — like giving a new team member a thorough briefing before they start work.
You can install skills in Claude Code, Claude Desktop, or any MCP-compatible client. Drop the file in your skills folder, and you're done.
The skills below work independently, but they're designed to chain together. Clean a transcript, then synthesize across 10 cleaned transcripts, then generate a stakeholder readout from the synthesis. Each step feeds the next.
The problem it solves: Raw transcripts from Otter, Fireflies, Zoom, Rev, or Grain are a mess. Speaker labels are inconsistent ("Speaker 1" vs. "John" vs. "J. Smith"), filler words clutter every sentence, timestamps break up the flow, and the formatting makes analysis painful.
What this skill does: Takes a raw transcript and produces a clean, analysis-ready document. It normalizes speaker labels to consistent names, strips filler words (um, uh, like, you know) while preserving meaning, removes or reformats timestamps, fixes obvious transcription errors from context, and structures the output with clear speaker turns.
When to use it: Right after your recording tool exports a transcript and before any analysis begins. This is always step one.
What makes it different from just asking Claude to "clean this up": The skill includes specific rules for handling overlapping speech, preserving emotional cues (laughter, sighs, pauses) that matter for analysis, and maintaining verbatim accuracy on key quotes even while cleaning surrounding text. Without the skill, Claude tends to over-edit — smoothing out the rough edges that researchers actually need.
Pro tip: If you're using Great Question for your studies, the platform handles transcript cleanup automatically. This skill is for transcripts from other sources you want to bring into your analysis workflow.
The problem it solves: You've run 12 interviews. You have 12 cleaned transcripts. Now you need to find the patterns, contradictions, and key themes across all of them — and that's a full week of work.
What this skill does: Processes multiple transcripts (or sets of notes) and generates a structured synthesis. It identifies recurring themes with supporting evidence from specific participants, flags contradictions between participants, highlights surprising or unexpected findings, and maps the strength of each theme based on how many participants mentioned it.
When to use it: After you've cleaned your transcripts and before you start building a readout or making recommendations. Works best with 5–15 transcripts. For larger sets (15+), run it in batches of 10–12 and then synthesize the batch outputs.
The output format: You get a structured document with themes ranked by prevalence, direct quotes mapped to each theme, a contradictions section (this is where the gold is), and a "signals worth investigating" section for things that only 1–2 participants mentioned but that seem significant.
Important caveat: This skill accelerates pattern recognition, but it doesn't replace interpretation. The AI identifies that 8 of 12 participants mentioned onboarding friction — you decide what that means for the product roadmap. Use it to organize the data, not to draw conclusions.
If you're running research at scale, Great Question's AI analysis handles cross-study synthesis natively across your entire research repository.
The problem it solves: Writing a good discussion guide takes 2–3 hours. Not because it's hard, but because you're balancing research objectives against conversation flow, making sure questions are open-ended enough, building in probes for each topic, and sequencing warm-up through core through wind-down.
What this skill does: Takes your research objectives and target participant profile, then generates a semi-structured discussion guide. It includes a warm-up section (non-threatening questions to build rapport), core topic blocks with primary questions and follow-up probes, transition language between topics, a closing protocol (anything else, referral requests, next steps), and estimated timing for each section.
When to use it: During research planning, after you've defined your research questions but before you start scheduling sessions.
What it doesn't do: It won't tell you what to research. If your research objectives are vague ("understand the user experience"), the guide will be vague. Garbage in, garbage out. Start with specific research questions: "How do enterprise teams currently manage participant recruitment across multiple researchers?"
The skill's secret weapon: It flags questions that are likely to produce yes/no answers or leading responses and suggests alternatives. This is the thing most discussion guides get wrong — asking "Do you find onboarding confusing?" instead of "Walk me through what happened when you first set up your account."
The problem it solves: You've pulled 200 highlights from your research. Now you need to categorize them: what theme does each one belong to? What's the sentiment? Which ones connect to insights from previous studies?
Doing this manually across a large study takes days. Doing it consistently across multiple studies takes a system most teams don't have.
What this skill does: Takes a set of research highlights (quotes, observations, notes) and applies a structured taxonomy. It assigns theme tags to each highlight, tracks sentiment (positive, negative, neutral, mixed), suggests connections to other highlights in the set, and builds a category hierarchy that emerges from the data rather than being imposed on it.
When to use it: After synthesis, when you're organizing insights for your research repository. Also useful for ongoing repository maintenance — run it on new highlights as they come in to keep your taxonomy consistent.
Integration with Great Question: If you're using Great Question's repository, this skill complements the platform's built-in tagging. Use it for batch processing highlights from external sources before importing them, or to audit and clean up existing tags.
A word on taxonomy design: The skill generates tags from the data, which is great for discovery. But over time, you'll want to align these with your team's existing taxonomy. Run the tagger, review what it generates, then merge similar categories and promote the ones your team actually uses.
The problem it solves: You've done the research. You have the findings. Now you need to present them to three different audiences: the PM who wants "just tell me what to build," the design lead who wants the user journey details, and the VP who wants business impact.
Same research, three different framings. That's three decks — or at least three significantly different sections.
What this skill does: Takes your synthesis or findings document and generates stakeholder-ready readout content. You specify the audience (PM, design, leadership, cross-functional), and it reframes accordingly. For PMs: leads with actionable recommendations, maps findings to product decisions. For designers: centers on user behavior, pain points, and journey moments. For leadership: frames everything around business impact, risk, and opportunity.
When to use it: After synthesis, before your readout presentation. This doesn't replace your thinking about what matters — it reformats your thinking for the audience.
What it actually outputs: Not a slide deck (use a presentation tool for that). It gives you structured content blocks with headers, key points, supporting evidence, and recommended actions — organized in the narrative flow that works for each audience. Copy these into your preferred presentation format.
The problem it solves: Recruiting the right participants is half the battle. A weak screener wastes everyone's time: you end up interviewing people who don't match your criteria, or you disqualify good participants with poorly worded questions.
What this skill does: Takes a target persona description and research objectives, then generates a screening survey. It includes 8–12 screening questions with qualification logic, red-flag disqualifiers (professional survey takers, competitor employees, etc.), a scoring rubric so you can rank-order candidates rather than just pass/fail, and response validation patterns.
When to use it: During study setup, after you've defined who you want to talk to. The output is formatted to paste directly into Great Question's recruitment tools or your screener platform of choice.
The detail that matters: The skill differentiates between hard disqualifiers (must-have criteria) and soft preferences (nice-to-have characteristics). This prevents the common mistake of building a screener so strict that nobody qualifies. It also randomizes response options and includes attention-check questions to filter out people who aren't reading carefully.
The problem it solves: Affinity mapping is one of the most powerful synthesis methods in research — and one of the most tedious. Sorting hundreds of observations into meaningful clusters, naming those clusters, then looking for patterns across clusters. On a physical wall, it takes half a day. Digitally, it's often done in FigJam or Miro, but you're still doing all the grouping manually.
What this skill does: Takes a set of observations, quotes, or notes and runs a digital affinity mapping process. It groups related observations into clusters, suggests descriptive names for each cluster, identifies outlier observations that don't fit neatly (these are often the most interesting), and shows the relationships between clusters.
When to use it: During analysis, especially when you have a large volume of unstructured observations. Works well with interview notes, usability testing observations, survey open-ends, or support ticket themes.
Important: this is a starting point, not the finished analysis. The skill's groupings are based on semantic similarity — words and concepts that appear together. But research insight often comes from unexpected connections that aren't semantically obvious. Use the AI-generated map as a draft. Move things around. Challenge the categories. The value is in the 80% that's already sorted correctly, freeing you to spend your time on the 20% that requires human judgment.
The problem it solves: Every research project needs a brief, and every brief needs the same things: background, objectives, methodology, participant criteria, timeline, success metrics. Writing one from scratch takes 1–2 hours. Adapting a template still takes 45 minutes.
What this skill does: Takes a business question or product decision and generates a complete research brief. It recommends a methodology with justification (why interviews over surveys for this question, why moderated over unmoderated), defines success criteria, scopes participant requirements, and estimates a realistic timeline.
When to use it: At project kickoff, when a PM or stakeholder brings you a question and you need to translate it into a research plan. Also useful for training purposes: show junior researchers what a solid brief looks like for different study types.
The methodology recommendation is the standout feature. Instead of just filling in a template, the skill evaluates the research question against method fit. Exploring a problem space? It recommends generative interviews. Comparing two design options? It suggests unmoderated usability testing. Need to validate with numbers? Survey with the right sample size calculation.
These skills are useful individually, but they're designed to work as a pipeline. Here's the typical flow for a qualitative research project:
Planning phase: Research brief writer (defines the project) → Discussion guide builder (creates interview protocol) → Screener builder (sets up recruitment criteria)
Analysis phase: Transcript cleaner (processes raw recordings) → Insight tagger (categorizes highlights) → Affinity mapper (clusters observations) → Research synthesizer (finds patterns across everything)
Communication phase: Stakeholder readout generator (formats for each audience)
You don't have to use all eight on every project. Pick the ones that target your bottlenecks. If transcript cleaning alone saves you 2 hours per interview across a 15-participant study, that's 30 hours back. If the synthesizer cuts your analysis time in half, that's another week.
The compound effect is what matters. Each skill removes friction from one step, and the cumulative impact across a full project is significant.
If you're using Great Question, many of these capabilities are already built into the platform, from AI-powered analysis to automated tagging in your research repository. These skills extend that functionality into your broader AI workflow, especially when you're working with data from multiple sources.
Want all 8 skills ready to install? Download the AI Research Toolkit — includes the skills, a prompt library, and workflow templates.
What exactly is a Claude skill?
A Claude skill is a markdown file containing detailed instructions that Claude reads before handling related tasks. When you install a skill, Claude gains expert-level knowledge about a specific workflow — the methodology, edge cases, and output format. It's like giving a new team member a thorough briefing that they reference every time they do that type of work.
Do I need Claude Code to use these skills?
No. Skills work with Claude Code, Claude Desktop (via the Settings → Capabilities → Skills upload), and Cowork. The installation method varies slightly by client, but the skill files themselves are the same.
Can I customize these skills for my team's specific workflow?
Yes — that's actually encouraged. A skill is just a markdown file. Open it, edit the instructions, save. If your team uses a specific taxonomy for tagging, edit the insight tagger skill to use those categories. If you have a standard discussion guide format, update the builder to match.
How do these skills compare to Great Question's built-in AI features?
Great Question's AI handles analysis, tagging, and synthesis natively within the platform — no setup required. These Claude skills extend similar capabilities to data that lives outside Great Question: transcripts from other recording tools, notes from field studies, or insights from external sources. They work together, not as replacements.
Will AI skills replace the need for human researchers?
No. These skills automate the mechanical parts of research — cleaning, sorting, formatting, pattern-matching. The intellectual work (choosing what to research, interpreting what findings mean, deciding what to recommend) stays with the researcher. The goal is to spend more time on thinking and less on processing.
How long does it take to see results?
Most researchers report time savings on their first project. The transcript cleaner saves 1–2 hours per interview immediately. The synthesizer typically cuts analysis time by 40–60%. The compound effect across a full project (using 3–4 skills together) is where the numbers get significant.
Tania Clarke is a B2B SaaS product marketer focused on using customer research and market insight to shape positioning, messaging, and go-to-market strategy.
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