Run fast, source-grounded UX research on the highest-signal problems users are experiencing with a user-specified digital product. Use when the user asks to research user pain, UX friction, onboarding issues, docs/help problems, developer experience friction, support pain, product workflow issues, or current user complaints for a named product.
909 tokens
context cost
the whole folder, loaded on every use
2
files
instructions only
0
copies elsewhere
how many repositories repackaged it
477
stars on the repo
on the repository, not the skill itself
Install
one command, takes just this skill from the repository
npx skills add https://github.com/openai/role-specific-plugins --skill research
What comes with it
244 bytes besides the instruction
agents/openai.yaml
The instruction itself
7 sections, as written by the author
Research
Run a fresh UX research scan for the product the user specifies.
Focus on current, evidence-backed user problems. Prioritize logged-in product experience, self-serve flows, onboarding, docs/help, developer experience, support friction, and product workflows.
Critical Overrides
Refer to the Plugin router $index before proceeding.
Follow $critical-overrides.
User Context
Before starting, load $user-context and run its preflight script when local shell access is available.
Use saved product URLs, Figma files, screenshots, reference images, codebase paths, Storybook, tokens, design systems, brand assets, component refs, browser preferences, and share targets as grounding material when relevant.
Do not inspect every saved reference. Inspect only what the current task needs.
Contract
Restate the product, audience, time horizon, and research scope before scanning.
Use public sources by default. Use internal sources when the connectors are available and the user request allows it.
Cite sources wherever available.
Separate observed evidence from inference.
Do not overclaim from anecdotes.
Do not return a dump of complaints. Tell a clear product story.
Say clearly when source access is missing or weak.
Workflow
Restate the research scope.
Search public sources:
Reddit
X/Twitter
Hacker News
Stack Overflow
GitHub issues/discussions
forums, blogs, reviews, YouTube comments, and developer communities where relevant
Search internal sources when available:
Slack
Gong
Notion
Google Drive/docs
Linear/Jira/GitHub
support or CRM notes if available
Cluster evidence into the highest-signal UX problems.
Separate:
product UI/workflow friction
docs/help friction
onboarding friction
account, billing, permissions, or setup friction
developer/API/SDK friction
reliability/performance issues
feature requests
Rank problems by severity, frequency, confidence, and product leverage.
Tell a clear product story.
Output
Default to an in-chat research brief unless the user asks for another format.
Include:
Executive read: the core story in 5-7 sentences.
Ranked UX problems: for each problem, include the problem, user goal, surface, what breaks, evidence, severity, frequency signal, confidence, and recommended product move.
Source map: what was searched, what each source contributed, and where signal was weak.
Opportunity map: group recommendations into fix this week, fix this quarter, and needs deeper research.
Rules
Use citations wherever available.
Do not overclaim from anecdotes.
Separate loud complaints from frequent problems.
Separate UX friction from missing features.
Separate reliability/performance issues from UX workflow issues.
Mark internal-only evidence separately from public evidence.
Keep the brief sharp, specific, and easy to consume.
How to use it
Copy the folder
Take openai/research from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
Check the name does not clash
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.