Mentor by Skippr AI
AI Product Review, Right in Your Browser
580 followers
AI Product Review, Right in Your Browser
580 followers
AI agent that catches design, copy, and accessibility issues before you ship. Works on Localhost, Production, Figma, Lovable, Replit & more. Syncs with coding agents via MCP. Finesse reviews every screen against UX and product best practices, then gives you real-time feedback. See annotations directly on your UI, generate shareable reports with one click, or create tickets for Linear and Jira. Need deeper feedback? Talk to your AI design lead - the deepest product critique agent available.
Agnes AI
Skippr seems to be an agentic version of QA！Nice angel to dive in - congrats team!
Can it read session data to perform analytics
Skippr AI
@benjaminmbrown This is super interesting direction. Please explain how do you see this working
Documentation.AI
How does Skippr differ from the AI coding agents? Seems like you’re solving a different, more upstream problem with no coding abilities?
Hey @roopreddy
Skippr operates on a completely different layer! Coding agents jump in once you already know exactly what you want to build. Skippr is the step before that, the product and UX brain that helps you avoid shipping the wrong thing faster.
It catches issues early, clarifies intent, shapes the experience, and makes sure the direction actually makes sense. Then, when you’re ready to execute, Skippr hands clean, intentional requests off to any coding agent through MCP. It’s upstream, not competing, and it saves a ton of wasted development cycles.
Does Skippr integrate directly with GitHub/GitLab/Bitbucket yet? Or is everything done locally through the Chrome extension?
Hey@dheeman_hota
Right now, we don't integrate directly with GitHub/GitLab/Bitbucket. You can use Skippr locally via localHost in a browser!
Mentor by Skippr AI
@dheeman_hota Great question! Everything currently runs through the Chrome extension - no direct GitHub/GitLab/Bitbucket integration yet.
But we're exploring this path to give Skippr codebase context and potentially push changes as PRs directly.
What's your main use case - better context awareness or automated PRs?
QA automation for AI-powered products is becoming critical. Automated design + accessibility checks before shipping is huge.
Q: How does it handle custom design systems or company-specific UX patterns? Also integrating with CI/CD pipelines - any plans for that?
Love the execution! 🚀
Mentor by Skippr AI
@imraju Thanks! Great questions.
Custom design systems were actually part of Skippr's original story. We're exploring ways to bring this knowledge into Skippr Finesse.
CI/CD integration is definitely on our radar. We're exploring running Skippr as an async process in CI/CD pipelines to auto-review each PR, plus integrating with GitHub/GitLab/Bitbucket to pull codebase context and potentially push fixes or suggestions as PRs directly.
Are you looking for that full closed-loop workflow - review, find issues, suggest improvements, implement fixes, and open PRs automatically - or more of a PR review agent?
superfill.ai
How does Skippr scale with team size? Is it more useful for tiny teams or also mid-sized engineering orgs?
Skippr AI
@mikr13 Great question! To be honest, it’s valuable for both—but in different ways. For small teams without a lead designer, UX, or accessibility expert, this can be the only quality control on UX and product issues before shipping.
For mid-sized teams, it can’t replace the final go/no-go decision by the Head of Design, PMs, or accessibility experts. However, it can significantly reduce rebuild cycles and shipping delays by preparing builders, engineers, and PMs ahead of the actual design review. Based on my estimation, it can save weeks of time.
Love the MCP integration idea — feedback loop straight into coding agents is smart. Does Skippr prioritize issues by severity, or does it dump everything at once? Curious how it avoids overwhelming Cursor/Claude with minor nitpicks vs critical UX blockers.
Skippr AI
@andreiboldyrev Thanks! This point was critical when designing the service. We decided to start by giving you a short summary of the most important insights. From there, we surface up to 12 issues (though we often detect more) in the Issues tab, prioritized by severity. You can also reorganize them by topic.
This is our second iteration, and it’s still a work in progress. We also provide an online, shareable report where we have more flexibility. Importantly, through the chat you can ask the agent to elaborate on or explain any issue—so it’s not just a passive report.