A Claude Agent Skill. Point it at a design; it pulls real competitor screens for the same patterns and returns a scored, evidence-backed review. A UX benchmark, not a pixel diff.
See a sample review → · works with the Mobbin and Figma MCPs
Give it a Figma frame, a screenshot, or a plain description. It:
- reads the design and its own on-canvas notes,
- pulls real competitor screens from Mobbin for each pattern, deliberately mixing same-industry and cross-industry examples,
- returns a self-contained HTML report: a competitive-position scorecard, findings by dimension (navigation, UX copy, visual, accessibility) each tied to a real example and the exact Figma layer, a cross-industry prevalence table, and prioritized fixes.
It's a first pass. It compresses the "sign up for apps, screenshot, catalog how others solve this" research so your time goes to judgment, not gathering. You decide how much of the score to take: a junior designer can read it as guidance, a senior as a currency check.
Most design-review tooling does one of two things: it critiques a screen in isolation, or it generates UI from a spec. Neither answers the question you have before shipping, which is how this compares to what real competitors already do. ux-proofsheet grounds every finding in a real shipped screen, and it looks across industries on purpose, because a pattern that holds in fintech, travel, and social is a stronger signal than one that's just common in your niche.
I lead product and came up through design. The way I stay sharp is hands-on: I use apps in our space, screenshot them, and keep my own library. This skill extends that to the apps I can't sign up for and to completely different industries, and it goes down to the element level (every good navigation in the database, not just whole flows). I feed its first pass into my own design reviews, layer my hands-on research and product intuition on top, and make the call. It keeps me current and lock-step with designers who research full-time, and it lets me turn around specific, detailed feedback fast, which unblocks engineering and gives sales something to show. The judgment stays human. This compresses the part a machine does better.
- Evidence over opinion. Every finding points to a real competitor screen or a cited source. No "I think."
- Convention isn't the goal, and popular isn't correct. It separates "you match the market" from "you deviate, and that's your edge" from "you deviate, reconsider," and it only calls something a convention when the direct set agrees.
- Never fake it. If it can't source a screen, a token, or a contrast number, it says so instead of inventing one.
- UX, not pixels. Screens in the report are clean recreations from the design's own tokens, self-contained and permanent; the exact pixels are one click away in Figma.
- Triggering eval. 18 realistic prompts against the skill next to its sibling design skills; it fired on the competitive-review asks and stayed out of the near-misses. Reproduce it live:
python evals/run_trigger_evals.py. (evals/) - A real run. Pointed at an actual design refresh, which surfaced real problems in the skill (fragile image handling, scope on large boards); fixed, and written up.
Best with the Mobbin and Figma MCPs connected. No Mobbin? It falls back to web research for real examples, so you still get evidence, just links instead of screenshots.
/plugin marketplace add mananritwik/ux-proofsheet
/plugin install ux-proofsheet
Or drop skills/ux-proofsheet/ into your skills directory, or upload the packaged .skill file to Claude.
skills/ux-proofsheet/SKILL.md: the workflow.references/: the critique rubric, the evidence model, and the Mobbin search recipes. Useful as a design-review method on their own.scripts/: a WCAG contrast checker and an image helper.evals/: the triggering set and the results.
Small, paced to real use. Near-term steps are tracked in ROADMAP.md; the larger arcs are a quantified design-token layer next, then a companion that turns a review into a starter design spec. Ship a sharp small thing, improve it in public.
Built with Claude Code. It's a benchmark of patterns, not a full audit, and its competitor evidence leans on Mobbin. Use the reports for your own design work rather than to republish competitor screenshots (see Mobbin's terms).
MIT.