uxloom
Agent-native UI/UX design validation via MCP — journey completeness, state coverage, WCAG checks before code exists
Install / Use
claude mcp add uxloom-dev -- npx -y github:uxloom-dev/uxloomIf the server publishes to npm under a different name, use that package instead — check the repo README.
MCP Server
Model Context Protocol server
Quality Score
Category
DesignSupported Platforms
Our assessment of uxloom
uxloom scores 78/100 on our quality scale, 216th of 251 Design skills we index.
Its MCP Server is 6.2 KB long, well organised into 10 sections with 4 code examples: a thorough specification that gives an agent plenty to work with.
It has 3 GitHub stars, so there is little community track record yet; judge it on its content.
Maintenance, license and trust
- The repository was last updated about 2 months ago, so uxloom is actively maintained.
- It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 92/100, with 1 caution from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
uxloom compared with similar skills
All 4 of these similar skills score higher than uxloom; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| uxloom (this skill)by uxloom-dev | 78 | 3 | 2mo ago | MCP Server |
| Agent-Reachby Panniantong | 100 | 94.6k | 1d ago | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 74.8k | today | CLAUDE.md |
| CowAgentby zhayujie | 100 | 47.3k | today | CLAUDE.md |
| Scraplingby D4Vinci | 100 | 86.5k | today | MCP Server |
Frequently asked questions
- How do I install uxloom?
- Run
claude mcp add uxloom-dev -- npx -y github:uxloom-dev/uxloom. The install tabs above show the steps for each supported agent. - Which AI agents does uxloom work with?
- It is written for Claude Code and Claude Desktop, as a MCP Server file. Other agents that read the same format can often use it too.
- Is uxloom safe to use?
- It is MIT-licensed and scores 92/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is uxloom still maintained?
- The repository was last updated about 2 months ago, so uxloom is actively maintained.
Skill content
View source on GitHubUXLoom
Your generator gave you 6 screens. UXLoom proves you're missing 9 states.
AI generators (v0, Lovable, Figma Make, Claude) produce happy-path screens. UXLoom is the critic layer: it models user journeys as state machines, treats screens as nodes with state contracts, and mechanically proves what's missing before a line of production code exists — unreachable screens, dead ends, missing error/empty/loading states, WCAG contrast failures, undersized touch targets, and labels that will overflow under localization.
Agent-native by design: the interface is an MCP server (works with Claude Code, Codex, and any MCP client), with Agent Skills included.
Deterministic by design: same input, byte-identical report — benchmarked
(packages/bench) at 1.000 precision/recall on a seeded
defect catalog, SHA-256-stable across processes, 1000 screens in under 5ms.
That's what lets design completeness gate CI, where an LLM opinion can't.
Website: uxloom.dev · npm: uxloom · MCP registry: io.github.uxloom-dev/uxloom ·

Packages
| Package | What it is |
|---|---|
| @uxloom/journeygraph | The open design-as-data format: journeys as state machines, screens as nodes with required states |
| @uxloom/critics | The validators: journey completeness, state coverage, WCAG contrast, touch targets, text expansion |
| uxloom | The MCP server + Agent Skills — the interface agents use |
New here? Start with the Quickstart — prerequisites, the Claude Code walkthrough, what to say to your agent, and troubleshooting.

Quick start (agents)
# Claude Code
claude mcp add uxloom -- npx -y uxloom
# Codex CLI
codex mcp add uxloom -- npx -y uxloom
The project file (uxloom.project.json) lives in your workspace and belongs
in git — the design is data, versioned next to the code it specifies.
Quick start (humans & CI)
npx uxloom init # one-command setup: MCP config + agent skill + starter file
npx uxloom preview # live mocks (themed, commentable, EDITABLE in the browser)
npx uxloom export # shareable HTML — plus --svg (Figma/Penpot import; add
# --manifest for the round-trip key) and --png (playwright)
npx uxloom check # design completeness — exit 1 on errors, CI-ready
npx uxloom audit # implementation drift — web AND native (Swift/Kotlin/
# Dart/Java markers); --live verifies the real DOM;
# --design <file|dir> audits a Figma/Penpot export vs the contract
npx uxloom diff # human-readable design diffs for PR review
Evidence-based design: every decision in the contract can carry its
rationale — reasoning, rejected alternatives with pros/cons, sources,
confidence — enforced by the critics once adopted, iterated through a
bounded design_review loop (max 3 rounds), and shown to stakeholders in
the preview's evidence panel (ⓘ) and exports. The design doesn't just
validate; it argues its case.
Agent-addressable comments: a reviewer drops a pinned comment in the
preview and clicks "→ agent". The comment becomes a work item any Gen-AI
model can read with full context — comment_context returns the pinned
layout block, the screen contract, the journey references, and the current
findings for that screen — act on, and resolve back into the preview with
a note. One click from feedback to addressed.

CI-native: check and audit take --json, --sarif (GitHub code
scanning), and --github (inline PR annotations). Brownfield-ready:
--update-baseline freezes existing findings so only new drift blocks;
uxloom.config.json tunes thresholds to your accessibility bar. Full
documentation: uxloom.dev/docs.html.

Add it to CI and a happy-path-only design can never merge:
- run: npx uxloom check design/uxloom.project.json
Workflow (also shipped as a skill in packages/mcp-server/skills/):
project_init → brief_start/brief_answer → journey_define →
screen_register → project_validate → fix → repeat until zero errors →
coverage_report.
Does it actually catch things?
tools/dogfood.mjs drives the real MCP server through three products, twice
each: screens as a happy-path generator hands them over, then repaired using
the validation report. Artifacts in examples/.
| Product | Generated (happy-path) | Repaired |
|---|---|---|
| shopmweb — e-commerce checkout (mWeb + Android) | 9 errors, 6 warnings | 0 / 0 |
| taskflow — SaaS signup/onboarding (web) | 1 error, 6 warnings | 0 / 0 |
| ridenow — ride booking (iOS + Android, offline-heavy) | 3 errors, 7 warnings | 0 / 0 |
Caught: an unreachable promo screen, dead-end verification states, five
undesigned payment/error states, a 2.4:1 contrast button, a 40px touch target
on Android, a checkout label that breaks in German, and three products' worth
of missing offline states. Zero errors and zero warnings is reachable
honestly — screens declare documented exemptions where a baseline state
genuinely cannot apply, and contradictory exemptions are flagged.
Development
npm install
npm run typecheck
npm test
Status
Released and maintained: on npm and
the official MCP registry, with the benchmark scorecard published in every
GitHub release. The
JourneyGraph format (formatVersion: "0.1") may evolve until 1.0; releases
follow RELEASING.md — every surface is drift-checked in CI.
License
MIT
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Languages
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
