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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/uxloom

If the server publishes to npm under a different name, use that package instead — check the repo README.

About this skill
🔌

MCP Server

Model Context Protocol server

Quality Score

78/100

Category

Design

Supported Platforms

Claude Code
Claude Desktop

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.

Substance
29/30
Structure
20/20
Description
12/15
Adoption
3/20
Freshness
15/15

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.

SkillScoreStarsUpdatedFormat
uxloom (this skill)by uxloom-dev7832mo agoMCP Server
Agent-Reachby Panniantong10094.6k1d agoCLAUDE.md
headroomby headroomlabs-ai10074.8ktodayCLAUDE.md
CowAgentby zhayujie10047.3ktodayCLAUDE.md
Scraplingby D4Vinci10086.5ktodayMCP 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.

UXLoom

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 · uxloom MCP server

uxloom check finding 9 errors in a generated checkout flow, then passing the repaired one

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.

Connecting UXLoom to Claude Code and running the first check

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.

Reviewer pins a comment, clicks "→ agent"; the pin turns amber and a copyable prompt hands the comment to any AI agent, which reads the block and contract and resolves it

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.

Live preview: state tabs and clickable journey events on wireframe mocks

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

Related Skills

View on GitHub
GitHub Stars3
CategoryDesign
Updated2mo ago
Forks0

Languages

TypeScript

Trust signals

92/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

1 low