roast-my-design-system
AI design system scanner + mcp server. Scores repos 0-100, writes fix prompts. 30K+ npm downloads.
Install / Use
claude mcp add gregkozakiewicz -- npx -y github:gregkozakiewicz/roast-my-design-systemIf 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 roast-my-design-system
roast-my-design-system scores 84/100 on our quality scale, 171st of 260 Design skills we index.
Its MCP Server is 30 KB long, well organised into 33 sections with 16 code examples: a thorough specification that gives an agent plenty to work with.
It has 43 GitHub stars, so there is little community track record yet; judge it on its content.
Maintenance, license and trust
- The repository was last updated 12 days ago, so roast-my-design-system 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.
roast-my-design-system compared with similar skills
All 4 of these similar skills score higher than roast-my-design-system; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| roast-my-design-system (this skill)by gregkozakiewicz | 84 | 43 | 12d ago | MCP Server |
| Agent-Reachby Panniantong | 100 | 105.3k | 3d ago | CLAUDE.md |
| taste-skillby Leonxlnx | 100 | 104.0k | today | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 78.1k | today | CLAUDE.md |
| CowAgentby zhayujie | 100 | 47.6k | today | CLAUDE.md |
Frequently asked questions
- How do I install roast-my-design-system?
- Run
claude mcp add gregkozakiewicz -- npx -y github:gregkozakiewicz/roast-my-design-system. The install tabs above show the steps for each supported agent. - Which AI agents does roast-my-design-system work with?
- It is written for Claude Code, Claude Desktop, Cursor, Windsurf, OpenAI Codex and Devin, as a MCP Server file. Other agents that read the same format can often use it too.
- Is roast-my-design-system 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 roast-my-design-system still maintained?
- The repository was last updated 12 days ago, so roast-my-design-system is actively maintained.
Skill content
View source on GitHubroast-my-design-system
Find where your AI agent will invent UI.
Your AI can write the UI. This makes sure it writes your UI.
npx roast-my-design-system@latest
Run it at the root of a UI repo. One second later, a report opens. No account. No network. No telemetry. Nothing in your repo changes.
Version history is in CHANGELOG.md.
The idea
An agent copies what it finds.
It invents where the repo has no answer.
We tested this on 10 real open-source products, in 355 agent sessions, with and without this tool.
Findings each session added, counted by this tool's own rules:
| Task | Model | When | Without roast | Roast MCP and rules installed | Roast plugin with its edit hook | |---|---|---|---|---|---| | A new chart and a new component, 4 products, 3 runs each | Sonnet 5 | Oct 2026, roast 10.1 | 21 in 24 runs | not run | 7 in 24 runs | | A new chart and a new component, 4 products, 3 runs each | Haiku 4.5 | Oct 2026, roast 10.1 | 72 in 24 runs | not run | 1 in 24 runs | | Add a panel, build a dashboard, tighten a list (80 sessions) | Sonnet 5 | Sept 2026 | 7 | 9 | not run | | A Christmas theme, 10 products | Sonnet 5 | Sept 2026 | 28 | 2 | not run | | A new chart, 4 products, 5 runs each | Sonnet 5 | Sept 2026 | 10 in 20 runs | not run | 0 in 20 runs | | A new chart, 4 products | Haiku 4.5 | Sept 2026 | 25 | 39 | 0 in 14 of 15 runs | | Chart, status colour, empty state, new component | Haiku 4.5 | Sept 2026 | 42 in 16 runs | 49 in 16 runs | 3 in 51 runs |
Routine work stayed on-system with or without the tool. Work that needed something new did not.
An agent does not always call a tool when it should. In October, Sonnet called the MCP tools in 7 of 24 sessions and Haiku in 2 of 24. The edit hook runs without being asked. That is the column with the lowest numbers.
Where did the agent invent? Where the repo had nothing to copy. Dub has no chart palette. Its own charts hardcode 17 colours. Asked for a chart, Haiku hardcoded 9 more. In October, 5 of the 7 findings Sonnet left with the plugin on were chart colours on Dub. The agent kept them and wrote a comment saying the repo has no chart palette.
So the mess an agent adds is a map of the gaps in your system.
This tool draws the map.
Roast. Teach. Guard.
Roast the repo to find its real design system, the mess in it, and the gaps.
Teach the agent, through generated rules and a local MCP server.
Guard every edit, so the agent does not have to remember to ask.
ROAST what is in the repo, and what is missing
↓
TEACH rules in the agent files, answers over MCP
↓
GUARD every edit checked, the PR gated
What you get
Where will your agent have to guess?
The first thing the report says: the places where the repo has no answer yet, or "No gaps found".
- the gap, in one line
- the files that prove it
- the one move that closes it
One gap is known for certain today, because the runs found it: charts hardcoding colours in a repo with no chart palette. 43 of 126 public repos look like that. A gap joins the list when it has been measured.
Fixes you can make right now to increase the health score
The mess the agent will copy, ranked by what fixing it is worth.
- what was found
- where, with the file path
- why it matters
- what to change
- a copy button with the fix prompt for your agent
Fix, rescan, press the next button.
A score you can defend in a meeting
0 to 100. The same number every run.
Measured against three yardsticks: the ideal norms of a design system, the median of 34 product repos at the core of a 119-repo benchmark, and 10 reputable systems (Primer, Polaris, Carbon, shadcn/ui and others).
Monorepos get a score per package. packages/ui at 80 stops hiding apps/web at 40.
Every finding, with its file path
- every hardcoded colour, and its near-identical twin
- every spacing value off the scale
- every duplicated component
And 8 more kinds, all in What it measures. One HTML file. Open it, Slack it, email it.
The colour usage bar shows where the repo's colour comes from: of every 100 colour uses, how many read a theme colour by name, how many use Tailwind's palette, and how many are strays written by hand. It follows CSS variables, Tailwind themes, Sass and Less variables and JavaScript theme objects to do the counting.
The header names what the repo is built on: shadcn/ui, a Tailwind theme, MUI, Mantine, Chakra or Ant Design, and both kits when a repo uses two.
Rules for your agent
Generated from your repo, into design-system-rules.md:
canonical components
the token file
known duplicates to avoid
spacing steps
typefaces
the kit's own vocabulary
--apply writes them into every agent file you have: Claude, Cursor, GitHub Copilot, Windsurf. Every scan also checks the rules you already have for stale references.
A script does the counting
Every number comes from a deterministic read of your files. Claude writes the explanation, labelled as written by AI and kept apart from the numbers.
How the runs were done
Claude Code, headless, on 10 public products pinned to one commit each: cal.com, Dub, Metabase, Plausible, SigNoz, trigger.dev and four more. 259 sessions in September 2026 and 96 in October 2026 on roast 10.1, with Sonnet 5 and Haiku 4.5. The October sessions repeat the chart and new-component tasks on Plausible, SigNoz, trigger.dev and Dub at the same commits. Every changed file was judged by this tool's rules at the end of the session and at the pinned commit. A finding counts only if the session added it.
Nothing was rendered. Zero findings means the code follows these rules. It does not mean the design was reviewed. Method, tables and limits are in the research write-up, which will be published separately.
Live examples
Eleven reports, hosted exactly as the tool writes them. Every number deterministic, every path real.
- npx shadcn create, fresh (factory install, all 61 components): read as a fresh install, "the score is the kit's, not yours"; 13 colours, every theme variable in place, shadcn's own 24 bracket values named and not counted. No score.
- Unleash (MUI): 1,109 files import the kit and the theme is read 6,166 times; 4 colours and 2 spacings per 100 kit files are written onto components. Score 60.
- Metabase (Mantine): its own wrapper over Mantine counts as the kit, so 2,679 files are read instead of 392; nothing written onto components, 2 spacings per 100 kit files. Score 47.
- SigNoz (Ant Design): 2 colours and 7 spacings per 100 kit files written in style objects where a token exists. Score 51.
- Apache Airflow (Chakra UI): no colours written onto components, 4 spacings per 100 kit files as pixel strings where a space step exists. Score 43.
- vercel/ai-chatbot (shadcn install): 71 values like [13px] written outside the Tailwind scale, and 66 palette colours per 100 files where a theme variable exists; Claude's notes embedded. Score 80.
- excalidraw/excalidraw: 78 off-scale spacing values and 90 !important declarations. Score 55.
- dubinc/dub: 642 arbitrary bracket values, 21 duplicated components, and the chart-palette gap named. Score 20.
- telekom/scale (Stencil): 95 Stencil components read by tag; 66 spacing values outside the scale where about 12 would do; Claude's notes embedded. Score 55.
- magicuidesign/magicui (registry): counted on the components it publishes, 52 off-theme colours per 100 files in the code it ships, its docs site kept out and named. Score 78.
- adobe/spectrum-web-components (Lit): 740 colour tokens with 8 hardcoded colours beside them, and 37 !important declarations. Score 83.
The full report for vercel/ai-chatbot. The verdict answers what the agent will learn here, the gap section comes first, then "What the repo teaches the agent", Claude's read of the scan:

The same report in light mode (one file, built-in toggle):

Why the numbers hold
- Deterministic. A zero-dependency Node script reads every file and returns the same numbers every run. About a second on a normal repo.
- Read-only. No network. No telemetry. The test suite fails if package.json ever declares a dependency. Every release runs 468 checks against 29 sample repos before it is tagged; the fixtures and their snapshots live in
tests/, one JSON file each. - Honest gaps. What the scan cannot read says "not measured" and drops out of the score.
- Honest exclusions. Tests, stories, docs sites, artwork and email templates are left out. Your own exclusions are printed in the report header with file counts.
- A real benchmark. 119 public repos. A core fleet of 34 React repos sets the medians; the rest feed the kit and web-component groups, so a shadcn repo is compared with shadcn repos and a Lit system with Lit and Stencil systems. The builder is in
tools/benchmark/. - Importable scoring.
scoreHarvest(harvest)returns the sc
Truncated for display — read the full file on GitHub.
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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.
