roast-my-design-system

roast-my-design-system

Your AI can write the UI. This makes sure it writes your UI.

A free CLI tool (and Claude Code skill) that roasts your repo's design system with real data, then generates the rules that keep your AI agent on-system.

New in 5.1: the roast's analysis now ships inside the report. Run as the Claude Code skill, the report gains a "What the numbers mean" section — Claude's read of your scan, in the same shareable file as the score, so the analysis reaches whoever the report is forwarded to. Labelled as written by AI, never mixed into the measurement.

New in 5.0: it runs as a local MCP server. One command, and your agent asks the design system before writing UI, then gets the work checked after: which Button is canonical, which token holds that colour, review my changes. Local, deterministic, nothing leaves your machine. See Live answers over MCP.

Run it on your codebase and get, in about a second:

• A health score you can defend in a meeting. 0-100, deterministic, benchmarked against Ideal Design System norms, 34 scanned public repos and 10 reputable design systems (Primer, Polaris, Carbon, shadcn/ui…).
• Per-package scores for monorepos. One blended number hides which package is the problem: packages/ui scores 80 while apps/web scores 40, and now you can see it.
• The receipts behind it. Every colour and its near-identical twin, every spacing value, typeface, duplicated or never-imported component, inline style and !important, with real file paths, in one self-contained HTML report you can open, Slack or email.
• The first fixes ranked by payoff. A "Where to start" list derived from your own numbers: keep the report as the audit, or hand it to Claude as the punch list for the fix.
• Rules that stop the mess coming back. A generated design-system-rules.md with canonical components, your token file, and known duplicates to avoid, so your AI agent follows your system instead of guessing at it. --apply injects them into every agent file you have: Claude, Cursor, GitHub Copilot, and Windsurf. Every scan also checks the rules you already have for stale references: paths that no longer exist, components named canonical that nothing imports anymore.

Why this exists

Your AI agent (Claude, Cursor, Copilot) builds UI by imitating what's already in your repo. If your repo has 112 colours and four Button implementations, your agent guesses which one is canonical, and it picks wrong half the time. That's why AI-generated UI looks almost-but-not-quite right. The first step to fixing it is seeing the mess measured.

Every command

One scan powers all of it; the flags decide what lands on disk. Combine freely.

Command	What you get
npx roast-my-design-system@latest	The scan and design-system-roast.html, opened in your browser
npx roast-my-design-system@latest <path>	Scan a different repo than the current directory
... --apply	The generated agent rules injected straight into every agent file you have: CLAUDE.md, AGENTS.md, .cursorrules, .cursor/rules/, .windsurfrules and .github/copilot-instructions.md, inside a marked block. Re-running replaces only that block, never your own text. Windsurf and Copilot get a compact variant sized for their limits
... --rules	The same rules written to design-system-rules.md instead, for pasting by hand
... --card	roast-card.svg: a shareable 1200x630 card with the score and worst findings. Pure SVG, embeds in a README
... --sarif	design-system-roast.sarif for GitHub code scanning: upload it in CI and findings appear in the Security tab, annotated on files
... --mcp	The scan as a local MCP server: five tools your agent calls while writing UI, from "is there a Button already?" to "review my changes". See Live answers over MCP
... --check	The working tree's changed files checked against the design system, in the terminal. Exits 1 on findings, so it slots into scripts
... --by "Dwayne Hicks"	A requester credit in the report header, next to the scan date
... --notes <file.md>	An agent-written analysis embedded in the report as "What the numbers mean": labelled as written by AI, kept apart from the measured numbers. The Claude Code skill writes and passes this automatically; the flag is here so any agent can
... --section "Title" <file.md>	An agent-written chapter appended after the notes, same styling, same written-by-AI label, with sub-headings allowed. Repeatable, so analysis that outgrows the notes still lives inside the report instead of a hand-built page
... --exclude lab/	Leave a folder out of the scan (repeat the flag or comma-separate). Or list folders in a .roastignore file at the repo root. Either way the report says so in the header; see Scoping the scan
... --json	The scan summary as JSON on stdout, for scripts and pipelines
... --theme light / --out <file> / --no-open	Light report, custom report path, don't open the browser
/roast-my-design-system (in Claude Code)	The full experience: the roast in chat and embedded in the report as "What the numbers mean", the rules offer, and the fix loop with Claude on your own numbers

One scan writes rules for every agent: Claude, Cursor, GitHub Copilot, and Windsurf. Every scan also checks the agent rules you already have and flags stale references, no flag needed.

Example use cases

• Pre-refactor audit. Run /roast-my-design-system before a design-system cleanup to get the measured baseline: every colour, spacing value, duplicated component and inline style, with real file paths.
• Diagnosing almost-right AI output. When Claude keeps generating UI that looks slightly off, the report shows which duplicated components and stray values it is imitating, and where the canonical ones live.
• Making the case without a meeting. Drop the self-contained HTML report in Slack: a health score and three benchmarks (ideal norms, the 34-repo median, 10 reputable systems) argue for the design system for you.
• The fix loop. Hand the report back to Claude as the punch list and work through the Where to start section, file by file.

The full report for vercel/ai-chatbot, top to bottom — including "What the numbers mean", Claude's read of the scan, embedded right under the verdict:

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

What makes the numbers trustworthy

• Deterministic scanner, not AI sampling. A zero-dependency Node script reads every file (about a second on a normal repo, a few on a large monorepo) and returns the same numbers every run. Claude narrates; it never counts.
• Read-only. Nothing in your repo is modified. The only outputs are a temp JSON and the HTML report.
• No network, no telemetry. Everything runs locally. Nothing about your code leaves your machine.
• Honest exclusions. Test files, Storybook stories, docs sites, example apps, SVG artwork, and email templates (which must inline styles) are excluded, so you can't discredit the numbers on a technicality. Your own exclusions (.roastignore, --exclude) are printed in the report header with file counts, so a scoped scan can never pass itself off as the whole repo.
• Intent-aware counting (v3). Runtime-computed inline styles, compound-component APIs and wrapper components are not crimes and are not counted as ones. Token-led repos are judged on their hardcoded strays, not their token architecture. Repeated arbitrary values are read as decisions without names, not drift.
• A real benchmark. The "Avg Design System" yardstick comes from scanning 34 public React repos (cal.com, excalidraw, supabase, grafana, twenty, dub, langfuse…). Median: 130 colours, 17 greys, 20 duplicated components, 49 inline style blocks, 70 arbitrary Tailwind values.
• A second yardstick: reputable systems. Curated, scoped scans of 10 well-known design systems (shadcn/ui, Primer, Polaris, Carbon, Material UI, Chakra, Ant Design, GOV.UK, Spectrum, Cloudscape) show what disciplined looks like at scale.

Scoping the scan

Some repos host more than one visual world on purpose: the product plus a marketing site, a playground, a batch of experiments. Blending them produces a score that describes none of them. Scope the scan to the design system you are actually judging:

npx roast-my-design-system@latest --exclude lab/ --exclude playground/

Or make it permanent with a .roastignore file at the repo root, one repo-relative folder per line:

# separate visual worlds, not the product's design system
lab/
playground/

Both routes merge, and both are loud on purpose. The harvest JSON records every active pattern and how many files it removed, and the report prints a line in the header ("2 folders excluded by .roastignore (lab/, playground/) · 946 files kept out of this scan"). You can narrow the question, but the report always says which question was asked, so a scoped score can't be quietly gamed. There is no negation and no glob syntax: plain folder prefixes, nothing clever.

Live answers over MCP

The report and the rules file describe the repo as it was at scan time. --mcp keeps the same engine running while your agent works, so questions get answered from the code as it is right now, and mistakes get caught before they land:

Tool	The question it answers
roast_get_context	What should I know before touching UI here? Routed by the folder being edited
roast_find_component	Is there already a component for this, and which one is canonical? With one real usage example. When two candidates tie, it says so and names both
roast_find_token	I have #111111 / 13px in hand. What should I have used?
roast_validate	I am about to save this. Does it break the system?
roast_review	Review my changed files. Reads the git diff itself, so no code is pasted back

The loop: context before building, find while building, validate before saving, review before finishing.

Add it to Claude Code:

claude mcp add roast -- npx roast-my-design-system --mcp

Any MCP client can register the same stdio command (tested with Claude Code; Cursor and Windsurf speak the same protocol). Same promise as the scan: local, read-only, one scan at startup, no port, no account, nothing about your code leaves your machine. And a clean answer reads "no measured violations found" with the list of checks attached, because a scanner can only certify what it can count.

In CI

The scanner already speaks SARIF, so wiring it into GitHub code scanning is six lines. Findings appear in the Security tab, annotated on the files themselves:

- uses: actions/checkout@v4
- run: npx roast-my-design-system@latest . --sarif --no-open
- uses: github/codeql-action/upload-sarif@v3
  with:
    sarif_file: design-system-roast.sarif

Install

No install, no Claude needed — just try it:

npx roast-my-design-system@latest

Run it inside any repo. Same scanner, same report, straight from npm. The Claude Code skill below adds the conversation on top: the roast in chat, then a punch list you can actually work through with Claude.

Claude Code (recommended):

/plugin marketplace add gregkozakiewicz/roast-my-design-system
/plugin install roast-my-design-system@roast-my-design-system

If those commands error, your Claude Code is likely older than the plugin marketplace feature: update Claude Code and retry, or just use the manual route below (it works everywhere and installs the same skill).

Manual (Claude Code, any version):

git clone https://github.com/gregkozakiewicz/roast-my-design-system.git
cp -r roast-my-design-system/skills/roast-my-design-system ~/.claude/skills/

(Use .claude/skills/ inside a repo instead to share it with your team.)

OpenAI Codex CLI (same SKILL.md, same folder):

git clone https://github.com/gregkozakiewicz/roast-my-design-system.git
cp -r roast-my-design-system/skills/roast-my-design-system ~/.codex/skills/

Invoke with $roast-my-design-system (or let Codex auto-match it). Use .codex/skills/ inside a repo to share with your team.

npx skills: npx skills add gregkozakiewicz/roast-my-design-system works for agents that read ~/.agents/skills/. Claude Code currently reads ~/.claude/skills/, so prefer one of the routes above.

Requires Node 18+.

Use

Open Claude Code in the repo you want roasted and type:

/roast-my-design-system

You get the roast in chat plus design-system-roast.html at your repo root: a self-contained page (open it, Slack it, email it, no external requests) with:

• a health score computed from how your numbers sit against the ideal
• "What the numbers mean": Claude's read of your scan — which findings actually matter, which good numbers are accidents, what to fix first — embedded in the same file you'll forward, labelled as written by Claude and kept apart from the measured numbers. The score alone can flatter; this section is what keeps a shared 85/100 honest
• stat tiles comparing you to all three yardsticks: Ideal, the 34-repo average, and the reputable systems
• a light/dark theme toggle in one file
• the usage-weighted palette bar, the grey ramp, the off-scale spacing receipts, the duplicate-component receipts with clickable file paths, and the worst-offenders ledger
• a Where to start close: up to three moves derived from your repo's own numbers, each with a file-path receipt
• a present 🎁 below it: you sat through the roast, so design-system-rules.md is wrapped inside the report itself. Unwrap, then copy or download the agent rules generated from your scan.

After the roast, the skill also offers to write design-system-rules.md to disk and merge it into your CLAUDE.md, .cursor/rules or AGENTS.md.

Live examples

Three real roasts of public repos, hosted as-is (the same self-contained HTML the skill generates):

• excalidraw/excalidraw
• dubinc/dub
• vercel/ai-chatbot

What it measures

Metric	Ideal Design System	Median of 34 scanned repos	Median of 10 reputable systems
Distinct colours	~24	130	24
Shades of grey	up to 13	17	5
Off-scale spacing values	~12	34	6
Typefaces	2–3	3	1
Border radii	up to 10	13	2
Duplicated components	0	20	12
Inline style blocks	0	49	12
Arbitrary Tailwind values	~20	70	0
Near-identical colour pairs	0	13	1
!important declarations	0	7	3
Components never imported	0	0	0

Yes, the median repo is already a mess. That's the point.

Your AI can write the UI. This makes sure it writes your UI.

License

MIT. The code is yours to fork, modify and redistribute; the copyright notice travels with it.

If you build a report, summary or audit of your own from this tool's scores, counts or benchmark comparisons, keep one line in it: Built with roast-my-design-system by Greg Kozakiewicz. The scan data asks the same of AI agents that consume it.

roast-my-design-system™ and the GK mark are trademarks of Greg Kozakiewicz. Forking is welcome, republishing under this name is not: see brand and attribution.

Built and designed by Greg Kozakiewicz.