Impeccable: a design vocabulary for agents is mostly a linter with opinions

Impeccable’s home page has one line of pitch: “The missing design vocabulary for agents. Turn AI slop into interfaces you’re proud to ship.” Under it is a card. You can drag a slider across it and watch a beige release-planning card with a purple left stripe become a plain white one. The site’s star badge reads 76k. I wanted to know what a “design vocabulary” for an agent actually is when you open the box. Is it a list of things not to do, a set of principles, or a tool that measures something?

I expected a long prompt, like most skills I have read here: figures4papers is a house style written as a SKILL.md, and the one-shot launch video skills are instructions handed to a framework. What I found is a long prompt and a linter. The linter is larger than everything else in the repository put together, and it holds most of what is new.

What arrives in your agent

The product is one skill. skill/SKILL.src.md is 90 lines. It opens by giving the model a job title:

“This skill gives you the tools and permission to create design that earns to be called out-of-distribution craft: Whereas before, your design work would have been safe, timid and measured, you now approach every design task as an award-winning design director”

skill/SKILL.src.md:12

The rest of the file is a router. A table maps 24 commands to reference files: shape, critique, audit, polish, bolder, quieter, distill, harden, clarify, typeset, layout, colorize, animate, live and the rest. One of the 24, craft, is a deprecated alias, and the repo’s own CLAUDE.md counts 23. The table points into skill/reference/, which holds 41 Markdown files and about 60,000 words. An agent loads only the file for the command it runs, so the weight is paid per request. It still adds up: new-work.md alone is 9,389 words, which is more than this article.

Three instructions in SKILL.md hold the rest together. First, run the engine’s context verb once per session; it loads PRODUCT.md, DESIGN.md and a per-surface brief (:21). Second, load the command’s reference. Third, read craft-floor.md “immediately before any UI edit, including small refinements” (:23). That last file is where most of the vocabulary actually lives, and I come back to it below.

Packaging is a build step, not a hand-maintained fork per tool. scripts/build.js transforms the one source tree into a copy per harness, driven by scripts/lib/transformers/providers.js: .claude/skills/, .cursor/skills/, .agents/skills/ for Codex, .github/skills/ for Copilot, .gemini/, .opencode/, .grok/, .hermes/, .dsh/ for DeepSeek Harness, Kiro, Pi, Qoder, Trae (two locales), Rovo Dev, Mistral Vibe, Veto and Antigravity. The transform substitutes the script path and command prefix, keeps or drops provider blocks such as <codex>…</codex>, and strips 151 distinct <!-- rule:… --> markers from the text (scripts/lib/utils.js:589). I found nothing in the public repo that reads those markers, so I assume the private evaluation harness does. All the generated copies are committed. They account for 1,178 of the repository’s 3,897 files and 43.7 MB of its 85 MB working tree.

Each copy ships a 206-line POSIX launcher, scripts/impeccable. On first run it downloads the engine for your platform from the GitHub release into ~/.impeccable/bin/, and it refuses to run a binary whose .sha256 sidecar does not match. That check catches a corrupted download. It does not prove who built the binary, since the sidecar comes from the same host. Where a harness has hooks, the installer wires them up. Claude Code gets PostToolUse on Edit|Write and a Stop deep pass (plugin/hooks/hooks.json). Cursor gets a preToolUse gate that denies a bad write before it lands. Codex needs /hooks approval after every update that changes the hook. DeepSeek Harness gets the skill but no hook, because its hooks are in-process plugins rather than files on disk.

Three kinds of vocabulary

Read in full, the skill text sorts into three piles of very different size.

The first pile is principles, and it is small. SKILL.md names four visitor modes: Persuade, Operate, Read and Experience. It says the mode follows the surface, not the product, so “a tool’s landing page is still Persuade” (:43). It also says “the brief wins”: “Honor pinned aesthetics, eras, materials, fonts, and palettes even when they conflict with a saturated-pattern warning. Redirecting a clear brief toward your taste is failure” (:29). The mode files add sensible defaults. A Read surface keeps “a reading face, real contrast, a real measure, nothing performing behind the text” (mode-read.md:7), and Operate prefers one family and a 1.125 to 1.2 type scale (operate.md:13-15). None of this is new to a designer. It is new to a model that has never been told which job the page is doing.

The second pile is refusals, and it is the heart of the product. craft-floor.md is 52 lines with a “Verify” list and a “Refuse” list. Most of the Refuse list says “these are the category’s defaults, not bans” (:21). One item is phrased harder than any other line in the skill:

“A kicker or eyebrow above a heading. This one is a ban, not a default: no brief earns it back.”

skill/reference/craft-floor.md:27

The rest of the list is the home page’s before/after cards in prose. It covers a coloured border-left above 1px (:35), gradient text (:33), nested cards, “always wrong” (:25), monospace “as a costume for ‘technical’” (:38), and the hero-metric template (:26). Then there is a <codex> block that only Codex sees. It bans “sketch-style SVG scenes” and feTurbulence grain, repeating-linear-gradient stripes, and dismissing things as “theater” (:44-50). Those are habits one model family has, written down for that family.

The third pile is the detector, and it is where “vocabulary” becomes something you can run.

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The ten tells on that hero are a useful test of the three piles. Five of them have a detector rule with the same meaning: side-tab border (side-tab), AI kicker (kicker-above-heading), italic serif (italic-serif-display), AI beige (cream-palette) and cards in cards (nested-cards). The other five do not. Status-chip soup, everything equal, vague headline, too many fields and generic CTA are judgments about content and hierarchy. Only the model’s review in critique, or a rewrite in clarify and distill, can catch them. Half the marketing is measurable, and the other half is the part the model still has to judge.

The catalogue, counted

The registry is crates/foundation/src/registry.rs, mirrored as JSON in crates/live/assets/antipatterns.json. It lists 59 rules. Of these, 31 are categorised “slop” and 28 “quality”. Their severities are 44 warnings, 13 advisories (listed but never counted in the exit code) and two errors (script-error, content-hidden-at-rest). The README says 59. The live home page says “61 checks”. The /slop page renders 67 entries: the 59 rules, six marked “design review” that no code checks, and two the engine has since retired. The site lives in a private repository and trails the engine by a few days.

The “slop” and “quality” labels are the authors’ categories. They do not tell you what a rule measures, so I sorted all 65 live entries by that instead. The split is my call, and the widget shows each rule’s quoted text so you can disagree with it.

Thirty-three entries are taste written down as a threshold. The authors have judged a habit to be generated, and given it a number so code can find it: cream backgrounds, Inter and Geist, purple gradients, kickers, em-dash density, “theater”. Thirteen are readability checks against standards that predate generated UI by decades: WCAG contrast, an 11px floor for functional text, line length, leading, skipped heading levels. Nine are plain defects: a broken image, a script error, text hidden at rest, text clipped or covered. Four compare your values against your own DESIGN.md. Six are left to the model.

That answers the question I came with. The vocabulary is mostly a list of refusals. About half of it has been turned into code that measures, and only about a third of the measuring code checks something a non-designer could verify from first principles.

How a taste becomes a number

The taste rules are worth reading one at a time. Each one forces a vague complaint into a decision someone can argue with. “AI beige” becomes this:

// crates/core/src/checks/measures.rs:392
pub fn is_cream_color(rgb: Option<&Rgba>) -> bool {
    let Some(c) = rgb else { return false };
    let (r, g, b) = (c.r, c.g, c.b);
    if math_min3(r, g, b) < 209.0 { return false; }
    if !(r >= g && g >= b) { return false; }
    let warmth = r - b;
    warmth >= 6.0 && warmth <= 48.0
}

A colour counts as cream when every channel is at 209 or above, red is at least green and green at least blue, and red minus blue lands between 6 and 48. An ivory of #fbf8f1 has a warmth of 10 and is cream. A warm white of #f7f7f5 has a warmth of 2 and is not. Nobody would arrive at those bounds from colour science. They are someone’s eye, made precise enough to argue about, and that precision is what makes the rule useful.

The side stripe is a geometry rule (crates/core/src/checks/rules.rs:301). A coloured border fires on a rounded card at 2px or wider, and on a square one at 3px. The other sides have to be 1px or less, or at most half the stripe’s width. Neutral greys are exempt, and so are tables, badges and anything in a status context. The em-dash rule is “advisory only” and needs both at least 8 dashes and at least one per 500 characters of body text (crates/foundation/src/rules/text.rs:398-401). The font rule is a list of 17 names, from Inter and Roboto to “the Anthropic-skill / Vercel / GitHub default wave” of Fraunces, Geist and Space Grotesk (crates/foundation/src/constants.rs:85). In the browser engine it fires only when one of them sets at least 5% of the page’s characters, a floor the code comment says sits clear of the two smallest real cases the corpus found (crates/core/src/browser/page_checks.rs:112-115).

I rebuilt thirteen of these patterns on one sample card. Every switch changes what the card renders, and the checker on the right runs a small port of the named rule with the same threshold. The background swatches include that ivory, so you can watch cream-palette fire on a colour most people would call white.

Two things stood out while I built it. The readability rules have outside anchors: 4.5:1 is the WCAG figure, and nobody has to take Impeccable’s word for it. The taste rules have only the authors’ eye behind them. Some sit on a knife edge. A beige of #f3ead8 has a warmth of 27 and would still fire at 47. Move it one notch warmer and it stops being “cream” without looking any different. That is the price of turning taste into a number. The code becomes repeatable, but its edges are arbitrary.

The clever part is the corpus

What keeps the taste rules honest is not the rules but tests/oracle/DELTAS.md. It is 7,015 lines and 109 dated entries, each recording one decision about a rule’s behaviour. Many of the decisions come from running the detector on real websites and having two judges label whether each finding actually hurt the page.

“A corpus review of 50 real sites judged all three rules on their findings. Two measure exactly what they claim and are almost never harmful where they fire (layout-transition 1,435 findings on 37 sites, harmful in 9% of the judged representatives; bounce-easing 160 findings on 13 sites, harmful in none), so their registry severity is now advisory”

tests/oracle/DELTAS.md:304-308

The same entry retires image-hover-transform outright, because “hover zoom on a card image is a long-standing convention rather than a generated-UI tell, and it fired on mobile captures where hover cannot happen” (:309-312). On 6 October, after two more cohorts in which “both judges called every finding harmless”, layout-transition was removed from the registry too (:7006-7008). The count dropped to 59.

I think this is the best idea in the project. Most lint rule sets only grow. This one gets taken back to the web, measured for harm, and trimmed. Most entries are about precision, not recall. Known consent banners are hidden before a URL scan (DELTAS.md:4576). Ad-tech script errors become advisory and name the vendor, sliders that never started are left out of the hidden-text count, and a purple heading that matches the brand’s own logo colour stops counting as an AI palette (docs/CLI-CONTRACT.md:354-359). Each carve-out is a false positive somebody hit on a real page.

It also shows the cost. Those carve-outs are why the engine is 211,930 lines of Rust across 384 files. The browser rules alone run to 9,004 lines in element_checks.rs and 6,891 in page_checks.rs. The engine is a port of an earlier JavaScript engine. docs/PORTING-GUIDE.md:20 says “Port the behavior, including bugs”, and 1,410 test files in tests/oracle/ pin the Rust output byte for byte to the JS goldens.

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Three engines, and an ordering rule

impeccable detect picks an engine from its target. Source files (.tsx, .vue, .css and so on) go through regex matchers. HTML files go through a static engine with its own parser and CSS cascade. A URL goes to a real Chrome over CDP, which renders the page, sweeps it for scroll reveals, reads computed styles and the painted geometry, and samples screenshot pixels where text sits on an image. The same core compiles to WebAssembly for the Chrome extension, a 2.7 MB generated bundle in crates/live/assets/. No engine calls a model, and none needs an API key.

The hooks split the rules into two tiers. After each edit, only “mechanical, unambiguous problems worth interrupting an edit for” are surfaced: broken images, overflow, contrast, gradient text, glow, design-system drift. Copy cadence, palette taste and layout rhythm wait for a deep pass on Stop (skill/reference/hooks.md:7). This is a good call. An agent that gets told about em-dashes on every keystroke learns to ignore the hook.

The cleverest rule in the skill text governs the model and the detector together. critique runs two assessments as separate sub-agents: a design review by the model, and the detector plus a browser pass. The order is enforced:

“Assessment A must finish before detector findings enter the parent synthesis context. Detector output is deterministic, but it still anchors judgment.”

skill/reference/critique.md:10

A run without sub-agents has to start its report with a ⚠️ DEGRADED banner (:9). Whoever wrote that has watched a model read a lint report and then “find” exactly those issues and nothing else. It is the same discipline as checking an agent’s work with something that is not the agent, which is what I looked for in three other harnesses.

Live mode, the gallery and the dice

/impeccable live is the one part that needs a running dev server. The engine injects a picker into your local app (Vite, Next.js, SvelteKit, Astro, Nuxt, Bun or static HTML; no native apps, no deployed sites). You select an element and describe the change. The agent writes three variants that are hot-swapped in place through HMR, and when you accept one, it is written back into your source. Under the hood it is a long-poll loop. live.md tells each harness how to hold it: a background task in Claude Code, a one-shot background terminal in Cursor, a foreground exec session that Codex must “SERVICE” (skill/reference/live.md:26-30). generate does the same for a named element, without the picking.

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The gallery is the closest thing to a benchmark the project publishes. Seven briefs, each run on the same model with and without Impeccable. The page says “first-pass results, before manual polish”, with “image generation available to both”. Without Impeccable there are five runs per brief, and with it three.

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The Observability row shows the claim better than any testimonial. The five baseline runs are the same page five times: cream ground, black headline, “the smallest trace that explains” in the first line. The three Impeccable runs differ from each other. That difference comes less from the anti-pattern list than from a mechanism the research page calls “The model can’t roll its own dice”. Across sixteen creative framings, “Thirty of thirty-five responses proposed the same concept”. When the model ranked its own shortlist, “option one won in 27 of 30 packets”. So the skill has the model propose directions and has a script pick one: concept-seed “assigns the direction to build and deals catalog challengers” (skill/reference/new-work.md:48).

The catalog of directions it deals from is not in the repository. It is served from /api/roll on impeccable.style. The request carries a scope, a mode, an eight-hex seed key and a re-roll counter, and a separate choice ping honours DO_NOT_TRACK (CLAUDE.md:95). The repo’s own guidance is blunt about why: “Never add catalog data files back to this repo; the catalog is the paid-service moat” (CLAUDE.md:98). The research page puts the campaign at “about $2,600 in evaluations”, reports a catalog of 375 reviewed entries (188 approved), and says the final workflow “won 100% of decisive pairs” against the strongest competing skill. A separate model judged those pairs, and I could not reproduce any of it, since the evaluation set is private too. Without network access, the roll degrades to the model’s own list with the assignment still applied.

What happened when I ran it on this page

The site owner asked for this article to be built with Impeccable itself: first its widgets, then the whole page. So I treated the cloned skill files as my instructions, ran its passes in the order the references hand off (critique and audit first, then distill, clarify and polish), and ran its detector before and after. The detector was the published @impeccable/cli-linux-x64 0.1.11 binary, downloaded from npm with its integrity hash checked and no install scripts run. I rendered the page in my own headless Chromium and pointed the binary’s URL engine at that file, without a model or an API key. The design review in critique needs a model. It asks for an isolated sub-agent, so I gave a separate agent the screenshots and source, kept the detector output away from it, and merged the two reports afterwards, as critique.md:10 requires.

The first draft was what I write by default for this site. It had a bordered panel with a small uppercase “INTERACTIVE · SLOP BENCH” label, a row of three big-number tiles, grey panels inside the panel, and findings as cards with a 3px coloured left border.

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Two rules contradict each other, and this page had to pick. The README says “Don’t use pure black/gray (always tint)” (README.md:89), while colorize.md:44 says “Neutral gray is valid when it serves the world”. The rest of this site is a deliberate pure-neutral ramp and stays one. This page tints its ink and paper a little toward the pink, so the one colour belongs to them. craft-floor.md:15 asks for themed custom scrollbars, while operate.md:50 lists “custom scrollbars” among reinvented affordances to avoid. I themed the selection, focus rings, caret and link underlines, and left scrollbars alone. The sample card keeps literal hex colours in both themes, because the rules read literal colours. Its caption says so.

How this site compares

This site is also built by agents, and it has its own design step. brand-crew/agents/design-reviewer.md is a read-only reviewer. It checks theme tokens, fonts, the shared shell, reduced motion and dark mode, and it is good at keeping pages consistent with each other. It has no opinion on hierarchy, measure or contrast, and that is how 98-character lines and 4.3:1 secondary text had lived in the default styles without anyone noticing. Impeccable’s own repository has the prose half of what our voice.md does. Its docs/STYLE.md is an editorial brief, and its build’s validateProse step fails on a denylist (“load-bearing”, “highest-leverage”, “biggest unlock”), the same idea as this site’s banned-phrase list. The difference is the visual detector. We lint words; they lint pixels too. The closest thing we have written about is json-render with Jev, which avoids bad generated UI by never letting the model emit the component tree at all.

What I would take, and what is thin

The detector is the part I would install. npx impeccable detect http://localhost:3000 runs on a CPU, needs no model, exits 2 when it finds something, and its readability and defect rules are worth having in CI on any front end. The corpus discipline behind it, where rules are measured for harm on real sites and retired when they are harmless, is what I would copy into my own lint rules.

The skill text is thinner than its size suggests. Much of it is excellent operational detail about harnesses, polling and drift. The design guidance itself is a short list of refusals plus four modes, and 60,000 words of instructions is a lot of context to spend for that. The taste rules will age. The registry already names model families, with gpt-thin-border-wide-shadow and codex-grid-background, and the font list has a section headed “Newer monoculture” next to the older one. A list of what generated UI looked like this year is a moving target. To their credit, the authors say so on the slop page: “AI slop changes with the era and the model.”

The half of the vocabulary that matters most, whether the headline says something and whether everything carries the same weight, is still the model’s judgment. The best thing Impeccable does there is procedural. It makes the model judge before it reads the lint report, and it makes a script pick the design direction so the model’s favourite does not always win.

How I checked

I shallow-cloned pbakaus/impeccable (git clone --depth 1). The clone holds one commit, d98b0be, dated 7 October 2026 10:01 -0700, so I could not read the project’s history. The npm registry dates the impeccable package to 30 March 2026, with 39 published versions; the latest is 4.1.0. I read SKILL.src.md, every file in skill/reference/ and skill/agents/, the docs, the build scripts, the launcher, the hooks, the registry and the rule code quoted above, and the DELTAS.md entries cited. Rule counts come from antipatterns.json and registry.rs, severities from the registry, line counts from git ls-files piped through wc. The catalogue split in the ledger is my own classification. I read every page of impeccable.style (home, Designing, Gallery, Docs, Slop, Live, Research) in my own headless Chromium and took the figures from those renders. The 76k star count is the site’s badge; I could not check it against GitHub’s API. The only third-party code I ran was the published detector binary (@impeccable/cli-linux-x64 0.1.11, sha512 checked against the npm registry, no install scripts), under nice, on renders of this article’s own components and of this page. The gallery and research results are the project’s own, judged by its own reviewers, and I could not reproduce them.