ai-assist-design-creator

REVIEW · 51
Registry indexed

Reverse-engineer a website's visual design system from a URL and produce a fully spec-compliant DESIGN.md file (https://github.com/google-labs-code/design.md). The output includes both machine-readable YAML design tokens (colors, typography, spacing, rounded corners, components)

Verified installs0
Stars88
Version1.0.0
Quality61/100 · Promising
Trust51/100 · Do not auto-install
Audit69/100 · Needs review

Supply asset profile

Research and knowledge work

Deep research, source comparison, literature review, RAG, knowledge search, and reports.

Browse track

Scenario

RAG and knowledge

I need my agent to build a RAG workflow over documents and retrieve reliable context.

Agent fit

Claude Code + CLI + Codex

Codex, Claude Code, Cursor, CLI, or custom agents.

Install

Ready

npx skills add jparkerweb/ai-assist-skills --skill ai-assist-design-creator

Maintenance

fresh

Pushed today

Risk

Needs review

License is unclear

GitHub quality

88

61/100 Quality · 59/100 Trust

Coverage tags

ResearchRAG and knowledgeagent-skill

Review notes

License is unclear · Dependency or permission surface needs review

Agent adoption scorecard

Trust, audit, and install readiness at a glance

These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.

Quality

Promising
61

Useful candidate, but compare it with alternatives before adopting.

Trust

Do not auto-install
51

Trust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.

Audit

Needs review
69

A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.

OpenAgentSkill Trust Score v5

Sandbox only

Choose a stronger alternative or inspect the source manually before any install attempt.

CodexClaude CodeCursorOpenAgentSkill CLI

Stars

88 GitHub stars

Repo activity

88 stars, 12 forks

Maintenance

Pushed today

License

Unknown

Install

npx skills add jparkerweb/ai-assist-skills --skill ai-assist-design-creator

Install safety

standard package or runtime install path

Permission surface

secrets or environment access, shell or command execution

Agent outcomes

No agent outcome data yet

Docs

Strong README/SKILL.md context

Risk summary

Review before production

  • Repository license is listed as 'Unknown' by GitHub; consider adding an explicit license to clarify usage rights.
  • License is unclear
  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution

Install readiness

Install path available

  • Install path is available
  • Repository evidence is available
  • License is unclear
  • No Agent Proven outcome evidence yet

Agent-readable metadata

Machine-readable decision data for this skill.

Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.

Open JSON

Suited tasks

  • RAG and knowledge workflows
  • Claude Code teams
  • builders willing to evaluate younger projects
  • Chunk documents

Suited agents

CodexClaude CodeCursorOpenAgentSkill CLICLI

Install decision

Command
npx skills add jparkerweb/ai-assist-skills --skill ai-assist-design-creator
Policy
block
Human review
yes

Trust and risk

Trust
51/100
Audit
69/100
Risk level
Needs review

Outcome loop

Endpoint
/api/agent/outcome
Event ID
resolve
Outcomes
5

Install command

npx skills add jparkerweb/ai-assist-skills --skill ai-assist-design-creator

Do not use when

  • teams that need a vendor-supported SLA
  • production agents without a repository review
  • Repository license is listed as 'Unknown' by GitHub; consider adding an explicit license to clarify usage rights.
  • High-risk permission hints: Shell or command execution, Secrets or environment access
  • License is unclear

Agent safety v2

25/100 · Avoid automatic install

Blocked for auto-installblock

This skill should not be selected by an agent without explicit human security review.

Do not auto-install. Inspect the source, dependencies, and permission surface first.

Resolve via API

high

Shell or command execution

Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.

medium

Network access

Skill likely fetches remote pages, APIs, repositories, or external services.

medium

Filesystem access

Skill may read or write project files, documents, generated artifacts, or local workspace state.

high

Secrets or environment access

Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.

  • High-risk permission hints: Shell or command execution, Secrets or environment access
  • License is unclear

Install targets

Install this skill in your agent workflow

Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.

skill install

OpenAgentSkill CLI

Resolve policy, run the source installer safely, and report a verified install receipt.

$ npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.2.1/openagentskill-0.2.1.tgz install jparkerweb-ai-assist-design-creator

Agent resolve plan

Let an agent verify fit before installing.

The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.

Open text plan

Agent should check

  • Task fit and alternatives from Resolve API.
  • Audit score, trust score, and safety policy warnings.
  • Install target compatibility for Codex, Claude Code, Cursor, or CLI.

Copy prompt

Task: Use ai-assist-design-creator in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20ai-assist-design-creator%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/jparkerweb-ai-assist-design-creator/install
Install command: npx skills add jparkerweb/ai-assist-skills --skill ai-assist-design-creator
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.

Agent handoff

Give an agent the install path, not another directory page.

Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.

Open install API

Agent prompt

Use ai-assist-design-creator for this task. Review https://www.openagentskill.com/api/skills/jparkerweb-ai-assist-design-creator/install, then install with: npx skills add jparkerweb/ai-assist-skills --skill ai-assist-design-creator

Registry metadata

Agent-readable profile for automatic skill selection.

This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open manifest

Agent fit

61/100

RAG and knowledge

Platforms

Claude Code

Audit report

Needs review · 69/100

A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.

View audit reportView eval report

Agent decision cockpit

Fallback candidate for RAG and knowledge

Prototype with this skill first; keep a fallback candidate ready.

61
Readiness
Prototype
Stage

Role in stack

Fallback candidate

Primary fit

RAG and knowledge

Trust label

Prototype first

Install path

Command ready

Use when

  • RAG and knowledge workflows
  • Claude Code teams
  • builders willing to evaluate younger projects

Evidence

  • recent repository activity
  • install command or GitHub repo available
  • 61/100 quality profile
  • 3 OpenAgentSkill engagement events

review first

  • Repository license is listed as 'Unknown' by GitHub; consider adding an explicit license to clarify usage rights.

Implementation path

  1. 1Install it in a sandbox agent and run one RAG and knowledge task end to end.
  2. 2Compare output quality, latency, and failure behavior against at least one alternative.
  3. 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.

Trust profile

Do not auto-install

Trust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.

51
OpenAgentSkill Trust Score

GitHub adoption

CHECK

88 GitHub stars

Stars/forks activity

CHECK

88 stars, 12 forks; issue activity unavailable in current metadata

Recent maintenance

PASS

Pushed today

License clarity

CHECK

Unknown

Good signals

  • AI review approved
  • Install path is available
  • Repository evidence is available
  • Recently maintained repository
  • Install command has no obvious high-risk pattern
  • Outcome loop is ready but needs first real agent run

Review before install

  • Repository license is listed as 'Unknown' by GitHub; consider adding an explicit license to clarify usage rights.
  • License is unclear
  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution
  • GitHub adoption: 88 GitHub stars
  • Stars/forks activity: 88 stars, 12 forks; issue activity unavailable in current metadata
  • License clarity: Unknown
  • Dependency/runtime risk: command execution surface, credential or environment access
  • Permission surface: secrets or environment access, shell or command execution
  • No real agent outcome reports yet
  • Human review required before unattended installation

Recommended action

Choose a stronger alternative or inspect the source manually before any install attempt.

Quality profile

Promising candidate for agent workflows

Useful candidate, but compare it with alternatives before adopting.

61
GitHub stars
88
Freshness
Today
Install ready
Yes
License
Unknown
Review before install: Repository license is listed as 'Unknown' by GitHub; consider adding an explicit license to clarify usage rights.

Workflow fit

Use this skill in these scenarios

Workflow fit

Add it to a complete workflow

Alternative shortlist

Compare before you install

Similar skills that may fit this task.

Compare all

Overview

--- name: ai-assist-design-creator description: "Reverse-engineer a website's visual design system from a URL and produce a fully spec-compliant DESIGN.md file (https://github.com/google-labs-code/design.md). The output includes both machine-readable YAML design tokens (colors, typography, spacing, rounded corners, components) and human-readable markdown rationale sections (Overview, Colors, Typography, Layout, Elevation & Depth, Shapes, Components, Do's and Don'ts). Use this skill whenever the user wants to generate a DESIGN.md, create a design system file from a website, capture a site's visual identity, extract design tokens, build a design spec from a URL, clone a site's look and feel, or scaffold a DESIGN.md from scratch. Also triggers on: 'design system from URL', 'generate DESIGN.md', 'extract colors from site', 'what are this site's design tokens', 'capture design from website'." argument-hint: "[URL of the site to reverse-engineer, or leave blank to be prompted]" ---

# DESIGN.md Creator

Reverse-engineer a website's visual design system and produce a fully spec-compliant `DESIGN.md` file following the [google-labs-code/design.md](https://github.com/google-labs-code/design.md) format.

## What this produces

A `DESIGN.md` file with two layers: 1. **YAML frontmatter** — machine-readable design tokens: colors, typography, spacing, rounded corners, components 2. **Markdown body** — human-readable rationale for each design decision, in 8 canonical sections

The output is ready for agents to consume immediately — no post-processing needed.

## Step 1: Get the URL

If the user provided a URL via `$ARGUMENTS`, use it. Otherwise ask:

> What website should I reverse-engineer? Provide the URL and I'll generate a DESIGN.md from its visual design. > > Optionally, also tell me: > - Where to save the file (default: `DESIGN.md` in the current directory) > - Whether this is a dark-mode or light-mode site (I'll detect this automatically if you don't know) > - Any specific components you want captured (buttons, cards, inputs, nav, etc.)

Wait for the URL before proceeding.

## Step 2: Fetch and analyze the site

Fetch the page and all significant visual signals:

1. **Fetch the main URL** — use whichever method your agent environment supports: - **`curl`** (works in any agent with shell access): `curl -sL --max-time 15 -A "Mozilla/5.0" "<URL>"` — captures raw HTML including `<style>` blocks and inline CSS - **`webfetch` tool** (if your agent provides it natively): use it directly for cleaner content extraction - If the initial fetch returns no CSS (JS-heavy SPA), also fetch the page's linked `.css` files: extract `<link rel="stylesheet" href="...">` URLs from the HTML, resolve each href to an absolute URL using the page's final URL after redirects (e.g., `/assets/app.css` → `https://example.com/assets/app.css`, `//cdn.example.com/app.css` → `https://cdn.example.com/app.css`), de-duplicate, then `curl` each one 2. **Identify key sub-pages** — if the site has a component library, style guide, or "About" page, fetch those too (up to 2–3 additional pages) to improve coverage 3. **Look for existing design system artifacts** — check for `/design-tokens.json`, `/tokens.json`, `tailwind.config.js`, or any design system links in the page source

What to extract from the fetched content:

| Signal | Where to look | |--------|--------------| | Brand colors | CSS variables (`--color-*`, `--primary`, etc.), inline styles, og:image colors, logo | | Typography | `font-family`, `font-size`, `font-weight`, `line-height`, `letter-spacing` in CSS | | Spacing scale | `--spacing-*`, padding/margin patterns, grid gutter values | | Corner radii | `border-radius` values across buttons, cards, inputs | | Elevation | `box-shadow`, `backdrop-filter`, `z-index` layering patterns | | Component styles | Button, card, input, nav, badge styles from class names or CSS | | Design personality | Logo, imagery, copy tone, overall layout density |

> **Note:** You're inferring from observed CSS/HTML. Be honest about what you can directly observe vs. what you're inferring from visual patterns. Dark-mode sites typically have low-luminance surface colors and high-contrast text; light-mode sites are the inverse. When you can't determine an exact hex value, make a design-coherent choice and note it in the prose.

## Step 3: Build the DESIGN.md

Read `references/design-md-spec.md` for the complete token schema and section rules.

### Token extraction rules

**Colors** — Extract the site's full color role set. At minimum: - `primary` — main brand/action color - `secondary` — supporting accent or secondary brand color - `neutral` / `surface` — background/surface color - `on-primary`, `on-surface` — text colors on those surfaces - Include semantic colors if detectable: `error`, `warning`, `success` - Name tokens semantically (`primary`, `secondary`, `tertiary`, `neutral`) or use Material Design role names if the site uses a Material-style palette

**Typography** — Identify the main type scale. Typically 5–12 levels: - Display/headline levels (large, impactful headings) - Body levels (body-lg, body-md, body-sm) - Label levels (captions, tags, small UI text) - Include all detectable properties: `fontFamily`, `fontSize`, `fontWeight`, `lineHeight`, `letterSpacing` - Dimensions must include units: `px`, `em`, or `rem`

**Spacing** — Extract the spacing scale. Common pattern: a base unit (4px or 8px) with named steps: `xs`, `sm`, `md`, `lg`, `xl`. Also include layout-specific values like `gutter`, `margin`, `container-max`.

**Rounded** — Extract corner radius values. Name them: `sm`, `DEFAULT`, `md`, `lg`, `xl`, `full` (for pill shapes).

**Components** — Capture 4–8 key components. For each, include as many valid properties as observed: `backgroundColor`, `textColor`, `typography` (token ref), `rounded` (token ref), `padding`, `height`, `width`. Use token references like `{colors.primary}` instead of hardcoded hex values wherever possible. Include hover variants as separate entries (e.g., `button-primary-hover`).

### Sections to write

Write all 8 sections in canonical order. Each section combines YAML tokens (defined in frontmatter) with prose rationale. For sections where tokens aren't applicable (Elevation, Shapes, Do's and Don'ts), write prose only.

1. **Overview** — Brand personality, target audience, emotional tone, design style (flat, glassmorphism, neumorphism, material, etc.), key design decisions. 2–4 sentences that give a coherent aesthetic picture.

2. **Colors** — Describe the role of each color palette entry. What does each color *mean* in the design? When is it used? Reference the token names.

3. **Typography** — Describe the font strategy: which typefaces, why they were chosen, how the scale is organized, any special treatments (tight tracking on headlines, text-shadow on dark backgrounds, etc.).

4. **Layout** — Grid system (fluid, fixed, 12-column?), spacing philosophy (8px grid, dense vs. airy), max-width, container strategy.

5. **Elevation & Depth** — How visual hierarchy is communicated: shadows, tonal layers, glassmorphism, borders, z-axis layering. If flat design, describe what replaces shadows.

6. **Shapes** — Corner radius philosophy: sharp/technical, soft/organic, fully rounded pills, mixed. Which components use which radius.

7. **Components** — Walk through the key component tokens and explain the design rationale for each group (action elements, containers, inputs, typography application).

8. **Do's and Don'ts** — 3–5 concrete rules for maintaining design consistency. Things like "always use `{colors.primary}` for CTAs, never `{colors.secondary}`" or "never use pure black (#000000) for text — use `on-surface`".

### YAML frontmatter structure

```yaml --- name: <Site/Brand Name> description: <optional one-line brand tagline> colors: primary: "#XXXXXX" ... typography: headline-lg: fontFamily: <font> fontSize: <Npx> fontWeight: <number> lineHeight: <1.2 or 24px> letterSpacing: <-0.02em or 1px> ... rounded: sm: <Npx or Nrem> ... spacing: base: <Npx> ... components: button-primary: backgroundColor: "{colors.primary}" ... ... --- ```

## Step 4: Validate and save

After generating the content:

1. **Self-check** these things before writing the file: - All token references (`{path.to.token}`) resolve to a defined token - Color values start with `#` followed by 6 hex digits - All dimension values have units (`px`, `em`, `rem`) — no bare numbers except: font weights, unitless line-height multipliers, and `spacing` values (which may be unitless ratios or column counts per the spec) - Section order matches the canonical order (Overview → Colors → Typography → Layout → Elevation & Depth → Shapes → Components → Do's and Don'ts) - Component properties: canonical keys (`backgroundColor`, `textColor`, `typography`, `rounded`, `padding`, `size`, `height`, `width`) pass the linter silently; unknown keys are accepted by the spec but will produce a linter warning — flag them in the confidence notes

2. **Check for an existing file** at the target path before writing: - If `DESIGN.md` (or the user-specified path) already exists, warn the user: "A `DESIGN.md` already exists at this path. Overwrite, save as `DESIGN-<site-name>.md`, or cancel?" Wait for their choice before writing. - If no file exists, proceed directly.

3. **Save the file** as `DESIGN.md` in the current working directory (or the path the user specified).

4. **Tell the user** what was generated:

> `DESIGN.md` saved. > > **Design system:** [Name] > **Style:** [e.g., Glassmorphism / Flat / Material / Custom] > **Colors:** [N tokens] — [brief palette description] > **Typography:** [N levels] — [font family names] > **Components:** [list of captured components] > > **Confidence notes:** > - [Any values that were inferred rather than directly observed] > - [Any sections that had limited CSS data and required design judgment] > > Want me to refine any section, add more components, or lint the file with `npx @google/design.md lint DESIGN.md`?

## Recovery

| Situation | How to handle | |-----------|--------------| | `webfetch` not available | Fall back to `curl -sL --max-time 15 -A "Mozilla/5.0" "<URL>"` — available in any agent with shell access | | Site blocks fetch (403/429) | Ask user to paste relevant CSS, screenshot, or describe the design manually | | JS-heavy SPA with no inline CSS | Fetch the JS bundle URL if visible; also try fetching linked `.css` files directly; ask user for computed styles or a screenshot as a last resort | | Can't determine exact hex values | Make design-coherent color choices; note them as "inferred" in prose and confidence notes | | Site uses a known design system (Material, Ant, Chakra, Tailwind UI) | Note this in the Overview — tokens will align with that system's defaults | | No typography found | Default to system fonts (Inter, -apple-system) and note it | | User wants lint | Run `npx @google/design.md lint DESIGN.md` and surface any errors/warnings |

## Rules

- Never fabricate specific brand hex values with false certainty — if you inferred a color, say so - All token cross-references must point to defined tokens — no dangling refs - Dimensions must always have units (exception: unitless `lineHeight` multipliers like `1.5` are valid) - The YAML frontmatter is normative; prose is explanatory context — don't contradict one with the other - Output goes in the current working directory as `DESIGN.md` unless the user specifies otherwise - If a section has genuinely no applicable content (e.g., a flat design with no elevation), include it briefly and explain: "This design system uses flat tonal layering rather than shadows — see Colors for the tonal surface stack"

Technical details

Version
1.0.0
License
Unknown
Last updated
Aug 22, 2026
Published
Aug 21, 2026

Decision snapshot

Fallback candidate

61
Ready
Prototype
Stage

recent repository activity

Audit

Install review

Install and adoption review

69
Needs review
Security
62/100
Maintenance
100/100
Install
92/100
Open full auditView eval report

Agent-proven evidence

Agent-proven evidence

Outcome reports after resolve, review, install, and one narrow run.

0
Proven
Needs first agent runAuto-install: review firstLast: Unknown
Success rate
Recent failure
Outcomes
0
Output quality
Failed
0
Not relevant
0
Installs
0
Risk blocked
0
Setup needed
0
Production
0

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Growth loop

Share kit

X

Scenario-led draft for ai-assist-design-creator, ready for a manual X post.

Curator note
ai-assist-design-creator: Reverse-engineer a website's visual design system from a URL and produce a fully spec-complia...

88 stars

https://www.openagentskill.com/skills/jparkerweb-ai-assist-design-creator?ref=x
Open X draft
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Listing + install path for ai-assist-design-creator:
https://www.openagentskill.com/skills/jparkerweb-ai-assist-design-creator?ref=x

Install: npx skills add jparkerweb/ai-assist-skills --skill ai-assist-design-creator

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Creator
jparkerweb
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Author

J

jparkerweb

@jparkerweb

Platform fit

Health signals

GitHub stars
88
Quality score
37/100
Last GitHub push
Aug 22, 2026
Framework hints
Unknown
OpenAgentSkill views
3
Install copies
0
Outbound clicks
0

Community signal

Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.

Trust & safety

Do not auto-install

51
  • GitHub adoption88 GitHub starsCHECK
  • Stars/forks activity88 stars, 12 forks; issue activity unavailable in current metadataCHECK
  • Recent maintenancePushed todayPASS
  • License clarityUnknownCHECK
  • README/SKILL.md completenessMetadata includes enough usage and workflow contextPASS
  • Dependency/runtime riskcommand execution surface, credential or environment accessFIX