ai-assist-design-creator

Tinjau · 51
Diindeks di Registry

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
Star88
Versi1.0.0
Kualitas61/100 · Menjanjikan
Kepercayaan51/100 · Do not auto-install
Audit69/100 · Perlu ditinjau

Profil aset

Riset dan pekerjaan pengetahuan

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

Lihat kategori

Skenario

RAG and knowledge

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

Kecocokan Agent

Claude Code + CLI + Codex

Cocok untuk Codex, Claude Code, Cursor, CLI, atau Agent khusus.

Pasang

Siap

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

Pemeliharaan

Terkini

1 hari sejak push

Risiko

Perlu ditinjau

Lisensi tidak jelas

Kualitas GitHub

88

61/100 Kualitas · 59/100 Kepercayaan

Tag cakupan

RisetRAG and knowledgeagent-skill

Catatan ulasan

Lisensi tidak jelas · Dependency or permission surface needs review

Kartu adopsi Agent

Kepercayaan, audit, dan kesiapan pemasangan dalam sekali lihat

Skor ini menggabungkan metadata repositori publik, sinyal ulasan OpenAgentSkill, kebaruan pemeliharaan, dan kesiapan pemasangan. Ini adalah sinyal shortlist, bukan pengganti peninjauan manusia.

Kualitas

Menjanjikan
61

Useful candidate, but compare it with alternatives before adopting.

Kepercayaan

Do not auto-install
51

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

Audit

Perlu ditinjau
69

Tinjauan yang dapat dibaca mesin tentang kesiapan pemasangan, metadata keamanan, pemeliharaan, dan risiko adopsi.

Trust Score OpenAgentSkill v5

Hanya sandbox

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

CodexClaude CodeCursorOpenAgentSkill CLI

Star

88 star GitHub

Aktivitas repositori

88 star dan 12 fork

Pemeliharaan

1 hari sejak push

Lisensi

Tidak diketahui

Pasang

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

Keamanan pemasangan

Jalur pemasangan paket atau runtime standar

Cakupan izin

secrets or environment access, shell or command execution

Hasil Agent

Belum ada data hasil Agent

Dokumentasi

Konteks README/SKILL.md kuat

Ringkasan risiko

Tinjau sebelum produksi

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

Kesiapan pemasangan

Jalur pemasangan tersedia

  • Jalur pemasangan tersedia
  • Bukti repositori tersedia
  • Lisensi tidak jelas
  • Belum ada bukti hasil Agent-Proven

Metadata yang dapat dibaca Agent

Data keputusan yang dapat dibaca mesin untuk skill ini.

Gunakan blok ini atau JSON tersemat untuk memutuskan apakah Agent perlu memasang skill ini, memilih alternatif, atau meminta tinjauan manusia terlebih dahulu.

Buka JSON

Tugas yang sesuai

  • alur kerja RAG and knowledge
  • Tim Claude Code
  • builders willing to evaluate younger projects
  • Chunk documents

Agent yang sesuai

CodexClaude CodeCursorOpenAgentSkill CLICLI

Keputusan pemasangan

Perintah
npx skills add jparkerweb/ai-assist-skills --skill ai-assist-design-creator
Kebijakan
Blokir
Tinjauan manusia
Ya

Kepercayaan dan risiko

Kepercayaan
51/100
Audit
69/100
Tingkat risiko
Perlu ditinjau

Lingkar hasil

Endpoint
/api/agent/outcome
ID event
resolve
Hasil
5

Perintah pemasangan

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

Jangan gunakan ketika

  • Tim yang membutuhkan SLA dengan dukungan vendor
  • production agents without a repository review
  • Repository license is listed as 'Unknown' by GitHub; consider adding an explicit license to clarify usage rights.
  • Petunjuk izin berisiko tinggi: Shell or command execution, Secrets or environment access
  • Lisensi tidak jelas

Keamanan Agent v2

25/100 · Hindari pemasangan otomatis

Blocked for auto-installBlokir

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.

Selesaikan via API

Tinggi

Eksekusi shell atau perintah

Metadata skill merujuk terminal, CLI, shell, subprocess, atau alur kerja eksekusi perintah.

Sedang

Akses jaringan

Skill kemungkinan mengambil halaman jarak jauh, API, repositori, atau layanan eksternal.

Sedang

Akses sistem file

Skill dapat membaca atau menulis file proyek, dokumen, artefak yang dihasilkan, atau status workspace lokal.

Tinggi

Secrets or environment access

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

  • Petunjuk izin berisiko tinggi: Shell or command execution, Secrets or environment access
  • Lisensi tidak jelas

Target pemasangan

Pasang skill ini di alur Agent Anda

Gunakan endpoint publik untuk mengambil perintah, checklist keamanan, prompt target, dan tautan kanonis.

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

Rencana resolusi Agent

Biarkan Agent memverifikasi kecocokan sebelum memasang.

API Resolve mengembalikan skill utama, alternatif, kebijakan keamanan, catatan audit, target pemasangan, dan prompt siap pakai.

Buka rencana teks

Agent harus memeriksa

  • 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.

Salin 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.

Serah-terima Agent

Berikan jalur pemasangan kepada Agent, bukan direktori lain.

Gunakan endpoint publik untuk mengambil perintah, checklist keamanan, prompt target, dan tautan kanonis.

Buka API pemasangan

Prompt Agent

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

Metadata Registry

Profil yang dapat dibaca Agent untuk pemilihan skill otomatis.

API Registry menyediakan sinyal keputusan, kepercayaan, audit, use case, dan pemasangan tanpa mengikis UI.

Buka Manifest

Kecocokan Agent

61/100

RAG and knowledge

Platform

Claude Code

Laporan audit

Perlu ditinjau · 69/100

Tinjauan yang dapat dibaca mesin tentang kesiapan pemasangan, metadata keamanan, pemeliharaan, dan risiko adopsi.

Lihat laporan auditLihat laporan evaluasi

Panel keputusan Agent

Fallback candidate for RAG and knowledge

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

61
Kesiapan
Prototipe
Tahap

Peran di stack

Kandidat cadangan

Kecocokan utama

RAG and knowledge

Label kepercayaan

Buat prototipe dulu

Jalur pemasangan

Perintah siap

Gunakan saat

  • alur kerja RAG and knowledge
  • Tim Claude Code
  • builders willing to evaluate younger projects

Bukti

  • recent repository activity
  • install command or GitHub repo available
  • profil kualitas 61/100
  • 3 event interaksi OpenAgentSkill

tinjau dulu

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

Jalur implementasi

  1. 1Pasang di Agent sandbox dan jalankan satu tugas RAG and knowledge dari awal hingga akhir.
  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.

Profil kepercayaan

Do not auto-install

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

51
Trust Score OpenAgentSkill

Adopsi GitHub

Periksa

88 star GitHub

Aktivitas star/fork

Periksa

88 star dan 12 fork; aktivitas issue tidak tersedia dalam metadata saat ini

Pemeliharaan terbaru

Lulus

1 hari sejak push

Kejelasan lisensi

Periksa

Tidak diketahui

Sinyal positif

  • Tinjauan AI disetujui
  • Jalur pemasangan tersedia
  • Bukti repositori tersedia
  • Repositori yang baru dipelihara
  • Perintah pemasangan tidak memiliki pola berisiko tinggi yang jelas
  • Loop hasil siap tetapi membutuhkan eksekusi Agent nyata pertama

Tinjau sebelum memasang

  • Repository license is listed as 'Unknown' by GitHub; consider adding an explicit license to clarify usage rights.
  • Lisensi tidak jelas
  • 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
  • Belum ada laporan hasil Agent nyata
  • Tinjauan manusia diperlukan sebelum pemasangan tanpa pengawasan

Tindakan yang disarankan

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

Profil kualitas

Menjanjikan kandidat untuk alur kerja Agent

Useful candidate, but compare it with alternatives before adopting.

61
Star GitHub
88
Keterkinian
1 hari lalu
Siap dipasang
Ya
Lisensi
Tidak diketahui
Tinjau sebelum memasang: Repository license is listed as 'Unknown' by GitHub; consider adding an explicit license to clarify usage rights.

Kecocokan alur kerja

Gunakan skill ini pada skenario berikut

Kecocokan alur kerja

Tambahkan ke alur kerja lengkap

Daftar alternatif

Bandingkan sebelum memasang

Similar skills that may fit this task.

Bandingkan semua

Ringkasan

--- 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"

Detail teknis

Versi
1.0.0
Lisensi
Unknown
Pembaruan terakhir
23 Agu 2026
Diterbitkan
21 Agu 2026

Ringkasan keputusan

Kandidat cadangan

61
Siap
Prototipe
Tahap

recent repository activity

Audit

Tinjauan pemasangan

Tinjauan pemasangan dan adopsi

69
Perlu ditinjau
Keamanan
62/100
Pemeliharaan
100/100
Pasang
92/100
Buka audit lengkapLihat laporan evaluasi

Bukti tervalidasi Agent

Bukti tervalidasi Agent

Laporan hasil setelah resolve, tinjau, pasang, dan satu eksekusi terbatas.

0
Terbukti
Needs first agent runPasang otomatis: tinjau duluTerakhir: Tidak diketahui
Tingkat sukses
Kegagalan terbaru
Hasil
0
Kualitas output
Gagal
0
Tidak relevan
0
Pemasangan
0
Diblokir risiko
0
Perlu penyiapan
0
Produksi
0

Belum ada data hasil Agent. Eksekusi pertama dapat melaporkan keberhasilan, kebutuhan setup, blok risiko, kegagalan, atau tidak relevan melalui /api/agent/outcome.

Pasang

Tambahkan ke alur Agent

Gratis dan sumber terbuka. Tinjau laporan sebelum memasang pada Agent produksi.

Siklus pertumbuhan

Kit berbagi

X

Draf berbasis skenario untuk ai-assist-design-creator, siap untuk posting manual di X.

Catatan kurator
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
Buka draf X
Balasan opsional dengan perintah pemasangan
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
Buka draf balasan

Sumber listing

Diindeks Registry

Dapat diklaim

Listing ini diindeks dari sumber publik dan belum ditandai resmi hingga klaim pemelihara disetujui.

Kreator
jparkerweb
Diindeks oleh
Indeks komunitas OpenAgentSkill

Atribusi menautkan ke repositori publik atau profil kreator. Kreator dapat mengklaim listing untuk memperbarui sinyal kepemilikan.

Klaim skill ini

Klaim pemilik

Klaim listing skill ini

Listing Diindeks Registry ini dikaitkan dengan jparkerweb, tetapi belum ditandai resmi. Klaim untuk menambahkan sinyal pemilik terverifikasi dan membuat pembaruan peluncuran, pemasangan, serta audit berikutnya lebih tepercaya.

Kit backlink kreator

Tambahkan badge bukti ke README Anda

Tampilkan listing kanonis, sinyal kepercayaan dan audit saat ini, serta bukti Agent-Proven nyata di tempat pengembang mengevaluasi repositori.

[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/jparkerweb-ai-assist-design-creator?metric=listed&label=Listed)](https://www.openagentskill.com/skills/jparkerweb-ai-assist-design-creator)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/jparkerweb-ai-assist-design-creator?metric=trust&label=Trust)](https://www.openagentskill.com/skills/jparkerweb-ai-assist-design-creator)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/jparkerweb-ai-assist-design-creator?metric=audit&label=Audit)](https://www.openagentskill.com/skills/jparkerweb-ai-assist-design-creator/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/jparkerweb-ai-assist-design-creator?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/jparkerweb-ai-assist-design-creator)

Penulis

J

jparkerweb

@jparkerweb

Kecocokan platform

Sinyal kesehatan

Star GitHub
88
Skor kualitas
37/100
Push GitHub terakhir
22 Agu 2026
Petunjuk framework
Tidak diketahui
Tampilan OpenAgentSkill
3
Salinan pemasangan
0
Klik keluar
0

Sinyal komunitas

Bagikan apakah skill ini bermanfaat untuk alur kerja Agent Anda. Masukan gabungan meningkatkan peringkat dari waktu ke waktu.

Kepercayaan & keamanan

Do not auto-install

51
  • Adopsi GitHub88 star GitHubPeriksa
  • Aktivitas star/fork88 star dan 12 fork; aktivitas issue tidak tersedia dalam metadata saat iniPeriksa
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