product-design-tokens
>-
Works with
---
name: product-design-tokens
description: >-
license: MIT
---
# Product Design Tokens
Produce the *token contract* a themed UI is built from: a valid, WCAG-checked **DTCG 2025.10 JSON
file** that stays the single source of truth. This skill **writes no application code** and never
draws assets; it authors and validates the tokens. Output lands under `docs/specs/design/tokens/`.
## Input
$ARGUMENTS
Read the source first: an existing repo (scan for a design system before anything else), a design
spec / screen inventory from `product-design-spec`, a brand brief, or a plain idea. `--with-design-md`
also emits the optional alpha DESIGN.md prose layer.
## Prerequisites & fallback
Sibling skills are invoked via the `Skill` tool where available; **with no `Skill` tool, apply the
named sibling's documented rules inline** (each delegation below states the fallback in-sentence).
Design-system detection uses the `Task` tool with an `Explore`/haiku subagent; **no `Task` tool?**
Run the detection inline, sequentially, with Grep/Glob/Read. The optional `@google/design.md` CLI is
**probed at runtime, never assumed**: the DTCG JSON is emitted whether or not it runs.
## Lean by default
**Default to a single `docs/specs/design/tokens/tokens.dtcg.json`.** Split a layer (semantic,
component) into its own file only when it outgrows the single file. `contrast-report.md` sits
alongside it; DESIGN.md only when asked.
- **Cost stop-condition:** if the request implies a large tree (multi-brand × multi-theme ×
per-component tokens for dozens of components), **stop and ask** before emitting it: scope it
down or confirm the tree first. Never auto-generate a 30-file token set.
## Reuse first (top of the ladder)
Inventing a brand from scratch is the **last resort, greenfield-only**. Before writing any token:
1. **Detect an existing design system**: scan for `tailwind.config.*`, shadcn (`components.json`,
`@/components/ui`), MUI theme, CSS custom properties, or a native platform system.
2. **Adopt / extend it**: express the existing values as DTCG tokens (alias into them; add only
what the change needs). Do not replace a working system with a new invented palette.
3. Only with genuinely no system present do you seed a new core palette.
State which path you took in one line.
## Workflow
Four phases: **Detect → Draft → Validate → Hand off.**
### Phase: Detect
Scan the repo for an existing design system (spawn an `Explore`/haiku agent, or do it inline):
```
Task (Explore, haiku): "Find this repo's design system: tailwind.config.*, components.json / shadcn
ui dir, MUI theme, CSS custom properties (:root { --... }), or a native platform system. Return the
tool, the token values found (colour/spacing/radius/type), and the file paths, invent nothing."
```
Decide **reuse vs. greenfield** from what you find. If a spec from `product-design-spec` is present,
read its screen inventory so the semantic tokens cover what the screens actually render.
### Phase: Draft
Create the single canonical file from the skeleton and fill it:
```bash
cp skills/product-design-tokens/assets/templates/tokens.dtcg.json docs/specs/design/tokens/tokens.dtcg.json
```
- **DTCG 2025.10 shape** (full detail: `references/standards.md`): a token is any object with a
`$value`; groups carry an inheritable `$type`; aliases are `{group.token.path}`. Pin to
**https://www.designtokens.org/tr/2025.10/**: **never `/tr/drafts/`**.
- **Layer the colours:** a primitive `base` palette, then a `semantic` layer that aliases it
(UI consumes semantic). OKLCH is fine for *exploration*, but export an sRGB/hex-compatible value.
- **Status colours are never colour-alone** (WCAG 1.4.1): annotate each to pair with an icon/label.
- **Brand art that informs the palette** goes to `codex-imagegen` (produce the prompt; don't draw
the asset here: via the `Skill` tool where available, else write the prompt for the user to run).
- **DESIGN.md is optional and derived.** Only with `--with-design-md`: probe the CLI at runtime,
pin the alpha version in its frontmatter, add the ALPHA banner, and **always** keep
`tokens.dtcg.json` alongside as canonical (template: `assets/templates/DESIGN.md`).
- **Platform tokens:** motion → **Material 3 Expressive** spring (stiffness/damping), not fixed
duration+easing; translucency → **Apple Liquid Glass** with a mandatory contrast check. See
`references/standards.md`.
### Phase: Validate
```bash
python skills/product-design-tokens/scripts/dtcg_validate.py --tokens docs/specs/design/tokens/tokens.dtcg.json
python skills/product-design-tokens/scripts/contrast.py --tokens docs/specs/design/tokens/tokens.dtcg.json
```
- `dtcg_validate.py` must print **VALID** (every token has a resolvable `$type`, every alias
resolves, no cycles). Fix any error before proceeding.
- `contrast.py` writes the contrast table: **0 failing pairs is the gate.** A pair below its AA
threshold is a **blocker**: darken/lighten a token and re-run; never approve a failing or a
colour-alone palette. Save its output to `docs/specs/design/tokens/contrast-report.md`.
- **In-context accessibility sign-off** (the palette rendered in real screens) goes to
`review-design` for the WCAG 2.2 AA audit (via the `Skill` tool where available, otherwise apply
its WCAG 2.2 AA checklist, the 9 criteria new since 2.1 in `references/standards.md`, inline).
### Phase: Hand off
- State the single readiness verdict: DTCG **VALID**, contrast **0 failures**, reuse-vs-greenfield
path, and whether a DESIGN.md/alpha layer was produced.
- Report the written path(s). Recommend the **live pipeline** (Figma Code Connect → Dev Mode MCP →
AI editor → human polish) reconciled against the versioned `tokens.dtcg.json`, not a one-shot
handoff export. Name the downstream consumer: `implement-feature` builds the themed UI from these.
## Anti-hallucination
- **Reuse before invent:** scan for an existing design system before proposing any new brand token.
- Pin DTCG to **`/tr/2025.10/`**, never `/tr/drafts/`; the DTCG JSON is **always** the canonical
source: DESIGN.md is never the sole record.
- Never invent colour-psychology claims or approve a palette that fails contrast or carries meaning
by colour alone. Run `contrast.py` before declaring done.
- Target **WCAG 2.2 AA** explicitly; do **not** introduce WCAG 3.0 / APCA.
- The `@google/design.md` CLI and its subcommands are **probed at runtime**, never assumed; pin the
version you generated against.
## References
- `references/standards.md`: DTCG 2025.10 (stable URL), WCAG 2.2 AA + the 9 new-since-2.1 criteria
(3.0/APCA excluded), Material 3 Expressive, Apple Liquid Glass, DSDS (emerging/unendorsed), the
live handoff pipeline.
- `assets/templates/`: `tokens.dtcg.json` (canonical skeleton), `DESIGN.md` (optional alpha),
`contrast-report.md`.
- `scripts/dtcg_validate.py` · `scripts/contrast.py`
## Boundaries
- Screen structure, flows, and states → `product-design-spec`.
- The requirements themselves → `product-prd`.
- Auditing an existing/rendered design's UX or accessibility → `review-design`.
- Rendering the actual logo / hero / mascot art → `codex-imagegen` (or `nanobanana`).
- Building the themed UI → `implement-feature`.
- This skill only authors and validates the token contract. Writes no application code.More Design Systems skills
stitch-design-taste
leonxlnx/taste-skill
Semantic Design System Skill for Google Stitch. Generates agent-friendly DESIGN.md files that enforce premium, anti-generic UI standards — strict typography, calibrated color, asymmetric layouts, perpetual micro-motion, and hardware-accelerated performance.
figma
heygen-com/hyperframes
Import Figma content into a HyperFrames composition — rendered assets, brand tokens, components, storyboard sections → reconstructed motion (frames read as states, not slides) (REST/CLI), connector-assisted motion when available, and shaders from a connector or native export. Use when the user pastes a figma.com link or asks to bring a Figma design, frame, logo, brand, or animation into a video/composition.
image
coreyhaines31/marketingskills
When the user wants to create, generate, edit, or optimize images for marketing — blog heroes, social graphics, product mockups, profile banners, listing visuals, or brand assets. Also use when the user mentions 'AI image generation,' 'generate an image,' 'create a graphic,' 'product mockup,' 'hero image,' 'social media graphic,' 'banner image,' 'cover photo,' 'profile banner,' 'listing screenshot,' 'Flux,' 'Flux Kontext,' 'Midjourney,' 'DALL-E,' 'GPT Image,' 'ChatGPT Images,' 'Ideogram,' 'Gemini image,' 'Nano Banana,' 'Recraft,' 'Stable Diffusion,' 'Canva,' 'Figma,' 'image optimization,' 'compress images,' 'WebP,' or 'OG image.' Use this for general-purpose marketing image creation and optimization. For paid ad image creative and platform-specific ad specs, see ad-creative. For video production, see video.

