Automated ingestion of prompt: Token Architecture
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title: "Token Architecture"
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contributor: "@gokbeyinac"
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tags: #coding, #gokbeyinac
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---
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You are a design systems architect. I'm providing you with a raw design audit JSON from an existing codebase. Your job is to transform this chaos into a structured token architecture.
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## Input
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[Paste the Phase 1 JSON output here, or reference the file]
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## Token Hierarchy
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Design a 3-tier token system:
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### Tier 1 — Primitive Tokens (raw values)
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Named, immutable values. No semantic meaning.
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- Colors: `color-gray-100`, `color-blue-500`
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- Spacing: `space-1` through `space-N`
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- Font sizes: `font-size-xs` through `font-size-4xl`
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- Radii: `radius-sm`, `radius-md`, `radius-lg`
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### Tier 2 — Semantic Tokens (contextual meaning)
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Map primitives to purpose. These change between themes.
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- `color-text-primary` → `color-gray-900`
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- `color-bg-surface` → `color-white`
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- `color-border-default` → `color-gray-200`
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- `spacing-section` → `space-16`
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- `font-heading` → `font-size-2xl` + `font-weight-bold` + `line-height-tight`
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### Tier 3 — Component Tokens (scoped to components)
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- `button-padding-x` → `spacing-4`
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- `button-bg-primary` → `color-brand-500`
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- `card-radius` → `radius-lg`
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- `input-border-color` → `color-border-default`
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## Consolidation Rules
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1. Merge values within 2px of each other (e.g., 14px and 15px → pick one, note which)
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2. Establish a consistent spacing scale (4px base recommended, flag deviations)
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3. Reduce color palette to ≤60 total tokens (flag what to deprecate)
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4. Normalize font size scale to a logical progression
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5. Create named animation presets from one-off values
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## Output Format
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Provide:
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1. **Complete token map** in JSON — all three tiers with references
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2. **Migration table** — current value → new token name → which files use it
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3. **Deprecation list** — values to remove with suggested replacements
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4. **Decision log** — every judgment call you made (why you merged X into Y, etc.)
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For each decision, explain the trade-off. I may disagree with your consolidation
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choices, so transparency matters more than confidence.
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