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Ds Token Architecture

Community
bradfrost
ds-token-architecture

Check a design token set against the Subatomic three-tier architecture (definitions → usage → components) and hand back evidence-tagged findings plus a prioritized work order, or establish a tier ladder from a production codebase that has no tokens yet. Use when the user pastes their token JSON and asks "is this right?", asks to audit, assess, review, or grade their tokens, asks whether their tokens are properly tiered or semantic, wants to know if dark mode or a second brand will be a remap or a rewrite, or has a codebase full of hardcoded values and wants to build token foundations from it code-first. Companion to ds-inspection (which grades the whole system across ten stations); this one goes deep on the token layer alone.

Overview

Publisherbradfrost
Repositoryskills
Skill nameds-token-architecture
Stars
51
Forks
1
Bundled files
17
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 17 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by bradfrost on GitHub. Read the source before you install it.

Installation

Install the Ds Token Architecture AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/bradfrost/skills/tree/main/skills/design-systems/ds-token-architecture
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/bradfrost/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/design-systems/ds-token-architecture .claude/skills/ds-token-architecture
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ds Token Architecture in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ds Token Architecture on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ds Token Architecture is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Design Token Architecture Check

Nine checks against the three-tier architecture Ian and I teach in Subatomic, run on a real token set with real evidence. The output is a sheet of nine lights and a work order that says what to fix Monday morning. There's no score, because a number out of a hundred hides the one finding that matters: whether a second theme would be a remap or a rewrite.

It also runs the other direction. A team with a product and no tokens can use establish mode to get from "every color in production" to a tier ladder, without letting a scanner make the design decisions.

Ground rules (read first, apply at every check)

  1. Evidence before judgment. Every finding cites its evidence and carries a tag: [verified] (you read the file, the variable, or the tool output yourself) or [reported] (a human told you and you couldn't confirm). Counts, file names, and token names, never vibes. "6 of 41 tier 2 color tokens hold raw hex" beats "some tokens aren't aliased."
  2. Check the structure, not the vocabulary. The architecture's words (definitions, usage, theme, knockout) are one set of words. A system that says primitives, semantic, sys, and on-brand is following the architecture. The intake's vocabulary translation exists so findings speak the team's language. A structural gap is a finding; a different word for the same structure is not.
  3. Read the source, not the build. Built CSS often resolves references to literals, which makes a tiered system look flat. Judge tiers and references from the JSON, the Figma alias structure, or the config.
  4. Do the arithmetic. A token named on-brand is a contrast claim. Resolve the values and compute the ratio, per theme, before you call the pair green. Names are not evidence of what they promise.
  5. Probe the design side every run. Tokens usually exist in Figma too, and a bridge is often connected that you won't notice unless you look. Ask, check your tools, make one real call, record the result. reference/FIGMA-ACCESS.md says what each bridge can see; the native bridge needs a layer selected to read variable definitions, and whole-collection reads need the Console bridge.
  6. Scope claims to what you read. "The 12 components I sampled," never "your components." Say what you did not inspect.
  7. Where the architecture has no opinion, say so. Opacity scales, layout tokens, sizing tokens, structured data-viz palettes: the course doesn't cover them. Describe what the system does in the report's "no guidance" section instead of grading it against a rule that doesn't exist.
  8. The human makes the calls. You surface, sort, and propose. The team decides what to rename, what to merge, and in what order. In establish mode this is non-negotiable: every merge changes pixels, and a script doesn't get to make that call.
  9. No shame. A flat pile of tokens was the right call when someone made it under a deadline. The report says what it costs to carry now.

Files in this kit

  • intake/TOKEN-INTAKE.md — the check-in; produces ds-token-architecture/PROFILE.md
  • checks/check-01…09-*.md — one file per check, each self-contained
  • establish/ESTABLISH.md — the code-first procedure for a system with no tokens yet
  • reference/SUBATOMIC-ARCHITECTURE.md — the architecture the checks test against, with lesson pointers
  • reference/FIGMA-ACCESS.md — what each Figma bridge can actually see
  • templates/report.md, templates/work-order.md, templates/ladder.md — output shells

State (in the user's project)

ds-token-architecture/
├── PROFILE.md                       ← the token set, its sources, the access map
├── reports/YYYY-MM-DD-report.md     ← the nine-light sheet and check records
├── work-orders/YYYY-MM-DD-work-order.md
└── (establish mode only)
    ├── extraction/                  ← raw values with provenance
    ├── consolidation.md             ← proposed merges, team decisions
    └── ladder.md                    ← the proposed tiers

A sibling of any ds-inspection/, product-inspection/, or ds-adoption-plan/ folder. If you can write files, keep this folder. If you can't (plain chat), produce each artifact as its own message the user can save, and don't fold the work order into the report.

Modes

Determine which mode the user wants; when ambiguous, ask one short question.

Mode 1 — Assess (the common case)

  1. Check in. On the first run, open with this, word for word:

    This design token inspection will help you improve (or establish!) your design tokens system based on best practices and architecture covered in Subatomic: The Complete Guide To Design Tokens (https://designtokenscourse.com). Answer a few questions (5 minutes total) to provide context and start the inspection.

    Then run intake/TOKEN-INTAKE.md. If PROFILE.md already exists, skip the opener and confirm the profile is current instead. The intake's job is to test evidence access and write the vocabulary translation; don't skip either.

  2. Announce the plan. One short message: the frame you inferred (solo / small team / platform org), which sources you could read, and which checks will come back N/I because the consumers or the build weren't reachable. Run all nine by default; nobody should have to know the nine checks to answer a question about them.

  3. Run checks 1 → 9 in order. Check 1 establishes whether tiers exist; the rest read its record. For each: open the file, follow its procedure, gather evidence down the fallback chain, assign a light, write its record. One-line status between checks ("Check 4: red. 3 filled surfaces have no content pair; background-brand + #fff is hardcoded in the button. Moving to 5.").

  4. Write the report from templates/report.md into reports/. The short version at the top is for a teammate who reads nothing else.

  5. Write the work order from templates/work-order.md. Every red is a fix-now item with a first move; every yellow is scheduled. Every item cites its check and evidence.

  6. Confirm the outputs exist before you report back: PROFILE.md, the report, and the work order, as three files (or three messages). A work-order-shaped table inside the report is not the work order.

Mode 2 — Single check

Run intake-lite if there's no PROFILE.md (the questions that check needs, always including the design-side probe). Run the check, write its record into today's report (create one holding just this check if none exists). Offer the natural next check without pushing.

Mode 3 — Establish

For a codebase with no token set. Run intake (mode establish), then establish/ESTABLISH.md E1 through E6: extract with provenance, consolidate with the team deciding every merge, mint tier 1 and core, assign tier 2 jobs, mint tier 3 sparingly, wire one pilot component and prove no pixels moved that weren't approved. Establish mode ends by running mode 1 against what was built. The hardcoded-value replacement beyond the pilot is ds-adoption-plan's job; hand it the provenance.

Mode 4 — Re-check

Read the most recent report. Run the requested checks fresh (don't look at the old lights while gathering evidence), then diff: lights changed, token counts moved, work-order items closed or still open. The delta is the result.

The course learner in plain chat

A common caller is someone who took the course, pasted their tokens.json, and asked whether they did it right. They have one file and no repo. That's enough for checks 1 through 7. Say up front that 8 and 9 need the consuming code and the build, run what you can, and make the report specific to their file: their token names, their counts, their theme. Tag everything you couldn't reach. The work order for this caller is usually short and concrete, and that's the point: they get feedback on their own work instead of a lecture.

Related course lessons

When preparing findings or the final deliverable, read Related course lessons. Fetch the public lesson catalog once and connect up to three useful lessons to the user's actual findings or questions. Use the reference's topic pointers, current summaries, and access labels; keep the work complete and useful even when lesson lookup is unavailable.

Voice

Plain, specific, warm. Findings name tokens, files, and counts. The person reading the work order should finish it knowing which token to open first and what to change in it.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ds Token Architecture AI skill do?

Check a design token set against the Subatomic three-tier architecture (definitions → usage → components) and hand back evidence-tagged findings plus a prioritized work order, or establish a tier ladder from a production codebase that has no tokens yet. Use when the user pastes their token JSON and asks "is this right?", asks to audit, assess, review, or grade their tokens, asks whether their tokens are properly tiered or semantic, wants to know if dark mode or a second brand will be a remap or a rewrite, or has a codebase full of hardcoded values and wants to build token foundations from i...

Why use Ds Token Architecture on TypingMind?

Because you install it once and use it with any model. Ds Token Architecture is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ds Token Architecture in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bradfrost/skills/tree/main/skills/design-systems/ds-token-architecture. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ds Token Architecture?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ds Token Architecture?

As many as you like. As long as a model supports skills, you can use Ds Token Architecture with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ds Token Architecture AI skill free?

Yes. It is published on GitHub by bradfrost under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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