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Design System Audit

Community
davekilleen
design-system-audit

Use when assessing use of named design-system components or tokens across a defined sample of product artifacts, including adoption and deviation questions.

Overview

Publisherdavekilleen
RepositoryDex
Skill namedesign-system-audit
Stars
481
Forks
130
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Design System Audit 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.
  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/davekilleen/Dex.git /tmp/Dex
mkdir -p .claude/skills
cp -r /tmp/Dex/packages/dex-agent-plugin/skills/_available/design/design-system-audit .claude/skills/design-system-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Design System Audit 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 Design System Audit 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 Design System Audit 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-system audit

When to use

Use this skill when a named design system has a canonical component or token source and the requester can define the artifact sample and as-of boundary. It can assess adoption, coverage, repeated patterns, deviations, and documented exceptions without changing them.

Do not use it to define a new system, refactor product work, approve an exception, or claim organization-wide adoption from an undeclared sample. Not for editing components, tokens, design files, code, or adoption records without a separately confirmed action.

Inputs and source discipline

  • Identify the canonical component and token sources, version, source date, and as-of date/time. Record the rule that makes a component or token canonical; if the source is missing or contradictory, keep adoption unknown.
  • Declare the sample and coverage: population if known, eligibility rule, included and excluded artifacts, artifact/version dates, and inspection method. Do not call a sample representative without evidence for that claim.
  • Define adoption numerator and denominator before counting. Record the adoption source, source date, retrieval/as-of date, and items that are uncountable; never invent a denominator or percentage.
  • Locate exception records and their rationale, authority, version, and date. Treat a local pattern as an observation, not an approved exception, unless the source says so.

Method

  1. Confirm the canonical sources, versions, sample boundary, coverage declaration, timezone if relevant, and as-of date/time.
  2. Inspect the declared sample read-only. Record exact canonical component/token usage by stable artifact and location, and mark unreadable or ambiguous cases unknown.
  3. Apply the declared adoption definition. Reconcile numerator and denominator from eligible artifacts only; report excluded, duplicate, and uncountable items separately.
  4. Compare observed usage with the canonical version. Classify a deviation only when the canonical rule supports that classification and no approved exception covers it.
  5. Classify an intentional exception only with an explicit rationale, authority, source, and date. Otherwise distinguish unsupported deviation, possible equivalence, stale documentation, and unknown rather than guessing intent.
  6. Identify repeated patterns and coverage gaps, state confidence and evidence, and offer recommendations as proposals. Do not approve a component, exception, or roadmap item.
  7. Show a complete report preview before any requested save or downstream action and obtain explicit confirmation from the human authority.

Truth and uncertainty rules

  • Observed: canonical version, component/token usage, artifact membership, exception record, or count directly present in a dated source.
  • Inferred: likely equivalence, adoption barrier, or repeated pattern derived from the sample; state the reasoning and confidence.
  • Unknown: missing canonical rule, unreadable artifact, incomplete sample, denominator, exception rationale, or authority.
  • Stale: usage or exception evidence tied to an older canonical version or outside the requested as-of boundary.
  • Contradictory: canonical sources, artifact records, or exception records disagree; show versions and dates instead of resolving the conflict silently.

Never invent dates, metrics, percentages, owners, intent, money, causes, status, or evidence. A recommendation is not an approval or human decision. Do not report an adoption percentage when the denominator is not reliable.

Output contract

Return an audit with:

  • canonical component/token sources, versions, source dates, as-of date/time, and authority;
  • declared sample, coverage, eligibility rule, exclusions, and inspection limitations;
  • component/token usage findings with artifact/version/source/date trace and confidence;
  • adoption numerator, denominator, calculation rule, uncountable items, or an explicit unknown when calculation is unsupported;
  • deviations, documented intentional exceptions, stale evidence, unknowns, contradictions, and recommendations separated from approvals or implementation decisions.

Safety and write boundaries

Default to read-only. Do not change canonical components, tokens, artifacts, code, exception records, or adoption metrics. Any requested write or downstream action requires a precise preview, explicit confirm, and human authority; execute only that approved scope. A gap recommendation does not authorize refactoring or exception approval.

Verification and recovery

Read back the canonical versions, sample manifest, artifact references, exception records, and counts, then reconcile duplicate handling, numerator, denominator, and coverage before delivery. After an authorized save, read back the destination and reconcile it with the confirmed preview. If a canonical source changes, mark affected findings stale and rerun the impacted sample; if a source or artifact cannot be read, mark the result unknown. If a write fails or partially succeeds, stop, preserve the preview and error, report the exact state, and obtain human authority before retrying or recovering.

Frequently asked questions

What does the Design System Audit AI skill do?

Use when assessing use of named design-system components or tokens across a defined sample of product artifacts, including adoption and deviation questions.

Why use Design System Audit on TypingMind?

Because you install it once and use it with any model. Design System Audit 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 Design System Audit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/davekilleen/Dex/tree/main/packages/dex-agent-plugin/skills/_available/design/design-system-audit. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Design System Audit?

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 Design System Audit?

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

Is the Design System Audit AI skill free?

It is published on GitHub by davekilleen. Check the repository for licensing terms. 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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