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Mstar Design System

Community
btspoony
mstar-design-system

Use when an implementation must be walked into alignment with an existing baseline — a prototype, a design export, an existing product's markup, or a written design contract — and each difference has to be judged as defect, decision, or phantom. Not for designing from nothing, and not for authoring or auditing `DESIGN.md` itself (that is `mstar-design-md`).

Overview

Publisherbtspoony
Repositorymstar-harness
Skill namemstar-design-system
Stars
67
Forks
4
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

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

Installation

Install the Mstar Design System 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/btspoony/mstar-harness/tree/main/skills/mstar-design-system
  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/btspoony/mstar-harness.git /tmp/mstar-harness
mkdir -p .claude/skills
cp -r /tmp/mstar-harness/skills/mstar-design-system .claude/skills/mstar-design-system
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mstar Design System 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 Mstar Design System 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 Mstar Design System 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.

Load Order

Before the first Read of this skill: Read mstar-harness-core (SKILL.md) — lifecycle, authorization, and conflict authority. The design contract, when the baseline is a written contract, is owned by mstar-design-md; this skill assumes an authoritative baseline exists and defers that contract's authority and sync direction to it; this skill does not restate those.

Also ReadWhen
mstar-design-mdany difference touches tokens, completeness levels, light/dark, or the baseline-vs-document authority rule
mstar-coding-behaviorthe round ends in code changes rather than document changes

On conflict, mstar-harness-core wins; on a design-contract question, mstar-design-md wins.

Scope

In scope: an authority already exists and an implementation has to be converged onto it — locating and making the baseline readable, deriving the machine-readable fixture, measuring parity per layer, triaging each difference, closing it in one direction, and reporting what was actually compared.

Out of scopeWhere it belongs
Designing a system where no baseline existsnot this skill — decide the contract first
DESIGN.md sections, token naming, completeness levels, light/dark rulesmstar-design-md
Prototype-vs-document authority and its one-directional syncmstar-design-md § Prototype vs document — read it there; this skill only applies it
The comparators, CI jobs, and generators of a specific repositorythat repository's own checks

Workflow

  1. Baseline — name the authority: its kind, its location, whether it is readable and tracked, and what was redacted before it was committed. No baseline → stop, this skill does not apply.
  2. Machine — derive what can be derived instead of typing it: fixture, snapshot, extraction. Record the regeneration command and its check mode in the same place.
  3. Parity — compare per layer: structure, appearance, behaviour. Every finding carries the count it was measured on; a probe that matched nothing says so instead of reporting zero defects.
  4. Triage — classify every difference as defect, decision, or phantom. A decision is written into the contract record with its reason, never silently into the implementation.
  5. Close — change the implementation, or amend the baseline when triage recorded a decision.
  6. Evidence — return what ## Evidence below describes; a verdict without its counts is not one.

Decision Rules

  1. A baseline that is not in the repository is not a baseline. Tracked, redacted, and read by the comparators. A baseline in a chat transcript, a screenshot, or a gitignored working directory cannot be converged onto — convert it into a tracked file first, or state that the round has no baseline.
  2. A baseline that no CI job reads is also not a baseline. Committing closes one of three links: committed → every comparator points at it and strict mode is the default → a CI job actually calls it. Never declare a check live before all three close; a fixture only a human runs is documentation.
  3. Generated artifacts are never hand-edited. Which artifact wins and which direction the sync runs is mstar-design-md's rule — this skill applies it, it does not restate it.
  4. Do not invent the baseline. No rounding a source value, no adding a component the source does not define, no drawing an asset it does not ship, no inference from an unreadable source. Silence in the baseline is a decision, recorded with its reason.
  5. A check that cannot say what it covered did not pass. Empty probe ≠ zero overflow. Every verdict names the count it compared — including the failure path: "baseline missing" must also state how much was owed, or it reads as a pass at exit 0.

Evidence

A finished round returns three things:

  • The classified list — every difference labelled defect / decision / phantom, with the unit it belongs to; a decision also names where it was recorded.
  • The coverage statement — counts compared per layer, plus what was skipped and why. "Everything matches" without a count is not a coverage statement.
  • The strict-mode run — the comparator invoked with failure fatal, output pasted as run, not summarized.

A screenshot, a rendered page, or a green check with no count is not evidence for any of the three.

References

WhenOpen
choosing a baseline kind, making it machine-readable, closing the three-link chain, redacting before committingreferences/baseline.md
comparing structure / appearance / behaviour, choosing a measurement, the @media vs @container trapreferences/parity.md
classifying a difference, recording a decision, checking a suspected finding against the phantom cataloguereferences/triage.md
the normative design contract, token rules, completeness levels, baseline-vs-document authoritymstar-design-md

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 Mstar Design System AI skill do?

Use when an implementation must be walked into alignment with an existing baseline — a prototype, a design export, an existing product's markup, or a written design contract — and each difference has to be judged as defect, decision, or phantom. Not for designing from nothing, and not for authoring or auditing `DESIGN.md` itself (that is `mstar-design-md`).

Why use Mstar Design System on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/btspoony/mstar-harness/tree/main/skills/mstar-design-system. 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 Mstar Design System?

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 Mstar Design System?

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

Is the Mstar Design System AI skill free?

Yes. It is published on GitHub by btspoony 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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