Memoire Design Tooling

Organization

memi-design

memoire-design-tooling

Set up and operate Mémoire's CLI, MCP server, agent kits, design preflight, UX audits, craft checks, tokens, and registry evidence. Use when a repository explicitly needs Mémoire tooling; use portable design skills when Mémoire is unavailable.

Overview

Publisher	memi-design
Repository	`design-skills`
Skill name	`memoire-design-tooling`
Stars	7
Forks	0
Bundled files	1
License	MIT
Links	Skill source Repository Homepage

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.
1 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by memi-design on GitHub. Read the source before you install it.

Installation

Install the Memoire Design Tooling AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/memi-design/design-skills/tree/main/skills/memoire-design-tooling
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.

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/memi-design/design-skills.git /tmp/design-skills
mkdir -p .claude/skills
cp -r /tmp/design-skills/skills/memoire-design-tooling .claude/skills/memoire-design-tooling

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Memoire Design Tooling 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 Memoire Design Tooling 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 Memoire Design Tooling 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.

memi Design Tooling

Use memi as the local interface-understanding layer before broad frontend changes. The goal is to give the agent evidence about the product UI before it edits code.

Fast Path

bash
npm i -g @memi-design/cli
memi suite init --project .
memi daemon start --project . --port auto
memi daemon status --json
memi agent install --dry-run --json
memi agent brief . --intent "Improve this interface" --json
memi status
memi diagnose .
memi ux audit . --json
memi craft audit . --json
memi tokens --from ./src --report
memi shadcn export --out public/r
memi mcp start --no-figma

Interface Understanding Protocol

Read the local instructions first: AGENTS.md, README files, .memoire/, specs, tokens, and memoire.agent.yaml.
Generate a design-agent preflight with memi agent brief . --intent "<task>" --json; use it as the local evidence and cost-control contract. For a supplied Figma or Paper design, pair the brief with design-to-code-handoff so the source, component/token mapping, Storybook state, browser check, and unresolved differences survive into the implementation receipt.
Collect evidence with memi diagnose ., memi ux audit . --json, memi craft audit . --json, and memi tokens --from ./src --report before making broad UI edits.
If a runtime route matters, run memi diagnose http://localhost:<port> or memi design-doc <url> --spec.
If Figma is connected, use memi/Figma context for token pulls, component inspection, screenshot capture, and sync. Figma is optional; do not block code-first audits on it.
Map components to Atomic Design levels: atom, molecule, organism, template, page.
Prefer shadcn/ui primitives and Tailwind. Avoid introducing CSS modules or styled-components for memi-generated UI.
Use UX Tenets and Traps as the review layer: clarity, feedback, control, consistency, accessibility, error recovery, progressive disclosure, workflow fit, trust, and state continuity.
Use Interface Craft as the polish layer: focusing mechanism, visual hierarchy, spacing rhythm, color intentionality, visual weight, component cohesion, responsive resilience, and user context.
For research-backed product work, run the research flow before implementation:

bash
memi research synthesize
memi simulate plan --hypothesis "Evidence links improve product confidence" --json
memi research design --intent "Design from the accepted research evidence" --json
memi research design --write-specs --mermaid-jam --json
memi mermaid-jam export --from research --json

For registry work, keep the output installable:

bash
memi shadcn doctor
memi shadcn export --out public/r
memi publish --name @you/ds
memi add Button --from @you/ds

End with evidence: commands run, artifacts produced, files changed, remaining assumptions, and the next verification command.

Agent Stack Setup

bash
memi agent install universal --project .
memi agent install hermes
memi agent install openclaw --project .
memi agent install claude-code --project .
memi agent install cursor --project .
memi agent install codex
memi agent install codex-plugin
memi agent install opencode --project .
npx skills add memi-design/memi --skill memoire-design-tooling

Use memi agent install --dry-run --json before writing kit files in shared repositories.

Public Surface and Release Proof

When the task is onboarding, publishing, or release alignment, verify the connected surfaces instead of describing them from memory:

Surface	Verification
npm engine	Confirm the current `@memi-design/cli` package and install command.
MCP	Check the committed server entrypoint and run `memi mcp start --no-figma` as the safe local proof.
Agent Skills	Verify the current `memoire-design-tooling` installation path and run a local discovery smoke.
Studio	Confirm the current macOS Studio repository, its relationship to the CLI, and the receipt or artifact contract.
Sandbox	Run the committed design-sandbox proof commands rather than relying on positioning copy.
Design Skills	Verify the catalog, provenance, selected-skill install, and generated-file checks.

Keep product boundaries explicit: the CLI is the engine, MCP exposes local tools, Agent Skills provide workflow context, Studio supervises runs, and the sandbox is a runnable proof surface. End with exact links, commands run, artifacts produced, and any surface that could not be verified.

Output Artifacts To Cite

.memoire/app-quality/diagnosis.json
.memoire/app-quality/diagnosis.md
.memoire/app-quality/ux-audit.json
.memoire/app-quality/interface-craft.json
.memoire/design-system.json
public/r/registry.json
.memoire/mermaid-jam/<package-id>/
memoire.agent.yaml

Common Mistakes

Starting a UI patch before collecting design evidence.
Treating Figma as mandatory when code-first audit commands are enough.
Creating new component patterns when shadcn/ui or local components already exist.
Omitting Atomic Design levels from new component specs.
Ignoring UX trap findings because the screen looks acceptable in one viewport.
Skipping interface craft checks, then relying on taste for hierarchy, rhythm, polish, or conventions.
Installing agent kits without a dry run in a shared workspace.
Skipping the design-agent brief, then guessing which evidence, agent stack, or cost mode applies.

Bundled files

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

note.json1 KB

Frequently asked questions

What does the Memoire Design Tooling AI skill do?

Why use Memoire Design Tooling on TypingMind?

Because you install it once and use it with any model. Memoire Design Tooling 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 Memoire Design Tooling in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/memi-design/design-skills/tree/main/skills/memoire-design-tooling. 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 Memoire Design Tooling?

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 Memoire Design Tooling?

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

Is the Memoire Design Tooling AI skill free?

Yes. It is published on GitHub by memi-design 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

memi Design Tooling

Fast Path

Interface Understanding Protocol

Agent Stack Setup

Public Surface and Release Proof

Output Artifacts To Cite

Common Mistakes

Bundled files

Frequently asked questions

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