Superpower logo

Superpower

Organization
memi-design
superpower

Orchestrate an explicit Mémoire canvas workflow across design inspection, planning, generation, and verification. Use only when the user requests Mémoire orchestration; do not use as a default session policy.

Overview

Publishermemi-design
Repositorydesign-skills
Skill namesuperpower
Stars
7
Forks
0
Bundled files
1
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.

  • 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 Superpower 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/memi-design/design-skills/tree/main/skills/superpower
  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/memi-design/design-skills.git /tmp/design-skills
mkdir -p .claude/skills
cp -r /tmp/design-skills/skills/superpower .claude/skills/superpower
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Superpower 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 Superpower 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 Superpower 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.

Mémoire Canvas Orchestrator

Internal Mémoire runbook. Activate only for an explicit orchestration request.

Freedom Level: Maximum

Work autonomously inside the user's granted scope. Host approval, sandbox, security, and user-confirmation policies remain authoritative. Read the relevant project and design context before making changes.

Core Loop

OBSERVE → PLAN → EXECUTE → VALIDATE → ITERATE

1. OBSERVE (always first)

  1. get_code_connect_map — check existing component mappings
  2. get_design_context — read the canvas
  3. figma_search_components — scan components (nodeIds are session-scoped)
  4. figma_get_variables / get_variable_defs — inventory tokens
  5. Read specs/ directory — check existing specs

Never create what already exists.

2. PLAN (Atomic Decomposition)

Decompose every intent bottom-up before writing a single line:

Intent: "Create a dashboard"
├── Page: Dashboard
├── Template: DashboardTemplate
├── Organisms: Sidebar, MetricsPanel, ChartSection, ActivityTable
├── Molecules: MetricCard, ChartContainer, TableRow, NavItem
└── Atoms: Button, Badge, Avatar, Icon, Label, Separator

Build order: atoms → molecules → organisms → templates → pages.

3. EXECUTE

  • Use use_figma for design-system-aware canvas writes (preferred)
  • Use typed Mémoire Figma actions for bulk operations; raw JavaScript execution is not shipped in the default package
  • Spawn parallel agents for independent subtasks (see /multi-agent)

4. VALIDATE (Self-Healing — MANDATORY)

Run after every visual creation or modification:

  1. Execute the design change
  2. figma_take_screenshot
  3. Analyze for: floating elements, raw hex values, missing Auto Layout, inconsistent padding, elements not bound to variables, DROP_SHADOW missing blendMode: "NORMAL"
  4. Fix all issues found
  5. Take final screenshot to confirm
  6. IF still broken after round 3 → report to user with specifics

Never skip step 2. Screenshots are the only reliable validation.

5. ITERATE

  • Multi-pass until output is correct — single-shot is never the goal
  • Full pipeline: canvas → spec → code → preview
  • Never stop at the canvas; always generate specs and code

Prefer Existing Tools Over Custom Code

bash
npx shadcn@latest add button     # don't hand-write button.tsx
memi generate MetricCard         # use the spec pipeline
memi pull                        # extract tokens from Figma
memi tokens                      # export design tokens
memi preview                     # verify generated output

Write custom code only when no existing tool handles the task.

Rules

#Rule
1Call get_code_connect_map before creating any component
2Use mapped codebase components when Code Connect returns a match
3Bind all visual properties to variables — never hardcode hex or pixel values
4Place all elements inside a Section or Frame — never floating on the canvas
5Build bottom-up — atoms first, pages last
6Use use_figma for canvas writes unless raw API is required
7Every canvas element gets a spec (memi spec component <Name>)
8Every spec gets generated code (memi generate <Name>)
9Run memi preview to verify generated output
10Take a screenshot after every visual change — no exceptions

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 Superpower AI skill do?

Orchestrate an explicit Mémoire canvas workflow across design inspection, planning, generation, and verification. Use only when the user requests Mémoire orchestration; do not use as a default session policy.

Why use Superpower on TypingMind?

Because you install it once and use it with any model. Superpower 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 Superpower in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/memi-design/design-skills/tree/main/skills/superpower. 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 Superpower?

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 Superpower?

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

Is the Superpower 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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