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Deep Component Figma

Organization
southleft
deep-component-figma

Extract a single Figma component (or any node) as a full recursive tree to N levels deep — every child's layout, fills/strokes/effects, typography, resolved bound variables (token names + code syntax), INSTANCE mainComponent references, prototype reactions, and annotations. Use when you need maximum-fidelity data to generate production code for ONE component — triggers: 'get the full component tree', 'deep extract this component', 'give me everything about this node for code gen', 'resolve all the tokens in this component', 'what instances does this component nest', 'extract reactions/prototype links', 'high-fidelity component spec'. Resolves bound variables to token names on ANY plan. Far deeper than the native MCP's get_design_context / get_metadata.

Overview

Publishersouthleft
Repositoryskills-for-figma
Skill namedeep-component-figma
Stars
20
Forks
1
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 southleft on GitHub. Read the source before you install it.

Installation

Install the Deep Component Figma 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/southleft/skills-for-figma/tree/main/skills/deep-component-figma
  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/southleft/skills-for-figma.git /tmp/skills-for-figma
mkdir -p .claude/skills
cp -r /tmp/skills-for-figma/skills/deep-component-figma .claude/skills/deep-component-figma
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Deep Component Figma 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 Deep Component Figma 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 Deep Component Figma 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.

deep-component-figma — unlimited-depth tree with resolved tokens

Walk one node (usually a COMPONENT / COMPONENT_SET / INSTANCE, but any node works) recursively to a depth you choose, capturing at every level: layout (auto-layout, padding, spacing, sizing), visual properties (fills, strokes, stroke weight, corner radius, effects, opacity), typography for text nodes, boundVariables resolved to token names + code syntax, INSTANCE main-component references (including whether the main is a variant and its set), prototype reactions, and dev-mode annotations. This is the richest single-component read for high-fidelity code generation.

Skill boundaries

  • use_figma rules — load the official figma-use skill first; it is the full Figma Plugin API reference. Essentials these scripts rely on: plain JS with top-level await + return (no IIFE, no figma.closePlugin(); console.log is not returned), inputs inlined as const at the top of each script, colors in 0–1 range, load fonts before any text op, await figma.getNodeByIdAsync(...), and atomic errors (a failed script applies nothing — read the error, fix, retry).
  • Whole-file inventory (all tokens/components/styles at once) → use design-system-inventory-figma.
  • Variant set as a CSS state machine (per-state diffs, pseudo-class mapping) → use analyze-component-set-figma.
  • This depth of tree + resolved tokens + reactions is not what the native MCP's get_design_context (rendered single-selection summary) or get_metadata (flat structure) return — it is a design-system code-gen capability layered on top.

Workflow

  1. Get the node id. From selection (figma_get_selection), search, or a pasted id.
  2. Choose a depth. Set const DEPTH (default 10). Deep design components rarely exceed ~6 levels; use a smaller number first to keep the payload manageable, then go deeper if needed.
  3. Run scripts/deep-component.js via use_figma (skillNames: "deep-component-figma"). Set const NODE_ID and const DEPTH at the top.
  4. Generate code. The returned tree mirrors the DOM you should build. Prefer boundVariables token names (and their codeSyntax) over raw hex; use mainComponent refs to reuse imported components; use reactions to wire up interaction/navigation.
  5. If the payload is too big, lower DEPTH (nodes past the limit are summarized with a childCount + _depthLimitReached marker) or target a smaller sub-node id.

What each node carries

  • Identity: id, name, type, visible. Hidden non-component nodes are marked _hidden and not recursed.
  • Layout: layoutMode, axis sizing/align, padding*, itemSpacing, counterAxisSpacing, layoutWrap, min/max width/height, clipsContent.
  • Visual: fills, strokes, strokeWeight, cornerRadius, effects, opacity (only when non-default; figma.mixed values are skipped safely).
  • Typography (TEXT): characters, fontSize, fontFamily/fontStyle, fontWeight, lineHeight, letterSpacing, alignment, truncation, case, decoration.
  • Tokens: boundVariables resolved to { id, name, collection, resolvedType, codeSyntax } per bound property.
  • Instances: mainComponent (id, name, key, isVariant, set name/id) + componentProperties.
  • Definitions (COMPONENT/COMPONENT_SET): componentPropertyDefinitions, variantProperties.
  • Prototype: reactions (trigger + action: navigation/transition/destination).
  • Dev: annotations (label markdown + categorized properties).

Notes

  • Resolved tokens work on ANY plan — the variable lookup is built via the Plugin API, so even non-Enterprise files get token names instead of opaque VariableID: aliases.
  • The root node echoes _variableMapSize and _maxDepthUsed for sanity-checking.
  • Returns affectedNodeIds: [NODE_ID] even though this is a read — keeps the convention uniform.

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 Deep Component Figma AI skill do?

Extract a single Figma component (or any node) as a full recursive tree to N levels deep — every child's layout, fills/strokes/effects, typography, resolved bound variables (token names + code syntax), INSTANCE mainComponent references, prototype reactions, and annotations. Use when you need maximum-fidelity data to generate production code for ONE component — triggers: 'get the full component tree', 'deep extract this component', 'give me everything about this node for code gen', 'resolve all the tokens in this component', 'what instances does this component nest', 'extract reactions/proto...

Why use Deep Component Figma on TypingMind?

Because you install it once and use it with any model. Deep Component Figma 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 Deep Component Figma in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/southleft/skills-for-figma/tree/main/skills/deep-component-figma. 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 Deep Component Figma?

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 Deep Component Figma?

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

Is the Deep Component Figma AI skill free?

Yes. It is published on GitHub by southleft 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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