Figma.Enumerate Component Urls logo

Figma.Enumerate Component Urls

Organization
coinbase
figma.enumerate-component-urls

Enumerates every public component set in a CDS Figma file and produces a list of [Component Name]: [Figma URL] entries grouped by page section. Use this skill whenever asked to list, generate, or audit Figma component URLs, produce a component inventory from a Figma file, or when doing a Code Connect refresh/audit that requires knowing which Figma node each component maps to. Also trigger when someone asks "what are the Figma URLs for CDS components" or wants to find the node-id for a specific component.

Overview

Publishercoinbase
Repositorycds
Skill namefigma.enumerate-component-urls
Stars
506
Forks
114
Bundled files
1
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 coinbase on GitHub. Read the source before you install it.

Installation

Install the Figma.Enumerate Component Urls 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/coinbase/cds/tree/master/.claude/skills/figma.enumerate-component-urls
  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/coinbase/cds.git /tmp/cds
mkdir -p .claude/skills
cp -r /tmp/cds/.claude/skills/figma.enumerate-component-urls .claude/skills/figma.enumerate-component-urls
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Figma.Enumerate Component Urls 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 Figma.Enumerate Component Urls 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 Figma.Enumerate Component Urls 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.

Overview

Produces a [Name]: [URL] list for every public component set in a CDS Figma file, grouped by the file's page sections. Uses the Figma REST API directly (more reliable than the Figma MCP tools, which only expose the currently open page in the desktop app).

Requires $FIGMA_ACCESS_TOKEN to be set in the environment.

Default file: Unless the user provides a different Figma URL, always use the canonical CDS Components file:


Step 1 — Get all pages via the REST API

Use the file key above (or extract it from a user-provided URL — it's the segment between /design/ and the next /). Then fetch all pages at depth=1:

bash
FILE_KEY="k5CtyJccNQUGMI5bI4lJ2g"  # override if user provided a different URL
curl -s -H "X-Figma-Token: $FIGMA_ACCESS_TOKEN" \
  "https://api.figma.com/v1/files/${FILE_KEY}?depth=1" | python3 -c "
import json, sys
data = json.load(sys.stdin)
pages = data['document']['children']
for p in pages:
    print(p['id'], '|', p['name'])
"

The output mixes two kinds of pages:

PatternMeaning
⚡️ Actions, 🔘 Inputs, …Section header — group label only, no component sets inside
↳ Button, ↳ Modal, …Component page — contains the actual component set(s)

Collect only the component page IDs (those with the ↳ prefix) for Step 2. Ignore section header pages, cover pages, staging pages, and deprecated sections (usually marked ⛔️ Deprecated).


Step 2 — Batch-query pages for component sets

Query the /nodes endpoint at depth=2 to discover COMPONENT_SET children within each page. Batch many page IDs into one request to stay efficient:

bash
# IDs are colon-separated (e.g. 60:512), URL-encode : → %3A and , → %2C
IDS="60%3A512%2C0%3A1%2C52733%3A3517"
curl -s -H "X-Figma-Token: $FIGMA_ACCESS_TOKEN" \
  "https://api.figma.com/v1/files/${FILE_KEY}/nodes?ids=${IDS}&depth=2" | python3 -c "
import json, sys
data = json.load(sys.stdin)
for nid, node in sorted(data['nodes'].items()):
    if node is None:
        continue
    page = node['document']
    sets = [c for c in page.get('children', []) if c['type'] == 'COMPONENT_SET']
    print(f'=== {page[\"name\"]} (id: {nid}) ===')
    for s in sets:
        print(f'  [SET] {s[\"id\"]} | {s[\"name\"]}')
    if not sets:
        # No sets at top level — print child types to diagnose
        for c in page.get('children', [])[:5]:
            print(f'  [{c[\"type\"]}] {c[\"id\"]} | {c[\"name\"]}')
"

Edge case: pages that use SECTION containers

Some pages nest their component sets inside Figma SECTION nodes rather than placing them directly as page children. When depth=2 returns only SECTION entries and no COMPONENT_SET entries, re-query those section IDs at depth=2 to find the component sets inside them.

The CDS Tray page is a known example — its Tray component set lives inside a section called "Tray component".


Step 3 — Exclude internal helper sets

Component sets whose names start with . are private design helpers used internally by other components. They are not public consumer APIs and should be excluded from the output.

Known examples in the CDS file:

  • Carousel: .count, .carouselPagination
  • Select: .Input Chip, .Dropdown Menu, .Checkbox, .Tray/Multi Select, .Desktop Input Chips, .CheckboxGroup
  • Modal: .Header
  • SegmentedTabs: .tabs
  • TabbedChips: .Tab Chip
  • BottomTabBar: .Tab Bar Icon
  • Tray: .ListCells-Mobile, .ListCells-Desktop, .TabbedChipps-Mobile, .TabbedChipps-Desktop, .Drag handle

The rule is simple: if name.startswith('.'), skip it.


Step 4 — Format the output URLs

Figma node IDs use : internally (60:512) but URLs use - (node-id=60-512).

Always link to the component node — never the page node.

Every entry must point to the actual component, whether it's a COMPONENT_SET (has variants) or a bare COMPONENT (no variants). Both are valid link targets. The only thing that's wrong is using a CANVAS (page) node ID — that lands on the whole page rather than the component.

SituationUse in URLName entry
Page has 1 public component (set or standalone)That component's node IDThe page name (stripped of ↳ and emoji)
Page has 2+ public componentsEach component's node IDThe component/set name

If you're unsure of a node's type, check via the REST API — type will be COMPONENT_SET, COMPONENT, or CANVAS. Skip CANVAS IDs.

URL template:

https://www.figma.com/design/k5CtyJccNQUGMI5bI4lJ2g/✨-CDS-Components?node-id={component-set-id-with-dashes}

Output format

Group entries under the section headers from the file (Layout, Actions, Inputs, etc.) using markdown ## headings. Within each section, list components in the order they appear in the file:

## ⚡️ Actions

**Button**: https://www.figma.com/design/k5CtyJccNQUGMI5bI4lJ2g/✨-CDS-Components?node-id=0-1
**ButtonGroup**: https://www.figma.com/design/k5CtyJccNQUGMI5bI4lJ2g/✨-CDS-Components?node-id=52733-3517
**IconButton**: https://www.figma.com/design/k5CtyJccNQUGMI5bI4lJ2g/✨-CDS-Components?node-id=57214-1766

For multi-set pages, each set gets its own line. If a set name contains emoji decorators (like 🔮 Card carousel), keep them — they convey meaning (e.g., 🔮 = experimental in CDS).


Tips

  • Batch aggressively: The /nodes endpoint accepts many IDs at once. Prefer fewer large requests over many small ones to stay within rate limits.
  • Check for WIP pages: Pages prefixed with 🚧 are works in progress. Include them but note the WIP status in the output.
  • Section order matters: Preserve the top-to-bottom page order from Step 1 when grouping under section headers — it matches the Figma file's navigation structure, which is what consumers expect.

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 Figma.Enumerate Component Urls AI skill do?

Enumerates every public component set in a CDS Figma file and produces a list of [Component Name]: [Figma URL] entries grouped by page section. Use this skill whenever asked to list, generate, or audit Figma component URLs, produce a component inventory from a Figma file, or when doing a Code Connect refresh/audit that requires knowing which Figma node each component maps to. Also trigger when someone asks "what are the Figma URLs for CDS components" or wants to find the node-id for a specific component.

Why use Figma.Enumerate Component Urls on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/coinbase/cds/tree/master/.claude/skills/figma.enumerate-component-urls. 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 Figma.Enumerate Component Urls?

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 Figma.Enumerate Component Urls?

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

Is the Figma.Enumerate Component Urls AI skill free?

It is published on GitHub by coinbase. 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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