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Component Search

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yonatangross
component-search

Search 21st.dev component registry for production-ready React components. Finds components by natural language description, filters by framework and style system, returns ranked results with install instructions. Use when looking for UI components, finding alternatives to existing components, or sourcing design system building blocks.

Overview

Publisheryonatangross
Repositoryorchestkit
Skill namecomponent-search
Stars
298
Forks
35
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 yonatangross on GitHub. Read the source before you install it.

Installation

Install the Component Search 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/yonatangross/orchestkit/tree/main/plugins/ork/skills/component-search
  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/yonatangross/orchestkit.git /tmp/orchestkit
mkdir -p .claude/skills
cp -r /tmp/orchestkit/plugins/ork/skills/component-search .claude/skills/component-search
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Component Search 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 Component Search 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 Component Search 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.

Component Search

Search 21st.dev's registry of production-ready React components. Returns ranked results with code, previews, and install instructions.

bash
component-search animated pricing table
component-search sidebar with collapsible sections
component-search dark mode toggle switch

How It Works

Query: "animated pricing table with monthly/annual toggle"
  │
  ▼
┌──────────────────────────────┐
│ 21st.dev Magic MCP           │  Search the 21st.dev developer registry
│ @21st-dev/magic              │  Filter: React, Tailwind, shadcn
└──────────┬───────────────────┘
           │
           ▼
┌──────────────────────────────┐
│ Results (ranked by relevance)│
│                              │
│ 1. PricingToggle (98% match) │  ★ 2.3K views · shadcn/ui
│ 2. PricingCards (87% match)  │  ★ 1.8K views · Radix
│ 3. AnimatedPricing (82%)     │  ★ 950 views · Motion
└──────────────────────────────┘

Step 0: Parse Query

python
QUERY = ""  # Component description

# 1. Create main task IMMEDIATELY
TaskCreate(subject="Component search: {QUERY}", description="Search 21st.dev registry", activeForm="Searching for {QUERY}")

# 2. Create subtasks for each phase
TaskCreate(subject="Parse query and detect project context", activeForm="Detecting project context")  # id=2
TaskCreate(subject="Search component registry", activeForm="Searching registry")                      # id=3
TaskCreate(subject="Present and deliver results", activeForm="Presenting results")                    # id=4

# 3. Set dependencies for sequential phases
TaskUpdate(taskId="3", addBlockedBy=["2"])  # Search needs project context first
TaskUpdate(taskId="4", addBlockedBy=["3"])  # Results need search done

# 4. Update status as you progress
TaskUpdate(taskId="2", status="in_progress")  # When starting
TaskUpdate(taskId="2", status="completed")    # When done — repeat for each subtask

# Detect project context for framework filtering
Glob("**/package.json")
# Read to determine: React version, Tailwind, shadcn/ui, styling approach

# Detect shadcn/ui style for result ranking
Glob("**/components.json")
# Read → "style" field (e.g., "radix-luma", "base-nova")
# Used to prefer components matching the project's visual language

Step 1: Search Registry

Browse first, and let the USER pick

The reason is choice, not cost. Search returns a preview image, a video, the author and the installCommand — enough for a human to recognise the right component at a glance. The model cannot. Guessing which of eight "command palette" results the user meant is the failure mode this flow exists to prevent, and it stays a failure mode on every pricing tier.

If 21st-dev-magic MCP is available (the real path):

python
# 1. BROWSE. Prefer search_picker: same params and results as search, but it
#    renders an inline picker so the USER chooses. Do not pre-filter to one
#    result on their behalf — show the options.
mcp__21st-dev-magic__search_picker(query="{QUERY}", type="component", limit=8)

# 2. PICK — the user selects. Stop here and wait.

# 3. RETRIEVE — the chosen component.
mcp__21st-dev-magic__get_component(id=<demo id from the chosen result>)
  • If the user only needs to know whether something exists, or wants the install command, stop after step 1 — search already answered it.
  • search returns an installCommand containing ?api_key=$API_KEY_21ST. That is a different env var from the MCP's TWENTY_FIRST_API_KEY; if npx shadcn add fails auth, that variable is missing from the environment.

Tier — check it, do not assume it

get_component is the only metered tool; search, search_picker, search_logo, get_theme and every list/metadata tool are unmetered on all tiers.

Call get_usage rather than assuming a cap. It returns tier plus freeRetrievalsRemaining (both null when unlimited).

tierget_component
free2 / day — treat each retrieval as the day's budget: never speculative, never in a loop, never "to compare"
paidunlimited — comparing two candidates is fine; the picker step still applies

Written 2026-08-16 against a free account and corrected 2026-08-17 when the account went paid: the original text argued the flow from the 2/day cap, which made it read as obsolete the moment the cap lifted. The cap is a constraint; letting the user choose is the design.

If 21st-dev-magic is NOT available (fallback):

python
# Genuine fallback ONLY — scraping returns no ids, no install commands, and
# no structured metadata. If the MCP is configured, you should never be here.
WebSearch("site:21st.dev {QUERY} React component")
# Or browse the registry
WebFetch("https://21st.dev", "Search for: {QUERY}")

Alternative generation path — v0.app MCP (2026): When the registry doesn't have a matching component, fall back to AI generation via the v0.app MCP server rather than WebFetch scraping:

python
# If @vercel/v0-mcp is available
# v0.app generates from the same query and can pin to shadcn style
# (e.g., luma / nova / lyra) via `shadcn apply` after download.

This is a generation path, not a registry search — results will not have view counts or stars. Prefer registry search when possible so you get battle-tested components; use v0.app only when the registry misses.

Step 2: Present Results

Show top 3 matches with:

  • Component name and description
  • Match relevance score
  • Popularity (views/bookmarks)
  • Framework compatibility
  • Preview (if available)
  • Install command
python
AskUserQuestion(questions=[{
  "question": "Which component to use?",
  "header": "Component",
  "options": [
    {"label": "{name_1} (Recommended)", "description": "{desc_1} — {views_1} views"},
    {"label": "{name_2}", "description": "{desc_2} — {views_2} views"},
    {"label": "{name_3}", "description": "{desc_3} — {views_3} views"},
    {"label": "None — generate from scratch", "description": "Build a custom component instead"}
  ],
  "multiSelect": false
}])

Step 3: Deliver Component

For the selected component:

  1. Show the full source code
  2. List dependencies (npm install commands)
  3. Note any required peer dependencies (Radix, Motion, etc.)
  4. Highlight customization points (props, tokens, slots)

Framework Compatibility

Project StackSearch FilterNotes
React + TailwindDefault — best coverageMost 21st.dev components
React + CSS ModulesFilter non-TailwindFewer results
Next.js App RouterPrefer RSC-compatibleCheck "use client" directives
Vue / SvelteNot supported21st.dev is React-only
shadcn/ui styleMatch visual languageLuma→rounded/pill, Nova→compact, Lyra→sharp

shadcn v4 style awareness: When components.json has a style (e.g., "radix-luma"), prefer components whose visual language matches — rounded pill shapes for Luma, dense layouts for Nova/Mira, sharp edges for Lyra. Components can be adapted post-install, but a closer match reduces customization work.

Related Skills

  • ork:design-to-code — Full mockup-to-component pipeline (uses this skill)
  • ork:design-system-tokens — Adapt components to project tokens
  • ork:ui-components — Component library patterns

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 Component Search AI skill do?

Search 21st.dev component registry for production-ready React components. Finds components by natural language description, filters by framework and style system, returns ranked results with install instructions. Use when looking for UI components, finding alternatives to existing components, or sourcing design system building blocks.

Why use Component Search on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yonatangross/orchestkit/tree/main/plugins/ork/skills/component-search. 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 Component Search?

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 Component Search?

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

Is the Component Search AI skill free?

Yes. It is published on GitHub by yonatangross 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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