A comprehensive SwiftUI component skill for building highly customizable, cross-platform search bars with support for tokens and suggestions.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install searchbar
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install searchbar using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is SearchBar Skill?
The SearchBar skill provides expert guidance on implementing a powerful and flexible search component tailored for the SwiftUI ecosystem. This Openclaw Skills resource bridges the functional gap between platform-specific implementations, such as UISearchBar on iOS and native SwiftUI views on macOS, by offering a unified API. It enables developers to create sophisticated search experiences that feel native across the entire Apple device spectrum, including visionOS.
By using this skill, developers can easily move beyond standard search fields to incorporate advanced UI elements like glass materials, capsule styling, and complex search tokens. The skill focuses on streamlining the development process by prioritizing built-in modifiers over manual view construction, ensuring high performance and adherence to platform-specific design guidelines.
SearchBar Skill Use Cases
- Creating unified search interfaces that work seamlessly across iOS, iPadOS, macOS, and visionOS.
- Implementing advanced filtering systems using search tokens and dynamic suggestions.
- Customizing the visual aesthetics of search components to match brand identity using materials and custom shapes.
- Managing complex search lifecycle events and focus states within a SwiftUI View hierarchy.
- Developing localized search experiences that automatically adapt to system language settings.
How SearchBar Skill Works
- Initialize the SearchBar by providing a text binding and a descriptive prompt to the component.
- Apply styling modifiers such as .searchBarStyle to define the shape, background color, and tint of the search interface.
- Configure display modes for internal elements like the Cancel and Clear buttons to dictate their visibility behavior.
- Bind advanced data structures for tokens and suggestions to enable complex filtering and auto-completion workflows.
- Implement action closures for events like begin editing, end editing, and cancellation to trigger specific app logic.
- Use platform-specific material modifiers to enhance the visual depth, particularly on visionOS and modern iOS versions.
SearchBar Skill Setup
To utilize this SearchBar resource from Openclaw Skills, you must first integrate the Swift Package into your project:
- In Xcode, select File > Add Package Dependency.
- Enter the repository URL:
https://github.com/SzpakKamil/SearchBar.git. - Select the version and add it to your app target.
- Import the module in your Swift code:
import SearchBar
Ensure your project deployment targets are set to iOS 14.0+, macOS 11.0+, or visionOS 1.0+.
SearchBar Skill Data Schema & Taxonomy
The SearchBar skill organizes data through a series of SwiftUI bindings and configuration models. The following structure outlines how information is handled within this Openclaw Skills implementation:
| Attribute | Data Type | Purpose |
|---|---|---|
| text | Binding<String> | Tracks the user's active search query. |
| tokens | Binding<[Token]> | Manages active filter capsules (requires iOS 16+). |
| suggestions | [Suggestion] | List of potential search matches or categories. |
| prompt | String | The placeholder text visible when the field is empty. |
| style | SearchBarStyle | Defines the corner radius, materials, and color scheme. |
SearchBar Skill Advanced Features
- Support for experimental glass and transparent material effects for modern UI design.
- Integrated search token management for sophisticated, multi-parameter data filtering.
- Automatic suggestion filtering logic to minimize manual data handling within the view.
- Custom icon injection to replace standard search glass visuals with context-specific imagery.
- Comprehensive event hooks for granular control over the search interaction lifecycle.
SKILL.md
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