Find UI elements using natural language descriptions
npx skills add https://github.com/testdriverai/testdriverai --skill testdriver:locating-elements
<!-- Generated from locating-elements.mdx. DO NOT EDIT. -->
Use natural language to describe elements. Descriptions should be specific enough to locate the element, but not too-specific that they break with minor UI changes. For example:
await testdriver.find('email input field');
await testdriver.find('first product card in the grid');
await testdriver.find('dropdown menu labeled "Country"');
<Info>TestDriver will cache found elements for improved performance on subsequent calls. Learn more about element caching here.</Info>
After finding an element, you can inspect its properties for debugging:
const button = await testdriver.find('submit button');
console.log(button);
This outputs all element properties:
{
description: 'submit button',
found: true,
x: 150,
y: 300,
coordinates: { x: 150, y: 300, centerX: 200, centerY: 320 },
threshold: 0.8,
confidence: 0.95,
similarity: 0.92,
selector: 'button[type="submit"]',
cache: {
hit: true,
strategy: 'pixel-diff',
createdAt: '2025-01-15T10:30:00Z',
diffPercent: 0.02,
imageUrl: 'https://...'
}
}
Find and interact with multiple elements:
// Find all matching elements
const products = await testdriver.findAll('product card');
console.log(`Found ${products.length} products`);
// Interact with each
for (const product of products) {
const title = await product.find('title text');
console.log('Product:', title.text);
await product.find('add to cart button').click();
}
// Or find specific element
const firstProduct = products[0];
await firstProduct.click();
Integration with protocols.io API for managing scientific protocols. This skill should be used when working with protocols.io to search, create, update, or publish protocols; manage protocol steps and materials; handle discussions and comments; organize workspaces; upload and manage files; or integrate protocols.io functionality into workflows. Applicable for protocol discovery, collaborative protocol development, experiment tracking, lab protocol management, and scientific documentation.
Analyzes job descriptions and generates tailored resumes that highlight relevant experience, skills, and achievements to maximize interview chances
Generate Excalidraw diagrams from natural language descriptions. Use when asked to "create a diagram", "make a flowchart", "visualize a process", "draw a system architecture", "create a mind map", or "generate an Excalidraw file". Supports flowcharts, relationship diagrams, mind maps, and system architecture diagrams. Outputs .excalidraw JSON files that can be opened directly in Excalidraw.
Build and distribute Expo development clients locally or via TestFlight
Use when you have a written implementation plan to execute in a separate session with review checkpoints
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.
Take testdriverai/testdriver:locating-elements from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.