Skill UI

A guide and toolkit for optimizing Grammarly API costs in large-scale applications. It addresses common cost overruns associated with high text volume by implementing strategies such as result caching, chunking long documents, sample-based scoring, and setting usage budget alerts. Use this when integrating Grammarly into enterprise workflows or managing billing for high-volume content analysis.

A guide on handling asynchronous operations and event notifications for the Grammarly API. Since Grammarly does not provide native push webhooks, this skill demonstrates techniques for implementing polling mechanisms (e.g., for plagiarism detection) and building a robust event bus to reliably handle streamed results, such as score completion or AI content detection alerts.

This elite skill acts as an art director and implementation strategist for modern web development. It mandates an 'Image-First' workflow: first generating high-fidelity, premium design images for specific sections (e.g., hero, product features), deeply analyzing these visual references, and only then translating them into clean, structured, and highly implementable frontend code. It is designed to avoid generic AI clichés, ensuring results that are aesthetically superior, structurally sound, and ready for professional deployment.

This guide provides a comprehensive overview of the rate limits imposed by the MindTickle API across various endpoints, such as user management, content upload, and quiz result exports. It details specific limits for different operations and provides robust engineering solutions using TypeScript. Key implementation strategies include implementing a token-bucket rate limiter, developing a retry mechanism with exponential backoff, and optimizing bulk data processing through batching to ensure stable and efficient application integration.

This guide provides a comprehensive setup for configuring CI/CD pipelines for Miro REST API v2 integrations. It uses GitHub Actions to automate unit and integration testing, validate API connectivity, and implement crucial cleanup steps to ensure test isolation and reliable results.

A comprehensive guide and implementation pattern for querying, searching, and paginating OneNote content using the Microsoft Graph API. This skill addresses the limitations of the standard OneNote search endpoint, providing robust methods for cross-notebook querying, advanced OData filtering on metadata, and client-side full-text indexing for body content. Ideal for building complex knowledge retrieval systems that handle large, distributed result sets.

This skill manages comprehensive identity verification using the Persona API. It covers government ID checks (passport, driver's license), selfie liveness detection, and database lookups (SSN, watchlist). Use this when implementing Know Your Customer (KYC) workflows, reviewing detailed verification results, or building custom approval logic based on multi-source identity data.

This guide provides a comprehensive walkthrough of the identity verification lifecycle using the Persona REST API. It covers the full process: creating a new inquiry, generating hosted or embedded verification URLs, polling the inquiry status until completion, and finally retrieving detailed KYC results. This is essential for developers building secure, automated user onboarding, and identity verification flows.

Optimizes the performance of the Persona API for identity verification workflows. This guide demonstrates best practices such as implementing batch inquiry creation, caching verification results, and managing parallel polling to ensure efficient and reliable data retrieval when dealing with large volumes of identity data.

This tool facilitates comprehensive prior art searches across global patent databases (Google Patents, Espacenet) and academic literature (Google Scholar, arXiv). It guides users through extracting core inventive concepts, performing targeted searches using keywords and IPC/CPC codes, and analyzing the results to assess novelty, overlap risk, and general freedom-to-operate.

This skill automates the critical submittal workflow within the Procore construction management platform. Users can programmatically create new submittals, defining necessary specifications, assigning reviewers, and setting due dates. It further supports updating the submittal status (e.g., approving or rejecting) and listing filtered results, ensuring systematic tracking and smooth management of the entire project review cycle.

This mechanism is designed to optimize Qdrant's performance when handling queries with very large limits or when paginating results across multiple shards. Instead of forcing every shard to transfer the full requested limit (which is bandwidth-intensive), it uses Poisson distribution statistics to calculate smaller, optimized limits for each shard. This significantly reduces inter-shard data transfer and improves the efficiency of large-scale vector search, while maintaining a very high degree of accuracy.