Skill UI

This guide sets up a comprehensive, fast, and reproducible local development workflow for integrating with the Cohere API v2. It utilizes mocking, vitest for unit testing, and hot reload capabilities (tsx watch) to ensure rapid iteration cycles without constant reliance on external APIs. Ideal for developers building AI applications with Cohere.

This skill helps users design robust and optimal software interfaces (APIs) for a module by applying the 'Design It Twice' philosophy. It generates multiple, radically different architectural designs using parallel agents, allowing for comprehensive comparison of trade-offs (e.g., simplicity vs. flexibility, specialized vs. general-purpose). Use this when you need to explore various structural options or compare module shapes before committing to a final design.

This guide details advanced patterns for building production-grade fault-tolerant integrations with external APIs, specifically targeting Figma. Learn to implement critical reliability mechanisms such as Circuit Breakers to prevent cascading failures, cached fallbacks for graceful degradation, exponential backoff retries for rate limits, and request timeouts. Use this when your application must remain stable despite external service outages or performance degradation.

Generates publication-quality, editable SVG vector graphics for structured diagrams, including system architectures, complex workflows, and pipelines. By taking a structured FigureSpec JSON as input, this tool ensures deterministic output: the same spec always yields the same diagram, making it ideal for formal documentation and system modeling where precise layout is mandatory. It operates entirely locally without external APIs. Use when you need a highly structured, technical diagram rather than a natural illustration or data plot.

This module provides robust patterns for managing rate limits and throttling when integrating with external APIs (e.g., Flexport). It covers three key strategies: monitoring rate limit headers (X-RateLimit-*), implementing exponential backoff with jitter for transient 429 or 5xx errors, and utilizing queue-based throttling for safe, controlled bulk operations. It ensures resilient and reliable API communication.

This plugin allows developers to build Framer components by syncing data from external data sources (such as APIs or databases) into Framer's Managed Collections. It serves as the primary integration pattern, handling both schema creation and item population. Use this when your design requires displaying dynamic content driven by external systems.

This comprehensive guide teaches developers how to build advanced, interactive, and data-driven components within Framer using React. It covers creating components with dynamic property controls (e.g., animated counters), integrating real-time data from external APIs, and implementing sophisticated behavioral code overrides (such as scroll-triggered animations or magnetic interactions). Ideal for extending Framer's capabilities for professional web design.

A utility designed to collect comprehensive diagnostic evidence for Framer development issues. It automatically gathers runtime environment details, installed package versions, API connectivity status, and necessary configuration files. Use this when preparing detailed support tickets or troubleshooting persistent bugs related to Framer plugins or server-side APIs.

A comprehensive technical guide detailing the process of migrating large-scale enterprise search platforms from sources like Elasticsearch or Algolia to Glean. It covers data export, schema transformation, using bulk indexing APIs, creating data sources, and validating search quality for smooth, large-scale deployment.

This skill provides comprehensive guidelines and implementation examples for managing rate limits when interacting with the Glean Indexing and Search APIs. It includes a custom RateLimiter class, robust retry logic (handling 429 errors and server errors), and best practices for batch processing large datasets, ensuring stable, high-volume data ingestion pipelines.

This document outlines the reference architecture for integrating diverse enterprise knowledge sources (such as Confluence, Jira, Notion, and Slack) into the Glean search index. It details the end-to-end data flow, covering connector frameworks, message queuing, permission synchronization (ACLs), and client-side search APIs. Designed to ensure continuous, reliable, and low-latency unified search across all corporate data.

This skill provides a comprehensive set of APIs for advanced writing analysis using Grammarly. It allows users to score text based on engagement, correctness, clarity, and tone. Furthermore, it includes functionality for AI detection and sophisticated plagiarism checking. It is ideal for developers needing to build content verification, grammar-checking, or academic integrity tools.