Skill UI

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 skill provides advanced techniques to optimize interactions with the Hootsuite API. It addresses common performance bottlenecks by implementing efficient LRU caching for social profiles, optimizing message scheduling through concurrent batch processing, and ensuring robust connection reuse. Use this when experiencing slow API responses or needing to process high volumes of automation tasks efficiently.

This skill creates complete specifications (requirements, design, tasks) for all features in a roadmap using parallel sub-agent dispatch organized by dependency waves. It ensures dependency ordering, cross-spec consistency, and parallel processing for efficient development planning.

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.

A comprehensive guide and implementation demonstrating how to handle API rate limits when interacting with AI services like Together AI. It details the token bucket algorithm for client-side throttling and provides robust retry mechanisms (exponential backoff) to manage 429 (Rate Limit Exceeded) and 5xx server errors, ensuring stable, high-throughput batch processing.

This skill converts Portable Document Format (.pdf) files into Markdown format, making the content easily analyzable, summarized, searchable, or extracted. It is designed to handle various user requests involving PDFs, such as summarizing reports, extracting data, or processing folders of documents. It can also be invoked alongside sibling skills for batch processing mixed file types (PDF, DOCX, XLSX).

This utility provides fundamental text processing capabilities, including detailed word and character counting, frequency analysis, and various text transformations (e.g., uppercase, title case). It is designed for analyzing documents and batch processing files, supporting output in both JSON and human-readable formats.

Apply Martin Kleppmann-inspired rules for designing reliable, scalable, and maintainable data systems. Covers consistency, replication, partitioning, transactions, streams, batch processing, and schema evolution. Use when making architecture decisions for data-intensive applications.

Knap is a command-line tool that renders Markdown templates using structured data like JSON or CSV. It supports variables, filters, and logic to batch-generate notes, format Defuddle output, and validate templates. Ideal for developers automating note creation and content processing workflows.