Skill UI

This library provides a comprehensive Java client for interacting with Azure Table Storage and Cosmos DB Table API. It supports all standard CRUD operations (Create, Read, Update, Delete), advanced querying using OData filters, and batch transactions. Ideal for building scalable, high-performance applications requiring structured NoSQL data persistence on Azure Cloud.

A comprehensive guide to optimizing costs when using the Exa API. This skill teaches techniques like implementing query-level caching (LRU/Redis), strategically selecting search types (keyword vs. neural), query deduplication for batch jobs, and setting up usage monitoring and budget alerts. Use this when managing Exa billing or reducing API expenditure in RAG applications.

This runbook provides a structured guide for responding to critical Guidewire production incidents. It covers the entire lifecycle of incident management, including initial triage (checking monitoring, API errors, and batch logs), diagnosing performance degradation (JVM metrics and query performance), implementing mitigation steps, and proper escalation procedures to Guidewire Support.

This skill provides comprehensive guidance on optimizing Guidewire application performance. It covers advanced development techniques such as optimizing Gosu queries using the Query API, implementing efficient batch processing with BatchProcessBase, implementing caching strategies for frequently accessed reference data, and performing JVM heap tuning via the Guidewire Cloud Console (GCC). Use this when system performance bottlenecks are detected in Guidewire insurance or core business platforms.

A comprehensive guide covering advanced patterns for the Linear SDK using TypeScript/JavaScript. Learn how to implement efficient cursor-based pagination, avoid N+1 query problems by batch-loading relations, perform complex filtering, and handle API calls with type-safe error management when integrating with the Linear GraphQL API.

This guide provides comprehensive strategies for optimizing API interactions with OpenEvidence. It covers implementing robust caching mechanisms (TTL management), efficient batch retrieval of citations, connection pooling, and handling rate limits. By applying these techniques, users can significantly reduce query latency, ensuring smooth system performance even when handling complex multi-condition queries or large citation batches.

A comprehensive workflow utilizing the Perplexity Sonar API for advanced, multi-turn research. It systematically decomposes broad topics into focused sub-queries, executes batch queries while maintaining context continuity, deduplicates citations, and synthesizes all findings into a structured, comprehensive research document. Ideal for academic research, market analysis, and generating detailed investment or industry briefs.

This comprehensive guide outlines advanced strategies for optimizing PostHog in production environments. Key techniques covered include implementing local feature flag evaluation to eliminate network latency, optimizing event batching for network efficiency, utilizing strategic event sampling, and writing efficient HogQL queries. Master these methods to dramatically improve data collection reliability and query speed.

This skill automates GraphQL introspection attacks to extract the complete API schema, including all types, queries, mutations, and subscriptions. It maps the attack surface, tests for query depth limits, and exploits GraphQL-specific weaknesses like batching attacks or nested query DoS. Used for comprehensive GraphQL security auditing and penetration testing.

Automates creation, querying, editing, and bulk operations for Feishu Bitable apps, including field, view, and record management with guidance on required formats, batch limits, and concurrency controls.

Guidance for implementing Cloud Firestore in Flutter: selecting the database, configuring offline persistence, designing schemas, querying with filters and pagination, writing batched transactions, indexing, optimizing for scale, and avoiding write hotspots.

Produces production-ready Dataverse SDK code for Python, showcasing advanced error handling and retries, batch operations, OData query tuning, metadata lifecycle management, configuration/timeouts, cache invalidation, chunked file uploads, and Pandas integrations with references.