Skill UI

This is a high-level SDK designed for managing the complete lifecycle of AI projects within Azure AI Foundry. It provides comprehensive tools in TypeScript to create, manage, and evaluate sophisticated AI agents, handle resource connections, process datasets, deploy models, and build custom search indexes.

This is a high-level SDK designed for managing the complete lifecycle of AI projects within Azure AI Foundry. It provides comprehensive tools in TypeScript to create, manage, and evaluate sophisticated AI agents, handle resource connections, process datasets, deploy models, and build custom search indexes.

A comprehensive guide to diagnosing and resolving common codebase indexing issues in Cursor IDE. Covers problems like stuck indexing, empty search results (@Codebase), stale data, and performance bottlenecks in large monorepos. Includes steps for cache clearing, configuration management, and optimizing resource limits.

A comprehensive guide to implementing Exa neural search at various scales. This resource details three distinct architectures—Direct Search, Cached Search, and full RAG Pipeline—allowing developers to choose the optimal blueprint based on traffic volume, required latency, and complexity. Use this when designing scalable Exa integrations, from simple UI search to advanced AI-powered research agents.

A comprehensive guide and code implementation for setting up observability, metrics, and alerting for Exa search integrations. This skill tracks crucial KPIs such as search latency (P50/P95), error rates, result usage, and cache hit ratios. It is essential for monitoring search quality, debugging performance bottlenecks, and managing API resource usage in production environments.

This comprehensive guide addresses common technical and business logic errors encountered when integrating with the Juicebox API. It covers HTTP status codes (e.g., 401, 403, 429), including authentication failures, quota exhaustion, rate limiting, data validation issues, and analysis timeouts. The resource provides detailed causes, specific fixes, and best practices for building reliable and robust integrations into recruiting and research workflows.

Defines MCP server tooling with file search, query execution, resource endpoints, prompt templates, and transport configurations alongside a client setup example for streamlined project automation.

This guide demonstrates how to implement robust Role-Based Access Control (RBAC) and financial governance for cloud GPU environments. It allows organizations to allocate resources, set team-specific budgets, whitelist approved GPUs, and track spending via detailed audit logs. Ideal for multi-team research and engineering environments requiring strict cost accountability and access separation.

OpenViking Code Repository Search exposes `ov` terminal commands for scoped semantic, keyword, and glob searches across viking:// resources, lets you read directory summaries/files, and manage indexed repositories (add/remove) with documented error handling.

A comprehensive guide detailing the breaking changes and step-by-step process for migrating applications from Algolia Search SDK v4 to v5. This resource covers updates to method signatures, import styles, and client usage patterns, ensuring a smooth upgrade process for developers.

This skill allows users to programmatically set up a comprehensive observability stack for Oracle Cloud Infrastructure (OCI) resources. It guides the creation of metric queries, alarm rules, custom metric publishing, and detailed log searches using the Python SDK. By automating monitoring and alerting, users gain full control over their infrastructure visibility, eliminating reliance on the OCI Console UI for critical operational checks.

This skill provides a powerful mechanism for modifying and extending AWS infrastructure using Pulumi handlers and dedicated declarative components. It allows developers to manage complex cloud setups, including configuring OpenSearch, VPC networking, resource tagging, custom domains, blue-green deployment strategies, and implementing environment-conditional configurations (e.g., production vs. development).