Skill UI

This skill provides comprehensive observability for the Databricks platform. It enables users to monitor job run histories, track cluster utilization and associated costs (DBU), detect slow SQL queries, and audit access logs. Use it for setting up robust monitoring dashboards, ensuring pipeline health, and optimizing resource consumption across the entire data stack.

Detect SQL injection weaknesses by running the sql-injection-detector plugin against codebases, identifying risky queries, and providing remediation guidance to secure applications.

This skill provides deep expertise in building robust Django web applications and powerful RESTful APIs using Django REST Framework (DRF). It covers designing optimized Django models with proper indexes, implementing serializers and viewsets, optimizing ORM queries (select_related/prefetch_related), and setting up secure authentication (JWT). Ideal for full-stack backend development tasks requiring production-grade reliability.

Mastery of advanced system design patterns, including Event Sourcing, CQRS, and Event-Driven Architecture (EDA). This skill covers designing event stores, building read/write projections, orchestrating sagas, and managing eventual consistency. It is ideal for building complex microservices that require guaranteed audit trails, historical state querying (temporal queries), and robust handling of business workflows with compensating actions.

A comprehensive workflow for implementing advanced search and retrieval features within Evernote. It allows users to build complex queries using search grammar, filter notes by tags, notebooks, dates, and completion status, and paginates through large result sets. The service also enriches results by mapping GUIDs to human-readable names for notebooks and tags.

A comprehensive guide to boosting Evernote API integration performance and scalability. Techniques covered include implementing response caching (using Redis), performing metadata-only queries to minimize data transfer, batching multiple operations, and optimizing client connection management. Essential for building high-performance, reliable applications.

Provides comprehensive strategies and code examples for managing API rate limits when integrating with the Evernote platform. Techniques include implementing exponential backoff, wrapping clients with minimum delays, batching operations, and optimizing queries (e.g., using metadata only) to ensure robust, high-throughput, and stable API usage.

This guide provides advanced technical patterns for optimizing Exa API performance in production workloads. It covers critical strategies such as selecting the optimal search type based on latency budgets (e.g., instant vs. neural), implementing robust result caching using LRU, minimizing content retrieval size, and executing parallel queries to maximize throughput. Essential for building reliable, low-latency, and cost-effective search integrations.

A comprehensive guardrail system for external search APIs (like Exa). It enforces content safety policies, manages domain access via allow/blocklists, sanitizes queries to prevent misuse, implements result freshness checks, and enforces per-user usage quotas. Essential for building reliable, secure, and cost-controlled integrations.

This comprehensive guide details essential security practices for integrating the Exa API. It covers key areas such as secure API key management using environment variables, implementing content moderation to filter unsafe results, restricting results via domain allow/block lists, and sanitizing user queries to prevent injection attacks. Use this when hardening or auditing an Exa-powered application.

This guide provides comprehensive setup for implementing CI/CD pipelines for Fireflies.ai integrations. It covers best practices for validating GraphQL queries, running mock-based unit tests (using Vitest), and executing live integration tests against the actual API. It integrates seamlessly with GitHub Actions, ensuring that code changes are automatically validated in a stable, automated testing environment before deployment. Essential for maintaining API stability and robustness.

A comprehensive guide detailing best practices for optimizing GraphQL queries against external APIs, demonstrated using Fireflies.ai. Learn advanced techniques such as requesting minimum required fields (field selection), implementing robust caching strategies (LRU/Redis), and managing rate limits through controlled batch processing and pagination. This ensures stable, fast, and scalable data retrieval.