Skill UI

Enables Claude to examine EXPLAIN plans, spot slow queries or missing indexes, and deliver concrete optimization steps—ideal for improving database query speed and resource usage when performance issues arise.

This tool provides comprehensive cost management for Apollo.io API usage. It optimizes credit expenditure by implementing a daily budget tracker, preventing double-enrichment through deduplication, and using a lead scoring mechanism to ensure credits are only spent on high-potential leads. Ideal for large-scale data enrichment tasks to minimize API costs.

Implements enterprise-grade Role-Based Access Control (RBAC) for Apollo.io API integrations. This skill is essential for managing team permissions, ensuring data segregation, and implementing robust security controls by proxying API calls. It defines granular roles (Viewer, Analyst, Admin) and maps specific API operations to required permissions, preventing all-or-nothing key usage.

A comprehensive Python SDK for interacting with Azure Monitor services. It enables developers to execute complex Kusto Query Language (KQL) queries against Log Analytics workspaces and retrieve various resource metrics (CPU usage, network traffic, etc.). Ideal for building robust monitoring dashboards, automated alerting systems, and custom data analysis pipelines in cloud environments.

This framework provides comprehensive compliance and security auditing for Cursor IDE usage. It covers major regulations like SOC 2, GDPR, and HIPAA, guiding users through detailed checklists, evidence collection, and remediation steps. It is essential for enterprise teams requiring robust data governance and adherence to strict industry standards.

Defuddle is a CLI tool to extract clean Markdown from HTML web pages. It removes navigation, ads, and clutter, reducing token usage. Users can output Markdown, JSON, or HTML, and extract metadata. Preferred over WebFetch for standard pages, it provides cleaner content for processing and analysis.

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.

A comprehensive guide and implementation pattern for enforcing granular Role-Based Access Control (RBAC) on the Exa search API in multi-tenant or enterprise environments. This skill demonstrates how to implement API key scoping, restrict search domains by role, enforce maximum result limits, and track daily usage to ensure robust data governance and security.

A comprehensive, compliant wrapper for integrating with the Groq API. This module automatically sanitizes input prompts and filters model responses to redact sensitive PII (emails, phone numbers, SSN, etc.). It also tracks detailed token usage and generates robust audit logs, ensuring data privacy and regulatory compliance (GDPR, etc.) in enterprise AI applications.

This guide provides comprehensive metrics, instrumentation code (TypeScript), health check logic, and alerting rules for monitoring a SaaS platform like Juicebox. It covers critical metrics such as search latency (p95), data ingestion rates, API error rates, and quota usage, ensuring the maintenance of an optimal user experience and data integrity. Use this to establish robust operational monitoring dashboards and alerts.

This module implements comprehensive Role-Based Access Control (RBAC) for multi-tenant LangChain applications. It provides granular permission models for controlling model access, tool execution, and chain invocation. Key features include multi-tenant data isolation in vector stores, enforcing usage quotas (token limits), and integrating middleware for robust security enforcement. Ideal for building secure, scalable, and compliant enterprise LLM systems.

This guide provides production-grade coding patterns for integrating Lokalise into TypeScript and Node.js applications. It covers crucial best practices such as client singleton implementation, robust cursor-based pagination for large datasets, structured error handling with retry logic, and efficient batch operations. Implementing these patterns ensures reliable, scalable, and maintainable API usage.