Skill UI

Expert skill in designing and implementing comprehensive observability stacks for enterprise microservices. Covers advanced monitoring (Prometheus, Grafana), distributed tracing (Jaeger, OpenTelemetry), centralized logging (ELK, Splunk), and defining SLIs/SLOs. Ideal for ensuring high availability, mastering incident response, and performing reliability engineering on large-scale, distributed applications.

Implements advanced Role-Based Access Control (RBAC) for collaborative Obsidian workspaces. This system allows administrators to define granular permissions (read, write, delete, manage) for different user roles (Admin, Editor, Viewer) across specific folders or paths within a shared vault. It is achieved via a custom plugin that intercepts file operations, ensuring data integrity and proper collaboration control in enterprise team environments. Ideal for managing large, secure knowledge bases.

Automates Openperplex operations by orchestrating Rube MCP calls via Composio, covering discovery, connection validation, tool execution, and schema-aware workflows.

This skill provides comprehensive reference architectures for building production-grade AI applications using OpenRouter as a unified LLM gateway. It covers three scaling patterns: simple single-service, standard microservice (with caching and budget enforcement), and enterprise event-driven systems (with message queues and workers). Use this when planning system design, reviewing complex AI pipelines, or ensuring scalability and cost control in your LLM integration.

Provides comprehensive patterns for creating production-grade client wrappers when integrating with OpenRouter via the OpenAI SDK. This skill covers handling advanced topics such as robust retry mechanisms, structured typing, caching strategies, and middleware implementation. It is essential for developers building stable, reliable SDKs or commercial client libraries that require enterprise-level API robustness.

This skill provides comprehensive, multi-layered diagnostic scripts for resolving complex and intermittent issues with the Perplexity Sonar API. It allows users to test connectivity (DNS, TLS, TCP), profile API latency, validate citation stability, and verify search recency filters. It is crucial when standard troubleshooting methods prove insufficient for diagnosing hard-to-replicate API bugs or performance regressions.

A comprehensive guide to implementing Perplexity's API integration at various scales. It provides three validated architectural blueprints—Direct Widget, Cached Research Layer, and Multi-Query Pipeline—allowing developers to choose the optimal structure based on required query volume, latency goals, and complexity. Learn how to scale from simple search widgets to automated, high-volume research pipelines.

This guide details how to configure robust CI/CD pipelines for Perplexity Sonar API integrations using GitHub Actions. It emphasizes best practices for automated testing, including separating costly live integration tests from lightweight mocked unit tests, ensuring code quality while managing API costs and rate limits.

This guide provides a comprehensive reference for diagnosing and fixing common errors and exceptions encountered when interacting with the Perplexity Sonar API. Use this when troubleshooting API integration issues, handling specific HTTP status codes (like 401, 429, 402), or debugging failed requests. It covers best practices for rate limiting, key validation, and implementing robust error handling logic.

This guide provides advanced strategies for developers to significantly reduce Perplexity Sonar API billing costs. It covers key techniques including model routing (selecting the right model by query type), setting maximum output tokens, implementing robust query caching, and utilizing domain filters to minimize per-request charges. Essential for building cost-effective, high-volume AI applications.

This tool is designed to collect comprehensive diagnostic evidence for troubleshooting Perplexity API issues. It automatically gathers critical environment details (OS, Node/Python versions), tests API connectivity, and captures redacted configuration settings. The resulting secure bundle is ideal for submitting detailed support tickets or sharing with technical teammates.

This guide details how to implement Role-Based Access Control (RBAC) for Perplexity API in enterprise environments. Since Perplexity lacks native RBAC, the solution involves building a middleware gateway. This gateway enforces granular policies, including team-specific API key usage, model restriction (e.g., limiting access to 'sonar' only), budget caps, rate limiting, and mandatory search domain filtering for compliance and security.