Skill UI

Simulates a highly structured, sequential peer-review process using multiple specialized AI agents (e.g., Skeptic, Constraint Guardian, User Advocate). This skill is designed to rigorously stress-test a design, surface hidden assumptions, identify potential failure modes, and enforce non-functional constraints before implementation. It transforms initial concepts into robust, validated specifications by simulating formal critique and achieving consensus via a mandatory Decision Log.

This skill provides a comprehensive decision framework and set of patterns for architects designing cloud-agnostic applications across major providers like AWS, Azure, and GCP. It details service comparisons (Compute, Storage, DB), multi-cloud patterns (e.g., Best-of-Breed, DR), cost optimization strategies, and structured migration plans, ensuring robust and resilient enterprise deployments.

A comprehensive guide for setting up Continuous Integration and Continuous Deployment (CI/CD) workflows for Obsidian plugins using GitHub Actions. This setup automates critical tasks like build validation on every push, manifest consistency checks, version number bumping, and structured GitHub releases, ensuring robust and reliable plugin deployment lifecycles.

A comprehensive, hands-on guide to building a minimal working Obsidian plugin. This skill demonstrates the five core building blocks of any Obsidian plugin, including defining typed settings, creating various command types (palette, editor, callback), implementing modals, and utilizing status bar updates. Perfect for developers new to the Obsidian API or those needing a structured way to learn core plugin patterns.

A comprehensive toolkit for implementing production-grade observability in Obsidian plugins. It provides structured logging with log levels and history, a robust metrics collector (supporting counters, gauges, and timers for performance tracking), and an error tracker with deduplication. Users can build a real-time debug panel to monitor plugin performance, diagnose issues, and set up comprehensive error reporting.

This runbook serves as a comprehensive guide for responding to incidents within the OpenEvidence platform. It outlines severity levels (P1-P3), step-by-step triage procedures, and mitigation actions, ensuring rapid and structured resolution of API downtime or degradation issues in a healthcare context.

This SDK provides comprehensive, production-ready patterns for interacting with the OpenEvidence clinical evidence API. It enables developers to efficiently query medical literature, retrieve clinical guidelines, and generate evidence-based recommendations. Key features include a singleton client for consistent authentication, robust error wrappers (handling retries, invalid keys), and structured data models designed to ensure audit compliance and detailed citation provenance in complex healthcare applications.

This guide provides a comprehensive roadmap for migrating and upgrading integrations with the OpenEvidence clinical AI platform. It addresses critical API changes, such as schema restructuring (e.g., flat answers to structured evidence), version detection, and updated citation formats. Developers can use this knowledge to implement robust migration logic, ensuring that downstream clinical applications maintain high levels of accuracy and safety when accessing evolving medical evidence.

This bundle provides a structured mechanism for capturing comprehensive debug information when interacting with the OpenRouter API. It captures the full request parameters, response content, detailed error logs, generation metadata (such as cost, token usage, and provider), and the execution environment. This tool is essential for diagnosing failed API calls, unexpected behaviors, or performance/latency issues.

A comprehensive technical audit checklist designed to validate the production readiness of OpenRouter API integrations. It covers critical areas including security practices (e.g., secret management, key rotation), system reliability (e.g., fallbacks, retries, timeouts), and observability requirements (e.g., structured logging, cost tracking). Use this guide before launching or during major operational reviews to ensure robust, scalable, and compliant deployments.

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.

Provides a structured workflow to analyze and improve application performance across frontend, backend, and database layers, including profiling, bottleneck identification, optimization tactics, and validation steps.