Skill UI

A comprehensive guide covering essential security practices for developing Obsidian plugins. Learn how to safely handle sensitive credentials (avoiding data.json), implement robust input validation, prevent Cross-Site Scripting (XSS), secure URI handlers, and adhere to modern Electron and Obsidian guidelines for data privacy and system integrity.

A comprehensive guide for developers migrating and upgrading Obsidian plugins across major API versions. It covers critical steps such as auditing plugin compatibility, implementing version-aware settings migration logic (for user data persistence), replacing deprecated API calls (e.g., Vault, Editor, FileManager), and updating configuration files like manifest.json to ensure stability on new Obsidian releases.

A comprehensive guide to the Obsidian event system, detailing how to handle various lifecycle and state changes within plugins. Learn to register listeners for vault events (file creation, modification, deletion), workspace events (layout changes, view activation), and metadata cache updates. Essential for building highly reactive and deeply integrated Obsidian plugins.

Automates Open Graph metadata guidance for frontend workflows, offering best practices, React/Vue/CSS tips, and ready-to-apply code.

Operates as an end-to-end GitHub repository steward, triaging issues, reviewing PRs, analyzing contributor activity, and generating maintenance guidance so the human can simply ask “what’s next” while the agent keeps UX, docs, and backlog healthy.

This skill provides comprehensive patterns for creating, maintaining, and validating OpenAPI 3.1 specifications for RESTful APIs. Use it for design-first API contract definition, generating specs from existing code, creating detailed API documentation, validating implementation conformance against the contract, and generating client SDKs. It ensures consistency across the entire API lifecycle.

Automates OpenAPI specification work by guiding API developers through best practices, generating production-ready code/configs, and validating outputs for REST, GraphQL, and authentication design patterns.

Automates the Continuous Integration and Continuous Deployment (CI/CD) process for OpenEvidence clinical decision support integrations. This setup ensures that unit tests (using mocked evidence queries and citations) and live API connectivity tests are run automatically on every pull request and merge to main. It validates critical functions like query formatting, evidence parsing, citation extraction, and response quality scoring, maintaining high reliability for medical AI applications.

This guide provides comprehensive error handling and debugging steps for the OpenEvidence API. It covers common failure points—including authentication issues (401, 403), query validation errors (422), rate limiting (429), and service timeouts (408). Users can learn how to diagnose the root cause of connection failures and refine their clinical queries to maximize data retrieval accuracy.

This skill provides comprehensive guidelines and optimization strategies for managing API usage and reducing costs when interacting with OpenEvidence. It covers best practices such as implementing caching, batching requests, selecting appropriate API tiers, and monitoring usage dashboards to ensure efficient and cost-effective data retrieval in healthcare applications.

This comprehensive guide defines the secure data lifecycle management for OpenEvidence, an AI-powered platform for clinical evidence synthesis. It outlines data classification (e.g., clinical queries, patient responses), strict adherence to global regulations (HIPAA, GDPR, FDA), and technical requirements for handling Protected Health Information (PHI). Key processes include PHI de-identification, data validation, secure API integration, and compliant data export, ensuring the system maintains the highest level of patient privacy and regulatory compliance.

This bundle collects comprehensive diagnostic evidence from the OpenEvidence clinical decision support API. It validates API credentials, checks query endpoint health, retrieves evidence citations, and captures model metadata. Use it to diagnose complex issues like query failures, missing references, or high latency without needing direct dashboard access.