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.

This skill acts as an expert guide for implementing comprehensive monitoring and observability solutions. It covers setting up the three pillars of observability—metrics, distributed tracing, and log aggregation—along with defining monitoring architecture, creating actionable dashboards, and establishing effective alerting runbooks. Use it to achieve full visibility into system health, reduce MTTR, and ensure proactive issue detection.

This skill specializes in implementing Service Level Objectives (SLOs) and Service Level Indicators (SLIs) to enforce reliability standards. It guides users in designing comprehensive SLO frameworks, calculating error budgets, and building robust monitoring systems. Use this when you need to establish data-driven reliability targets, balance feature velocity with technical health, and align service performance directly with core business objectives.

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.

This runbook provides systematic procedures for diagnosing and resolving critical failures within the Obsidian knowledge base. It guides users through recovering from plugin crashes, vault corruption, sync conflicts, and performance degradation using advanced command-line tools and developer console access. Essential for advanced users and technical support.

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 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.

This skill automates the setup and configuration of Ollama, allowing users to deploy Large Language Models (LLMs) locally. It performs system assessment (OS, GPU, RAM), selects optimal models based on hardware availability, and handles installation across macOS, Linux, and Docker. Use this when you require free, self-hosted AI alternatives, need offline inference capabilities, or wish to eliminate reliance on costly hosted API services.

This comprehensive playbook provides effective patterns and structured templates for managing on-call shift transitions. It guides engineers on documenting active incidents, ongoing investigations, recent system changes, and upcoming maintenance events. The goal is to ensure seamless context transfer, maintaining continuity and reliable incident response across different shifts.

A comprehensive workflow for systematically reviewing medical evidence. It allows users to search databases for clinical studies, build curated citation collections, grade evidence using established frameworks like GRADE, and generate professionally formatted, structured reports in academic citation styles (e.g., AMA). Ideal for conducting literature reviews and supporting clinical research. This tool helps transform raw medical data into actionable, structured knowledge.

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 system implements Role-Based Access Control (RBAC) for OpenEvidence, an AI-powered clinical decision support platform. It strictly manages access to sensitive data, including PHI-adjacent datasets and medical literature, ensuring compliance with regulations like HIPAA. The system defines distinct roles—Admin, Clinician, Researcher, Student, and Auditor—with granular permissions for querying, data export, and institutional configuration, maintaining a secure and auditable data environment.