Skill UI

This bundle collects comprehensive diagnostic evidence from the Juicebox platform. It gathers API connectivity status, dataset health reports, analysis quota usage, and rate limit information into a single archive. Use it to systematically troubleshoot complex issues such as failed dataset uploads, stalled AI analysis runs, API authentication failures, or quota exhaustion.

This module provides minimal working examples for the OpenEvidence API, designed for clinical intelligence applications. Users can perform complex clinical queries to get treatment recommendations, check potential drug interactions based on patient context, and search for authoritative medical guidelines from major health organizations (e.g., ACC/AHA). Ideal for developing evidence-based healthcare decision support systems.

An AI-driven comprehensive health analysis system that integrates multi-dimensional datasets, including biometrics, lifestyle, and medical history. It excels at anomaly detection, predicting various health risks (e.g., CVD, Diabetes), providing personalized, evidence-based recommendations, and generating detailed, interactive health reports.

This skill integrates multiple digital health sources (such as Apple Health, Fitbit, Oura, and CSV/JSON files) into a unified system. It processes, standardizes, and validates diverse health data formats, and connects this data to a dedicated knowledge base (WellAlly.tech). It provides intelligent recommendations and article retrieval based on the user's current health context and needs.

This utility detects and classifies various anomalies (such as spikes, flatlines, drift, or pattern breaks) within telemetry data streamed from IoT devices. It utilizes Z-score anomaly detection to provide actionable insights. This is crucial for investigating unusual device metrics, ensuring system health before deploying major firmware updates (canary advancement), or triaging fleet-wide operational alerts.

This comprehensive guide helps researchers evaluate whether a clinical or health-services study is appropriate for publication in JAMA Internal Medicine. It details the journal's high bar for methodological rigor, including requirements for randomized trials (CONSORT), observational studies (STROBE), and systematic reviews (PRISMA). It covers everything from study design and statistical reporting to addressing potential desk-reject triggers, ensuring authors submit high-quality, impactful research.

A comprehensive guide to assessing whether a mental health or psychiatry study is suitable for publication in The Lancet Psychiatry. It details the journal's high standards, focusing on large-scale population/epidemiological analyses, policy relevance, and rigorous methodology (e.g., RCTs, cohort studies). The skill helps authors re-frame their research question to meet global health and system-level impact requirements, covering reporting guidelines (CONSORT/STROBE/PRISMA) and submission structure.

This skill implements an advanced reference pattern for integrating with Monitoring Control Platform (MCP) servers. It ensures accurate data retrieval by first reading a domain-specific 'gotchas' reference file, mitigating query failures caused by complex server behaviors, pagination limits, or subtle API quirks. It analyzes issue data, groups errors by root cause, and generates a structured, actionable markdown report for improved system health visibility.