Skill UI

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 provides a local development loop for integrating the OpenEvidence clinical decision support API. Developers can build health-tech tools and quickly iterate using mock evidence queries, citation responses, and clinical summaries. It supports toggling between mock mode for rapid development and sandbox mode for validation against the real platform, ensuring development uses de-identified data.

This guide outlines the strict multi-environment setup for the OpenEvidence clinical AI system, crucial for maintaining HIPAA compliance. It details how to configure and validate separate environments (Development, Staging, Production) to manage different data classifications—synthetic, de-identified, and full PHI. The process emphasizes environment validation checks, BAA adherence, and robust audit logging to prevent regulatory violations and PHI exposure.

This guide provides essential security protocols and coding best practices for integrating with OpenEvidence, a platform processing Protected Health Information (PHI). It covers mandatory topics such as HIPAA compliance, API key management, webhook signature verification, input validation, and data redaction, ensuring all interactions are secure and audit-compliant.

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

Helps analyze and enhance application caching strategies by reviewing TTLs, key structures, hit rates, and invalidation policies to boost Redis or Memcached performance.

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.

Auto-activating ML training skill that kicks in whenever you mention optuna study creator, then guides you through data prep, model training, hyperparameter tuning, and experiment tracking with production-ready code and validations.

Automated guidance for DevOps practitioners handling package json manager tasks, offering best practices, configuration generation, and validation for foundational CI/CD and infrastructure workflows.

Guides API development teams through pagination helper tasks by providing best-practice guidance, ready-to-run code, and validation for REST, GraphQL, OpenAPI, and related authentication/design workflows.

An expert guide for performing complex data analysis, cleaning, and transformation using pandas DataFrames. This skill covers advanced techniques such as vectorized operations, efficient merging, time series resampling, group-by aggregations, and memory optimization, ensuring production-grade code and robust data validation.

Guides you through automating Parseur tasks via Composio’s Rube MCP, covering tool discovery, connection validation, schema compliance, and execution best practices so workflows stay reliable.