Skill UI

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.

Access DepMap data to fetch CRISPR Chronos dependency scores, drug sensitivity readouts, and gene effect profiles for cancer cell lines, supporting target validation, biomarker discovery, and synthetic-lethality research via API or downloads.

Prepares and validates research-only clinical decision-support evaluation, evidence-profile, cohort, survival, biomarker/model, privacy, and governance artifacts using aggregate or synthetic data. Not for patient care or live clinical use.

This tool provides access to the Cancer Dependency Map (DepMap) data, derived from genome-wide CRISPR screens, RNAi, and compound sensitivity assays. It allows users to query gene dependency scores, identify cancer-specific vulnerabilities, predict synthetic lethal interactions, and validate potential oncology drug targets across various cancer cell lines.

Create, analyze, and visualize complex networks in Python with NetworkX 3.x. Build graphs from data, run algorithms like shortest paths, PageRank and centrality, detect communities, generate synthetic networks, and read/write GraphML, JSON or edge-list files.

This workflow establishes a comprehensive local environment for developing and testing clinical AI integrations. It simulates an Electronic Health Record (EHR) using a local HAPI FHIR server and utilizes synthetic patient data. Developers can iterate on complex encounter workflows and API connections safely, without ever using real Protected Health Information (PHI).

This skill enables the creation and registration of reusable cloned voices from local audio samples for subsequent Text-to-Speech (TTS) applications. It implements a strict 'Consent-first' workflow, requiring explicit user consent, detailed metadata (speaker, permitted use), and adherence to copyright laws before any voice cloning can occur. It ensures the resulting voice ID is safe, traceable, and legally compliant for professional content creation.

A portable, framework-agnostic system designed for robust field-side observability in React or React Native apps. It establishes a strongly typed event taxonomy, ensures best-effort, non-blocking data fan-out across multiple analytics providers, and enforces consent gates for all telemetry data. This system complements synthetic testing by capturing real user vitals (LCP, INP, CLS) and behavioral events in production.

Run simulated focus groups by leveraging real CRM data to predict how diverse customer segments will react to new messaging, pricing structures, or product concepts. This powerful tool generates structured predictions, including sentiment analysis and key objections, providing directional feedback before committing to expensive live research or campaigns. It bridges the gap between intuition and data-driven strategy.

This tool simulates market demand using synthetic audience panels grounded in real CRM data to estimate customers' willingness-to-pay (WTP). It calculates optimal price points, acceptable price ranges, and analyzes price sensitivity across different buyer segments. Use this command to guide pricing strategies before product launch, tier adjustments, or competitive positioning evaluation, providing actionable intelligence without expensive formal research.

A comprehensive guide for researchers submitting work to INTERSPEECH. It details how to package and manage various artifacts—including code, audio samples, and trained models—through the conference lifecycle. The document emphasizes maintaining anonymity during review, ensuring scientific reproducibility, managing complex data licenses, and adhering to ethical guidelines for voice data and synthetic media release.

This guide provides an advanced framework for conducting rigorous causal inference and policy evaluation, essential for academic journals. It covers state-of-the-art methods including DiD, RDD, Synthetic Control, and experimental analysis. The core focus is moving beyond simple p-values to estimate effect magnitudes in tangible 'decision units' (e.g., dollars per household, percentage points). It mandates comprehensive stress-testing, including pre-trend checks, placebo outcomes, and detailed subgroup heterogeneity analysis, ensuring results are immediately actionable by regulators.