Skill UI

A comprehensive checklist designed to ensure TwinMind integrations are deployed securely, stably, and compliantly in a production environment. It covers critical best practices across multiple domains, including authentication, data encryption (PII, GDPR), robust infrastructure setup (auto-scaling, load balancing), advanced monitoring (metrics, tracing, alerting), and full CI/CD deployment workflows. Use this when preparing for go-live or conducting production audits.

Automatically checks application metrics such as load times, bundle sizes, and API responses against predefined performance budgets, surfacing regressions and preventing slippage before deployments—ideal for CI/CD workflows.

This skill provides comprehensive monitoring capabilities for GPU instances running on Vast.ai. It collects key metrics including GPU utilization, instance uptime, temperature, and accumulated costs. Use this when setting up operational monitoring dashboards, configuring critical alerts (e.g., idle GPUs, high temperature, budget overruns), or automating resource usage tracking in a cloud computing environment.

This skill thoroughly inspects and validates Vertex AI Agent Engine deployments. It assesses critical components such as the Code Execution Sandbox, Memory Bank, A2A protocol compliance, security posture, and performance metrics. The tool generates weighted production-readiness scores (0-100%) and provides detailed, actionable recommendations to ensure the deployment is robust, secure, and ready for production use.

This comprehensive guide outlines the core principles for developing immersive Virtual and Augmented Reality (VR/AR) applications. It covers critical aspects such as platform selection (Quest, ARKit, ARCore), mandatory performance metrics (FPS, frame budget), user comfort techniques (motion sickness prevention), and best practices for spatial interaction and world design. Essential reading for engineers building next-generation spatial computing experiences.

Retrieve AlphaFold’s AI-predicted protein models via UniProt IDs, download CIF/PDB coordinates, inspect confidence metrics, and feed them into structural analysis or drug-discovery pipelines.

Access over 800,000 economic time series from FRED's API, covering GDP, employment, inflation, interest rates, regional metrics, and more; ideal for macroeconomic analysis, financial research, policymaking, and forecasting workflows.

Access aggregated OFR hedge fund time series and metadata without auth, covering Form PF stats, repo volumes, futures trader positioning, and leverage/counterparty metrics for risk monitoring or research.

End-to-end Neuropixels recording analysis covering SpikeGLX/OpenEphys/NWB loading, preprocessing, motion correction, Kilosort4 sorting, quality metrics, Allen/IBL curation, and AI-assisted visualization for extracellular electrophysiology.

scikit-bio provides a Python-native biological data toolkit for reading/writing FASTA/Newick files, manipulating sequences, building alignments and phylogenies, and calculating diversity metrics for microbiome and ecology workflows.

Toolkit for Python-based survival and time-to-event modeling with scikit-survival, supporting Cox models, ensemble learners, SVMs, and evaluation metrics for censored data.

Statsmodels is a comprehensive Python library dedicated to advanced statistical modeling and econometrics. It provides tools for rigorous statistical inference, including Ordinary Least Squares (OLS), Generalized Linear Models (GLM), mixed-effects models, and time series analysis (ARIMA). Use this skill for detailed diagnostics, coefficient estimation, hypothesis testing, and producing publication-ready statistical reports in fields like econometrics and biostatistics.