Skill UI

COBRApy is a Python library for constraint-based reconstruction and analysis of genome-scale metabolic models, letting you load SBML/JSON/YAML models, perform FBA/FVA/sampling, and study gene or reaction knockouts for systems biology and metabolic engineering.

Structured workflow for building hierarchical Bayesian models with PyMC 5+, covering data prep, NUTS sampling, diagnostics, posterior checks, and predictive inference.

Query UCSF’s ZINC22 repository of 230M+ purchasable compounds via the web UI or CartBlanche APIs to fetch IDs/SMILES, run similarity, supplier, or random sampling, and assemble docking-ready libraries for virtual screening.

PyMC is a powerful Python library for probabilistic programming and advanced Bayesian inference. This comprehensive guide details the full workflow for building, fitting, and validating complex Bayesian models. Learn to implement hierarchical structures, perform MCMC sampling (NUTS), conduct posterior predictive checks (PPC), and effectively quantify uncertainty using industry best practices.

A comprehensive guide to implementing custom visual effects in Three.js using shaders. Learn how to work with GLSL, utilize ShaderMaterial, manage uniforms (scalars, vectors, matrices, textures), and manipulate vertex and fragment data. Covers advanced patterns like texture sampling, vertex displacement, and custom lighting effects.

A comprehensive toolkit for building robust, structured logging pipelines in Golang. It provides advanced modules for handler composition (slog-multi), throughput control (slog-sampling), attribute transformation (slog-formatter), and integrates with major backend logging sinks (e.g., Sentry, Loki, Datadog, Elasticsearch). Use this when building complex Go applications that require deterministic log routing, sampling, and data sanitization.

Performs constraint-based metabolic modeling with COBRApy: FBA, pFBA, FVA, gene knockouts, flux sampling, medium design, production envelopes, gap filling, and SBML model validation for systems biology and metabolic engineering.

Build, fit, and validate Bayesian models with PyMC 6, covering hierarchical models, MCMC/NUTS sampling, variational inference, prior and posterior predictive checks, diagnostics, and LOO/WAIC model comparison.

PyTorch Geometric is a standard library for Graph Neural Networks built on PyTorch. It supports node, link, and graph classification, message passing algorithms like GCN and GAT, heterogeneous graphs, and neighbor sampling. Ideal for building scalable GNN models with custom datasets and transforms.

A guide to managing and optimizing costs within the Klaviyo platform. This skill teaches how to audit active profiles, clean up unengaged contacts by suppressing them, implement event sampling for high-volume tracking, and monitor API usage to prevent unexpected billing increases. Ideal for marketing teams focused on maximizing ROI and maintaining a healthy mailing list.

This skill guides the creation of production-grade Model Context Protocol (MCP) servers and basic clients using C# and the .NET framework. It ensures adherence to the stable 2.x specification, addressing common pitfalls like stateful/stateless HTTP defaults, handling deprecations (roots, sampling), and utilizing appropriate transports (STDIO, Streamable HTTP). Essential for developers integrating structured AI APIs into enterprise applications.

A comprehensive guide for strengthening and defending the methodological rigor of academic manuscripts across various anthropological subfields (ethnography, archaeology, biological, etc.). It instructs users on how to justify sampling, interpretative leaps, ethical considerations, and how to preemptively address rival readings and potential disconfirmations, ensuring the argument is robustly connected to evidence.