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 collection of composable handlers and middlewares for Go's standard slog package. This skill enables developers to build robust, multi-stage logging pipelines by implementing critical steps like log sampling (throughput control), attribute formatting (PII stripping), advanced routing, and sinking logs to various professional backends (e.g., Sentry, Loki, Datadog). Ideal for production-grade services requiring high observability.

PyMC is a powerful Python library designed for probabilistic programming and sophisticated Bayesian modeling. It enables users to build complex models, such as hierarchical structures and time series, and perform rigorous inference using methods like MCMC (NUTS) sampling and Variational Inference. Key features include model comparison (LOO, WAIC) and comprehensive diagnostic tools for uncertainty quantification.

Qiskit is a comprehensive SDK for developing quantum algorithms. It enables users to build, optimize, simulate, and execute quantum circuits using modern APIs (Qiskit 2.x). It supports local exact sampling, high-performance noisy simulation via Qiskit Aer, and direct execution on IBM Quantum hardware through the Runtime service, making it a powerful tool for quantum research and development.

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.

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.

A comprehensive guide for planning rigorous scientific studies in Earth and Planetary Sciences, covering sampling, analytical campaigns, experimental runs, and modeling. It ensures that research designs are robust, addressing critical elements like sample context, traceability to reference materials, replication levels, and falsifiability before data collection begins. Essential for high-impact scientific publishing.

This guide assists in structuring the design of Environmental Science & Technology (ES&T) studies, including lab, mesocosm, field sampling, or modeling experiments. It ensures methodological rigor by planning for environmental relevance, necessary controls (e.g., abiotic loss, sorption), proper replication, mass/energy balance, and a comprehensive QA/QC plan from the outset, helping the study withstand expert peer review.