Skill UI

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.

This skill provides advanced techniques for optimizing PostHog Cloud usage and reducing associated costs. It covers auditing event volume, implementing intelligent event sampling (e.g., sampling pageviews or autocapture events), filtering bot traffic, and tuning session recording rates. The goal is to maintain critical analytical value while staying within budget limits.

This comprehensive guide outlines advanced strategies for optimizing PostHog in production environments. Key techniques covered include implementing local feature flag evaluation to eliminate network latency, optimizing event batching for network efficiency, utilizing strategic event sampling, and writing efficient HogQL queries. Master these methods to dramatically improve data collection reliability and query speed.

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.

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.

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.

A comprehensive guide for conducting rigorous empirical analysis on field and survey data in low- and middle-income settings. It covers advanced methods for handling clustered sampling, measurement error, differential attrition, and treatment-effect heterogeneity, ensuring results meet the high standards of reproducibility and robustness required by leading developmental economics journals.

A comprehensive methodology guide for designing rigorous multi-country or cross-cultural studies, essential for international marketing research. It covers critical steps such as country selection criteria, ensuring construct, translation, and sampling equivalence, and planning for measurement invariance (MGCFA). It guides researchers through advanced designs including cross-cultural experiments, multi-level estimation, and handling secondary export/entry-mode data.

A comprehensive guide for auditing survey methodology, focusing on the Total Survey Error (TSE) framework required by top-tier academic journals like Public Opinion Quarterly (POQ). It provides detailed steps to rigorously defend every methodological element—including coverage, sampling, nonresponse, weighting, and measurement—to meet AAPOR standards. This is a critical tool for strengthening research rigor before submission.