Skill UI

Provides comprehensive guidelines for achieving high levels of scientific reproducibility for academic papers, particularly those in systems metrics. It covers mapping claims to verifiable evidence, ensuring simulators are seeded and deterministic, and documenting measurement provenance to allow reviewers to independently verify all reported results.

A comprehensive guide for designing rigorous Monte Carlo simulation studies, particularly for methods-heavy academic submissions (e.g., SMR). This guide covers specifying the Data Generating Process (DGP) space, selecting non-negotiable competitor methods, choosing appropriate performance metrics (e.g., power, bias, coverage), and critically reporting the limitations and failure regimes of the proposed method to ensure maximum credibility and reproducibility.

A comprehensive guide for authors aiming to submit manuscripts to 'Studies in Science of Science.' It details the journal's focus areas, such as science metrics, knowledge networks, and technology policy, emphasizing the need for deep theoretical and methodological contributions. The skill helps authors optimize structure, avoid common rejection traps, and ensure adherence to academic publishing standards.

A comprehensive tool for assessing whether an econometric manuscript is suitable for submission to The Econometrics Journal (EctJ). It guides authors through evaluating methodological novelty, theoretical rigor, structural compliance, and adherence to high academic standards, covering everything from new estimators and inference procedures to house style and potential desk-reject risks. Use this skill when targeting a highly specialized economic journal or needing to re-frame an applied study into a theoretical methods contribution.

Statsmodels is Python's premier library for rigorous statistical modeling. It provides comprehensive tools for estimation, inference, and diagnostics across a wide range of statistical methods. Use this skill for advanced analysis, including linear regression (OLS, WLS), generalized linear models (Logistic, Poisson), and complex time series forecasting (ARIMA). Ideal for econometric studies and producing publication-ready results.

This skill computes disproportionality metrics (PRR, ROR, EBGM, IC) using FAERS/OpenFDA drug-event data. It analyzes spontaneous reports to statistically screen for potential drug-reaction associations (Signal of Disproportionate Reporting, SDR). By constructing a 2x2 contingency table, it helps pharmacovigilance professionals identify over-reported adverse events, providing a statistical hypothesis for drug safety assessment.

This skill calculates and documents the residual re-identification risk of de-identified datasets. It goes beyond simple direct identifier removal by assessing the risk posed by quasi-identifiers (age, ZIP, dates). It computes structural metrics like k-anonymity and l-diversity, and runs empirical linkage attacks to provide a defensible memo supporting HIPAA Expert Determination for determining if a dataset is safe for release.

This skill performs deep 'git archaeology' by cross-referencing commit history, changelogs, and initial summaries. It extracts verified metrics like commit velocity, contributor rankings, and release timelines, generating multiple detailed reports. Use this when building a REX talk and needing factual, data-backed evidence from a source git repository.

This skill performs pharmacokinetic and pharmacodynamic modelling, including non-compartmental analysis, compartmental fitting, and population PK. It helps derive exposure metrics, evaluate dosing regimens, and predict drug interactions using Python scripts. Suitable for analyzing concentration-time data and supporting clinical pharmacology workflows.

Stress-test core claims for Journal of Systems Engineering submissions: parameter sensitivity, initial conditions, alternative models and metrics, extreme scenarios, Monte Carlo repetitions, random seeds, software environments, data/code statements, and explicit failure boundaries.

ADHX provides a dedicated API to fetch X/Twitter posts as clean, structured JSON. It extracts comprehensive data, including author profiles, engagement metrics (likes, retweets), and full article content for long-form posts. This method is vastly superior to traditional scraping techniques, making it ideal for LLM analysis, summarization, and structured data workflows.

A comprehensive guide to product analytics, covering event tracking, funnels, cohort retention, north star metrics, OKRs, and dashboards. Examples include PostHog implementation and Python code for retention analysis. Ideal for product managers and data analysts seeking data-driven decisions.