missing-data-handling
Diagnose missing data patterns and apply appropriate imputation strategies
Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Diagnose missing data patterns and apply appropriate imputation strategies
Numerical algorithms and computational techniques for statistics
Expert panel data regression analysis with fixed effects and GMM
Panel data analysis with fixed and random effects models
Structured methodology for constructing and verifying mathematical proofs in statistical research
Panel data analysis with Python using linearmodels and pandas.
Use when starting empirical analysis, creating a data pipeline, generating results, or when data or model specifications change. Enforces end-to-end reproducibility — every number in the paper must be regenerable from raw data by a script with a fixed seed. Replaces TDD for the research domain.
Research Coordinator v12.0 - Human-Centered Edition (Systematic Review Automation) Context-persistent platform with 24 specialized agents across 9 categories (A-G, I, X). Features: Human Checkpoints First, VS Methodology, Paradigm Detection, Systematic Review Automation. Supports quantitative, qualitative, mixed methods research, and systematic review automation. Language: English. Responds in Korean when user input is Korean. Triggers: research question, theoretical framework, hypothesis, literature review, meta-analysis, effect size, IRB, PRISMA, statistical analysis, sample size, bias, journal, peer review, conceptual framework, visualization, systematic review, qualitative, phenomenology, grounded theory, thematic analysis, mixed methods, interview, focus group, ethnography, action research, paper retrieval, AI screening, RAG builder, humanization, AI pattern detection
Simulated peer review of the sewage-house-prices manuscript. Dispatches 2 independent referee reviews (parallel) and an editorial decision (sequential). Produces referee reports and accept/revise/reject recommendation. This skill should be used when asked to "review the paper", "get feedback", "simulate peer review", or "what would referees say".
Fit, summarize, plot, and interpret a chosen CausalPy experiment. Use after the causal method has been selected, including when configuring PyMC/sklearn models and scale-aware custom priors.