Skill UI

This advanced workflow enables users to perform deep analyses on large-scale people intelligence datasets. Users can build custom queries with multi-dimensional filters, run comparisons across different candidate pools (e.g., Q1 vs Q4), and aggregate skill density by geographical region. It is essential for talent acquisition teams needing to identify market trends and optimize hiring strategies beyond standard search functions.

PyDESeq2 brings the DESeq2 differential expression workflow to Python, letting users load bulk RNA-seq counts, specify design formulas, fit DESeq2 models, run Wald tests with FDR correction, optionally apply apeGLM shrinkage, and export gene-level statistics within pandas/AnnData pipelines.

This is an advanced, structured workflow designed to conduct deep, multi-source investigations. It synthesizes current public evidence with user-provided context to generate highly structured reports. The workflow explicitly differentiates between verifiable facts, user-supplied evidence, logical inferences, and actionable recommendations, making it ideal for complex comparative analysis or continuous monitoring tasks.

Perform differential gene expression analysis for bulk RNA-seq with PyDESeq2, including formulaic designs, Wald tests, FDR correction, LFC shrinkage, and result visualization.

An end-to-end orchestrator for bulk RNA-seq analysis, guiding users from raw FASTQ reads through QC, alignment, quantification, and differential expression. It supports both nf-core/rnaseq and standalone tools like STAR/Salmon, ensuring reproducible and statistically sound results for gene-level counts and pathway enrichment.

Perform pathway and gene-set enrichment analysis on gene lists or ranked data using tools like gseapy, Enrichr, and g:Profiler. Supports ORA, GSEA, ssGSEA, and functional profiling across GO, KEGG, Reactome, and MSigDB databases. Ideal for interpreting differential expression results, CRISPR screen hits, and cluster markers to identify over-represented biological pathways.

This is an advanced, structured workflow designed to conduct deep, multi-source investigations. It synthesizes current public evidence with user-provided context to generate highly structured reports. The workflow explicitly differentiates between verifiable facts, user-supplied evidence, logical inferences, and actionable recommendations, making it ideal for complex comparative analysis or continuous monitoring tasks.

A comprehensive guide outlining rigorous data analysis and reporting standards for geographic manuscripts. It covers spatial statistics, remote sensing accuracy, and qualitative interpretation, emphasizing spatial honesty, quantifying uncertainty, and ensuring results are robust across different scales and units.

A comprehensive guide for strengthening the methodological rigor of academic manuscripts, particularly for high-stakes journals. It covers advanced techniques for causal identification (e.g., Difference-in-Differences, Instrumental Variables, Regression Discontinuity), pre-analysis planning, linking theory to empirical tests, and defending claims against skeptical reviewers. This skill focuses on methodological architecture, not code.

A comprehensive guide for correcting bias in Difference-in-Differences (DID) analysis conducted with staggered adoption timing or heterogeneous treatment effects. This methodology replaces the biased Two-Way Fixed Effects (TWFE) approach with modern estimators such as Callaway–Sant'Anna, de Chaisemartin–D'Haultfœuille, and Sun–Abraham, ensuring compliance with advanced econometric standards.

A comprehensive guide for advanced econometric identification strategies, detailing best practices for conducting Difference-in-Differences (DID), event studies, and staggered treatment analysis. It provides stringent requirements for ensuring causal claims are robust and methodologically sound in academic research.

This guide outlines rigorous standards for conducting heterogeneity analysis in economic research. It stresses that heterogeneity should not merely involve slicing data by region or size, but must be theoretically driven, linking multiple dimensions (e.g., ownership, financing constraints, industry type) to core economic mechanisms. The analysis must demonstrate significant group differences and clearly connect findings to differential policy implications, elevating the discussion from mere observation to actionable policy science.