Skill UI

A rigorous checklist and guide for conducting causal inference in development economics, specifically tailored to the high standards of top-tier journals like The World Bank Economic Review (WBER). It covers advanced econometric designs (RCT, DiD, RD, IV), focusing not just on estimation but on defining the estimand, addressing external validity, general equilibrium effects, and translating local findings into actionable policy recommendations.

This guide provides a structured framework for conducting and presenting robust checks in econometric manuscripts, particularly relevant for studies using developing-country data. It moves beyond simple appendix dumping by organizing tests around potential identification threats and specific data-quality risks (e.g., measurement error, coverage gaps). It helps ensure the core findings are load-bearing against sophisticated academic scrutiny.

Provides comprehensive guidelines for designing and auditing academic tables and figures, specifically for development economics journals. It teaches how to translate statistical coefficients into actionable policy magnitudes, validate research design (e.g., balance checks, pre-trends), and adhere to strict page limits while maintaining clarity for both economists and policymakers.

This guide provides a comprehensive framework for authors writing for development economics journals, specifically addressing the need to bridge the gap between rigorous econometric analysis and actionable policy advice. It teaches how to structure introductions, write concise abstracts, and frame findings by emphasizing policy magnitudes over statistical significance, ensuring the work speaks to both technical economists and non-specialist policymakers.

This guide assists researchers in assessing the suitability of their manuscripts for the World Economic Papers. It covers the journal's focus on international economics, open macro, and global value chains. It provides detailed checks on formatting requirements (abstract, references), assesses methodological suitability (e.g., GVC decomposition), and identifies potential submission pitfalls, ensuring academic rigor before targeting a high-level academic journal.

This skill guides the creation of self-contained, highly accessible, and reproducible exhibits (tables, figures, maps) for World Politics manuscripts. It addresses strict length limits by differentiating between core arguments (in the article) and supplementary material (online appendix). It emphasizes visual communication excellence, moving beyond simple coefficient tables to forest plots and event studies to convey magnitude and uncertainty effectively.

This guide provides a comprehensive framework for designing rigorous and defensible evaluation sections for machine learning and information retrieval papers, particularly for top-tier conferences like WSDM. It covers the four critical evidence quadrants—Effectiveness, Validity, Efficiency, and Robustness—and details necessary protocols, such as temporal splits, proper baseline selection (including heuristics), ablation studies, and hardware-aware performance reporting. Use this when auditing or writing the experimental section of a research paper.

Biopython is a comprehensive Python library designed for computational molecular biology. It provides robust tools for handling biological sequences (DNA, RNA, Protein), structural analysis (PDB), sequence alignment, phylogenetic tree construction, and interfacing with major public databases like NCBI (Entrez). Ideal for researchers performing advanced bioinformatics tasks.

gget is a versatile CLI and Python package designed for rapid, reproducible bioinformatics searches across major genomic reference databases (e.g., Ensembl, UniProt). It allows users to quickly query gene metadata, perform initial BLAST/BLAT style alignments, and explore associated pathways, cancer, or disease modules. It is ideal for initial data exploration and generating reproducible evidence logs before building complex, full-scale local pipelines.

Seaborn is a high-level Python visualization library designed for creating publication-quality statistical graphics. It facilitates dataset-oriented plotting, multivariate analysis, and complex statistical visualizations (like KDEs, distributions, and relationships) directly from Pandas DataFrames with minimal code. It builds upon Matplotlib to provide semantic mapping and statistical awareness.

This skill audits OpenMed NER or de-identification models to detect performance disparities across sensitive demographic subgroups (e.g., sex, age, race). It calculates metrics like per-group recall and leakage disparity, helping users identify instances of 'under-protection' before deployment, ensuring equitable AI governance and compliance.

This specialized skill is designed to analyze complex security flaws involving forced authentication (coercion) and identity relay chains. It guides users to trace and document the complete, minimal path from the initial coercion trigger (e.g., a specific service or protocol edge) through intermediate hops, to the final service that accepts the relayed credentials. Use this for deep security research, forensics, and documenting the decisive sequence leading to privilege escalation or code execution.