Skill UI

scikit-bio provides a Python-native biological data toolkit for reading/writing FASTA/Newick files, manipulating sequences, building alignments and phylogenies, and calculating diversity metrics for microbiome and ecology workflows.

TorchDrug is a PyTorch-native toolbox for building graph neural networks tailored to molecules, proteins, and biomedical knowledge graphs, covering property prediction, generation, retrosynthesis, and knowledge graph reasoning with curated datasets and models.

GeoMaster is a comprehensive geospatial science skill that guides remote sensing, GIS, spatial analysis, and machine learning workflows for Earth observation and planetary data projects, covering satellite imagery, vector/raster math, point clouds, terrain, hydrology, and cloud-native data stacks.

A comprehensive guide to building an automated Indicator of Compromise (IOC) enrichment pipeline using OpenCTI. This skill teaches how to leverage OpenCTI's native STIX 2.1 model and internal connectors to pull deep context from major external threat intelligence sources like VirusTotal, Shodan, AbuseIPDB, and GreyNoise. The pipeline automatically enriches newly ingested indicators, correlates them with known threat actors and campaigns, and generates confidence scores for rapid analyst prioritization.

AWS Detective automatically analyzes log data from CloudTrail, VPC Flow Logs, GuardDuty, and EKS audit logs to build interactive behavior graphs. This tool enables security analysts to investigate suspicious activity, trace lateral movement across accounts, profile compromised entities (IAM users, EC2 instances), and reconstruct complex attack timelines without manual log parsing. Ideal for triaging GuardDuty findings or investigating suspected AWS compromises.

Build and operate reproducible genomics workloads on DNAnexus using dx CLI, dxpy, apps/applets, native workflows, and Nextflow. Supports data transfers, app development, execution monitoring, and project automation for genomic data analysis pipelines.

This comprehensive guide provides blueprints for implementing optimal Snowflake data architectures. It covers diverse patterns, including traditional data warehouses, Lakehouse designs using Iceberg tables, decentralized Data Mesh structures, and advanced data sharing and Snowpark native patterns. Use this when designing enterprise data platforms across varying scales.

This guide details the process of creating advanced, static visualizations in Power BI using Python libraries like matplotlib and seaborn. It covers the full workflow, from script writing and data injection to best practices and limitations, enabling users to render complex, custom charts not supported by native Power BI visuals.

A comprehensive guide detailing the rigorous methodologies for conducting and reporting high-level academic research. It covers norms for quantitative, comparative-historical, and computational analyses, emphasizing transparency, reproducibility, robustness checks (e.g., confounders, alternative specifications), and linking claims directly to evidence, suitable for top-tier social science journals.

A comprehensive guide to structuring and defending research designs across various sociological methods, including quantitative, comparative-historical, ethnographic, and computational approaches. It emphasizes building a credible link between theoretical arguments and empirical evidence, critically addressing alternative hypotheses, and defining the scope of causal claims to meet the highest academic standards.

This guide provides rigorous methodological standards for conducting mechanism analysis (mediation and moderation) in academic economics research. It strongly cautions against the traditional three-step mediation approach due to identification issues, recommending preferred alternatives like split-channel regression and causal moderation analysis to enhance the robustness of causal claims.

A comprehensive methodological guide for conducting exhaustive robustness checks in academic economics manuscripts, particularly for journals requiring high empirical rigor. It provides an exhaustive checklist covering variable replacement, sample window manipulation, advanced identification strategies (like PSM-DID and placebo tests), and crucially, systematically ruling out alternative explanations and confounding factors. Mastering this process is essential for defending research findings against rigorous peer review.