Skill UI

Configure automated Continuous Integration and Continuous Deployment (CI/CD) pipelines for Juicebox AI data analysis integrations. This setup utilizes GitHub Actions to run comprehensive unit tests (with mocked datasets) and integration tests (connecting to live APIs) on every PR and merge to main. It ensures that data upload logic, analysis execution, and result parsing workflows are robust and reliable before deployment.

A comprehensive toolkit for reconstructing and analyzing the evolutionary history of biological sequences (genes, proteins, genomes). The standard workflow includes multiple sequence alignment using MAFFT, robust maximum likelihood tree inference with IQ-TREE 2 and FastTree, and advanced visualization/manipulation using ETE3. This skill is essential for studies in viral phylodynamics, microbial genomics, protein family evolution, and molecular clock dating.

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.

This comprehensive geospatial science skill covers remote sensing, GIS, spatial analysis, and machine learning for Earth observation. It supports satellite imagery processing, vector and raster data operations, spatial statistics, and point cloud processing. Users can utilize cloud-native workflows and 8 programming languages with 500+ code examples. Ideal for terrain analysis, hydrological modeling, and marine spatial analysis tasks.

Build or audit reproducible research workflows with NeuroKit2 for physiological time-series: preprocessing, event/interval analysis, multimodal alignment, variability and complexity on ECG, RSP and EDA signals.

This skill enables the construction and analysis of phylogenetic trees using tools like MAFFT for alignment, IQ-TREE 2 for maximum likelihood inference, and FastTree for large datasets. It supports evolutionary analysis, microbial genomics, viral phylodynamics, and molecular clock studies. Visualization is handled via ETE3 or FigTree. Ideal for inferring evolutionary relationships, detecting horizontal gene transfer, and reconstructing ancestral sequences in biological research.

Evaluate scientific claims, experimental design, and evidence quality using GRADE and Cochrane risk-of-bias frameworks. Identifies biases, confounders, statistical pitfalls, and logical fallacies, and guides constructive critical analysis of research papers.

Python toolkit for biological data: sequence manipulation, pairwise and multiple alignment, phylogenetic trees, alpha/beta diversity and UniFrac metrics, ordination (PCoA), PERMANOVA, and FASTA/Newick/BIOM I/O for microbiome analysis.

A comprehensive tutor designed to build robust statistical thinking. It guides users through the entire data analysis lifecycle, from exploratory data visualization and descriptive statistics to complex hypothesis testing and regression modeling. The focus is on understanding the 'why' and 'how' of statistical logic using real datasets and practical problems, rather than just memorizing formulas. It is tool-agnostic, supporting concepts learned in Python, R, SPSS, or Stata.

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.

Hayabusa is a specialized threat-hunting and fast-forensics tool designed to analyze Windows EVTX event logs. It applies a comprehensive library of Sigma detection rules to raw log data, generating a single, prioritized, chronological timeline. The output includes severity levels, MITRE ATT&CK mappings, and detailed metrics, enabling analysts to perform rapid, SIEM-free incident triage and export results for collaborative analysis.

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.