Skill UI

This guide teaches advanced methodology for identifying target species using multiple biological features. Users learn to combine distinct clues such as habitat, morphology, behavior, and geographic range. A key technique involves first identifying the country through demographic clues (e.g., population growth, endemic status) to significantly narrow the search scope before searching for the specific species.

This skill is designed to translate vague, colloquial, or descriptive biological details (such as behaviors, appearances, or cultural uses) into precise scientific or common species names. It is essential for constructing effective search queries in biological databases when the question does not use formal taxonomic names. By accurately identifying candidate species names from descriptive clues, users can drastically improve search relevance and accuracy.

A comprehensive mentor for graduate students and early-career researchers, guiding the entire research lifecycle. This skill covers formulating rigorous research questions, designing studies (quantitative, qualitative, mixed-methods), conducting literature reviews, analyzing data, and writing publishable papers. It emphasizes methodological rigor, critical thinking, and ethical considerations.

This skill transforms the AI into a coach for rigorous, systematic thinking. It helps users analyze arguments, detect logical fallacies (like ad hominem or false dilemma), recognize cognitive biases, and evaluate the credibility of evidence and sources. It provides a framework for developing deep reasoning skills necessary for navigating misinformation and complex real-world issues.

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.

LaminDB is an open-source lineage-native lakehouse designed for biological datasets and models. It enables queryable, traceable, and validated data management across various storage formats like S3 and Postgres. Users can track computational workflows, curate data with ontologies via Bionty, and ensure FAIR compliance. Ideal for managing scRNA-seq, spatial transcriptomics, and integrating ML pipelines while maintaining reproducibility and collaboration.

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.

This specialized skill automates the process of converting visual designs (from Stitch) into high-quality, modular React and Vite components. It adheres to modern frontend best practices, enforcing logic isolation via custom hooks, data decoupling using mock data files, and ensuring type safety with TypeScript interfaces. The workflow includes automated asset fetching, style mapping (Tailwind), and rigorous validation cycles to produce production-ready, maintainable code.

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 guide and utility for managing API interactions with BambooHR. It implements robust retry logic (with exponential backoff and jitter) to handle 503 and 429 rate limit errors. It also provides patterns for proactive optimization, such as queue-based throttling, batching data using custom reports, and utilizing incremental sync to maximize API throughput and reliability.