Skill UI

A comprehensive CLI skill for interacting with Paperzilla. It allows users to fetch the latest project recommendations, summarize canonical academic papers into Markdown, analyze paper relevance to research, export structured data (JSON/Atom), and provide feedback on recommendations. Ideal for researchers needing deep access to academic literature data.

This skill guides users on how to search for physical monuments, memorials, or landmarks by linking them to their originating historical events. The core technique involves treating the historical event (e.g., a battle, a protest, a significant date) as the primary search anchor, and then locating the associated memorial or structure. It provides structured query templates and anti-patterns to ensure accurate, context-aware geographical and historical information retrieval.

Use this skill to comprehensively verify all constraints related to a candidate cricket player. By cross-referencing structured data from sources like Wikipedia or Cricinfo, you can confirm details such as birth date, debut match specifics (format, opponent, score), career records, and teams played for. It is crucial for establishing factual accuracy, especially when distinguishing between different formats like ODI, T20I, and Test matches.

AnnData is a core Python framework designed for managing and analyzing annotated matrices, essential in single-cell genomics and high-dimensional data science. It provides a standardized structure (X, obs, var) to store experimental measurements alongside rich metadata. Users can read/write various formats (h5ad, zarr), perform subsetting, concatenation, and complex data manipulation, making it indispensable for modern bioinformatics workflows.

A powerful tool designed for systematic literature reviews and meta-analyses. It searches scientific papers and retrieves highly structured experimental data directly from the full text, going beyond mere abstracts. It extracts over 25 fields, including detailed methods, quantitative results, sample sizes, quality scores, and conclusions, enabling comprehensive evidence synthesis for academic research.

BioServices is a comprehensive Python library providing programmatic access to over 40 major bioinformatics web services and databases (e.g., UniProt, KEGG, ChEMBL). It is designed for complex biological data workflows, enabling cross-database querying, identifier mapping across diverse biological resources, and advanced sequence/pathway analysis in a single, consistent API structure.

Dask is a powerful Python library designed for scalable, parallel, and distributed computing. It enables workflows for datasets that exceed available RAM or require processing across multiple machines. Users can scale familiar pandas and NumPy operations (DataFrames, Arrays) and process unstructured data (Bags) efficiently, making it ideal for large-scale ETL, analytics, and distributed machine learning tasks.

An autonomous research agent that plans complex investigations using Directed Acyclic Graphs (DAGs). It executes sub-questions in parallel, performs gap analysis, and iteratively refines findings before synthesizing a comprehensive, structured report. Ideal for deep market analysis, academic literature reviews, or complex problem-solving requiring multiple data sources.

Perform deep exploratory data analysis (EDA) across a vast array of scientific formats, including chemistry, bioinformatics, microscopy, and spectroscopy. This skill automatically detects the file type, assesses data quality and integrity, extracts format-specific metadata, and generates detailed markdown reports. It is ideal for understanding the structure, content, and potential analysis paths for complex scientific datasets.

LaminDB is an open-source, lineage-native lakehouse designed for managing complex biological datasets and computational workflows. It makes diverse scientific data (like scRNA-seq, AnnData) queryable, traceable, and compliant with FAIR principles. Core capabilities include robust data lineage tracking, schema validation, ontology-backed annotation (Bionty), and unified querying across multiple cloud/local storage systems. It is essential for bioinformatics research and building reproducible data infrastructure.

This powerful Python library provides comprehensive medicinal chemistry filters for compound triage in drug discovery. Users can apply established drug-likeness rules (like Lipinski's Rule of Five, Veber, and CNS rules), detect structural alerts (PAINS, NIBR), calculate complexity metrics, and filter molecular libraries using a custom query language. It is essential for prioritizing hit compounds and optimizing lead structures.

Molfeat is a comprehensive Python library designed to convert chemical structures, such as SMILES strings, into numerical feature vectors. It unifies over 100 pre-trained and hand-crafted molecular featurizers (e.g., ECFP, MACCS, ChemBERTa). This tool is essential for building Quantitative Structure-Activity Relationship (QSAR) models, conducting virtual screening, performing similarity searches, and training deep learning models on molecular datasets.