Découvrez les skills documentation scientific computing, comparez leurs dépôts et consultez leurs instructions.
Patterns for protein structure prediction using AlphaFold2/ColabFold, structural analysis, model quality assessment, and integration with experimental data. Covers best practices and critical interpretation of...
Patterns for physics-based simulation including numerical integration, rigid body dynamics, fluid simulation, finite element methods, and multi-physics coupling. Covers accuracy, stability, and performance. Use...
Conduct deep research on any topic using parallel subagents and web tools (web_search, web_fetch, playwright). Use for queries that require comprehensive research from multiple perspectives.
Internal skill - Research subagent that executes focused research tasks using web tools. Called automatically by the deep-research lead agent.
Master the AI tools that accelerate research and information gathering. From market research to academic analysis, find insights faster and make better decisions. Use when "research, find information, academic...
Synthesize information across multiple sources into a structured document. Processes sources in batches across sessions to handle more data than fits in context. Discovers and queues related sources automatical...
Deep research on a topic, creating persistent documentation for future reference. Use for technology decisions, competitive analysis, or complex topics.
Performs comprehensive, multi-layered research on any topic with structured analysis and synthesis of information from multiple sources. Use when the user needs thorough investigation, market research, technica...
Ultra-fast data discovery and loading skill for industrial finance datasets. Handles CSV, JSON, DuckDB, Parquet, and Feather with memory-efficient 2-step discovery and loading.
TAM/SAM/SOM calculation, competitor analysis, and market trends identification. Use when analyzing markets, validating business ideas, or entering new markets.
Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.
Use this skill when working with scientific research tools and workflows across bioinformatics, cheminformatics, genomics, structural biology, proteomics, and drug discovery. This skill provides access to 600+...
Time series machine learning toolkit for classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use this skill when working with temporal data, performing t...
Autonomous biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical rese...
Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
This skill should be used when working with single-cell omics data analysis using scvi-tools, including scRNA-seq, scATAC-seq, CITE-seq, spatial transcriptomics, and other single-cell modalities. Use this skill...
Comprehensive biosignal processing toolkit for analyzing physiological data including ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals. Use this skill when processing cardiovascular signals, brain activity, electr...
Molecular machine learning toolkit. Property prediction (ADMET, toxicity), GNNs (GCN, MPNN), MoleculeNet benchmarks, pretrained models, featurization, for drug discovery ML.
Primary Python toolkit for molecular biology. Preferred for Python-based PubMed/NCBI queries (Bio.Entrez), sequence manipulation, file parsing (FASTA, GenBank, FASTQ, PDB), advanced BLAST workflows, structures,...
Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
Query CZ CELLxGENE Census (61M+ cells). Filter by cell type/tissue/disease, retrieve expression data, integrate with scanpy/PyTorch, for population-scale single-cell analysis.
Genomic file toolkit. Read/write SAM/BAM/CRAM alignments, VCF/BCF variants, FASTA/FASTQ sequences, extract regions, calculate coverage, for NGS data processing pipelines.
Gene regulatory network inference with GRNBoost2/GENIE3 algorithms. Infer TF-target relationships from expression data, scalable with Dask, for scRNA-seq and GRN analysis.
Graph-based drug discovery toolkit. Molecular property prediction (ADMET), protein modeling, knowledge graph reasoning, molecular generation, retrosynthesis, GNNs (GIN, GAT, SchNet), 40+ datasets, for PyTorch-b...
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.