Data & Analytics

--> --- name: bio-cellxgene-census description: Query the CELLxGENE Census (61M+ cells) programmatically. Use when you need expression data across tissues, diseases, or cell types from the largest curated single-cell atlas. Best for population-scale queries, reference atlas comparisons. For analyzing your own data use scanpy or scvi-tools. tool_type: mixed primary_tool: Unknown measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_f...

--> --- name: bio-biopython description: Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices. tool_type: mixed primary_tool: Unknown measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. ...

--> --- name: bio-bindingdb-database description: Query BindingDB for measured drug-target binding affinities (Ki, Kd, IC50, EC50). Search by target (UniProt ID), compound (SMILES/name), or pathogen. Essential for drug discovery, lead optimization, polypharmacology analysis, and structure-activity relationship (SAR) studies. tool_type: mixed primary_tool: Unknown measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell...

--> --- name: bio-astropy description: Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astro...

--> --- name: bio-arxiv-database description: Search and retrieve preprints from arXiv via the Atom API. Use this skill when searching for papers in physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering, or economics by keywords, authors, arXiv IDs, date ranges, or categories. tool_type: mixed primary_tool: Unknown measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_...

--> --- name: bio-arboreto description: Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets. tool_type: mixed primary_tool: Unknown measurable_outcome: Execute skill workflow successfully with valid output within 15 ...

--> --- name: bio-anndata description: "Data structure for annotated matrices in single-cell analysis. Use when\ \ working with .h5ad files or integrating with the scverse ecosystem. This is the\ \ data format skill\u2014for analysis workflows use scanpy; for probabilistic models\ \ use scvi-tools; for population-scale queries use cellxgene-census." tool_type: mixed primary_tool: Unknown measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools...

--> --- name: bio-alphafold-database description: Access AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology. tool_type: mixed primary_tool: Unknown measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-aeon description: This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs. tool_type: mixed p...

--> --- name: bio-proteomics-ptm description: Post-translational modification analysis including phosphorylation, acetylation, and ubiquitination. Site localization, motif analysis, and quantitative PTM analysis with MSstatsPTM. tool_type: mixed primary_tool: proteomics measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Identify, quantify, and analyze post-translational modifications from mass spectro...

--> --- name: bio-proteomics-ms-qc description: Mass spectrometry raw data quality control using PTXQC, rawTools, or MSstatsQC. tool_type: mixed primary_tool: proteomics measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Mass spectrometry data quality control. Computes basic QC statistics for protein/peptide abundance tables.

--> --- name: bio-proteomics-de description: Differential protein abundance testing using MSstats, limma, proDA, and scipy/statsmodels for Python. Multiple testing correction with BH FDR. tool_type: mixed primary_tool: proteomics measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Statistical testing for differentially abundant proteins between experimental conditions.

--> --- name: bio-proteomics-differential-abundance description: Statistical testing for differentially abundant proteins between conditions. Covers limma and MSstats workflows with multiple testing correction. Use when identifying proteins with significant abundance changes between experimental groups. tool_type: mixed primary_tool: MSstats measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: illumina-connected-multiomics description: Operate Illumina's Connected Multiomics SaaS to orchestrate tertiary analysis across single-cell, spatial, proteomic, methylation, and bulk omics with DRAGEN integration. keywords: - multi-omics - illumina - spatial-transcriptomics - methylation - tertiary-analysis measurable_outcome: Build a study, ingest DRAGEN outputs, and publish a multi-layer dashboard (cells + spatial + methylation) for collaborators within one day. license: Propr...

--> --- name: bio-metabolomics-statistics description: "Statistical analysis for metabolomics \u2014 PCA, PLS-DA, clustering,\ \ and univariate tests." tool_type: mixed primary_tool: metabolomics measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Statistical analysis module for metabolomics data. PCA, PLS-DA, hierarchical clustering, and univariate tests.

--> --- name: bio-metabolomics-de description: Metabolomics differential analysis using univariate tests (t-test, FDR), multivariate methods (PCA, PLS-DA, OPLS-DA, sPLS-DA), Random Forest, and ROC analysis for biomarker discovery. tool_type: mixed primary_tool: metabolomics measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Univariate and multivariate statistical analysis for identifying differentiall...

--> --- name: bio-spatial-trajectory description: Trajectory inference and pseudotime analysis using DPT, Monocle3, Slingshot, scVelo for RNA velocity, and PAGA for abstracted graph analysis. tool_type: mixed primary_tool: singlecell measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- You are **SC Trajectory**, a specialised OmicsClaw agent for trajectory inference and pseudotime ordering in single-cel...

--> --- name: bio-sc-preprocessing description: Single-cell RNA-seq QC, normalization, HVG selection, PCA, UMAP, and Leiden clustering. Supports both Scanpy (Python) and Seurat (R) workflows. tool_type: mixed primary_tool: singlecell measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- You are **SC Preprocessing**, the foundation skill for single-cell analysis in OmicsClaw. Your role is to load scRNA-se...

--> --- name: bio-sc-grn description: "Gene regulatory network inference using pySCENIC three-step pipeline\ \ (GRNBoost2 \u2192 cisTarget \u2192 AUCell), with correlation-based fallback. Identifies\ \ transcription factor regulons and scores their activity per cell." tool_type: mixed primary_tool: singlecell measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- You are **SC GRN**, a specialised OmicsCla...

--> --- name: bio-spatial-de description: "Differential expression analysis for single-cell data \u2014 marker\ \ gene discovery using Wilcoxon, t-test, MAST, or DESeq2 pseudo-bulk analysis." tool_type: mixed primary_tool: singlecell measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- You are **SC DE**, the differential expression and marker gene discovery skill for single-cell data. Your role is to id...

--> --- name: bio-sc-communication description: Cell-cell communication analysis via ligand-receptor interaction scoring using CellChat (R), NicheNet (R), LIANA (Python), or built-in L-R database. tool_type: mixed primary_tool: singlecell measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- You are **SC Communication**, a specialised OmicsClaw agent for cell-cell communication analysis via ligand-recept...

--> --- name: rna-velocity description: Infer transcriptional dynamics from spliced/unspliced layers using scVelo with latent time, driver gene ranking, and velocity graph exports. measurable_outcome: Deliver annotated .h5ad files containing velocity layers, latent time, confidence metrics, and ranked driver genes with Markdown + PNG diagnostics. allowed-tools: - read_file - run_shell_command - python reliability: - source: https://github.com/theislab/scvelo score: 0.93 rationale: >- Canonica...

--> --- name: bio-single-cell-perturb-seq description: Analyze Perturb-seq and CROP-seq CRISPR screening data integrated with scRNA-seq. Use when identifying gene function through pooled genetic perturbations in single cells. tool_type: python primary_tool: Pertpy measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-single-cell-metabolite-communication description: Analyze metabolite-mediated cell-cell communication using MeboCost for metabolic signaling inference between cell types. Predict metabolite secretion and sensing patterns from scRNA-seq data. Use when studying metabolic crosstalk between cell populations or metabolite-receptor interactions. tool_type: python primary_tool: MeboCost measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. all...