Find the most suitable skill for a given biomedical task. Use this skill when: (1) You are unsure which skill to use for a specific biomedical task, (2) You want to discover available skills for a particular domain, (3) You need to compare multiple skills for a given use case.
Find the most suitable skill for your biomedical task by analyzing your request and matching it against available skills.
The agent analyzes the user's natural language request and determines the most suitable skill based on:
| Skill | Description |
|---|
| drug-candidate-discovery | Generate diverse druggable molecules for a given target or disease using AI-powered drug discovery tools. |
| drug-lead-analysis | Analyze drug candidate molecules for drug-likeness (QED, Lipinski), ADMET properties, BBB penetration, and safety profiles. |
| target-based-lead-design | Generate diverse lead compounds for a specific protein target using structure-based drug design with MolCraft. |
| admet-prediction | Predict comprehensive ADMET properties (BBB penetration, side effects, Caco-2 permeability, half-life, LD50 toxicity). |
| retrosynthesis-planning | Expert-in-the-loop retrosynthetic planning workflow for breaking down target molecules into available starting materials. |
| drug-drug-interaction-analysis | Analyze potential drug-drug interactions (DDI) for up to 5 drugs using KEGG DDI database. |
| text-based-molecule-editing | Modify molecules based on natural language descriptions using MolT5/BioT5 models. |
| Skill | Description |
|---|---|
| protein-mutation-analysis | Analyze protein mutations by retrieving protein data, explaining mutation effects with MutaPLM, and predicting structure. |
| mutation-design-aav | Design high-fitness and high-diversity mutants of AAV VP1 capsid protein. |
| mutation-design-gfp | Design high-fluorescence and high-diversity GFP mutants. |
| functional-protein-design | Generate functional protein sequences using CodeFun with Gene Ontology (GO) tag guidance. |
| similar-protein-retrieval | Retrieve proteins with similar structures or sequences from UniProt, PDB, and AFDB databases. |
| structure-prediction-boltz-2 | Predict protein complex structures and protein-ligand complexes with binding affinity using Boltz-2. |
| protein-structure-design-boltzgen | All-atom protein design using BoltzGen diffusion model for binder design and peptide design. |
| antibody-structure-prediction-tfold | Predict antibody/nanobody structures and antigen-antibody complex structures. |
| antibody-design-iggm | Epitope-conditioned de novo antibody design and affinity maturation. |
| binding-affinity-prediction-prodigy | Predict binding affinity scores for protein complexes using Prodigy. |
| protein-ligand-binding-analysis-plip | Analyze protein-ligand interactions in PDB structures using PLIP (Protein-Ligand Interaction Profiler). |
| protein-function-prediction | Predict protein function and properties from amino acid sequence using BioT5. |
| protein-subcellular-localization-prediction-biot5 | Predict protein subcellular localization from amino acid sequence using BioT5. |
| Skill | Description |
|---|---|
| single-cell-foundation-model-scrna-seq-geneformer | Geneformer workflows for tokenization, cell/gene classification, embedding extraction, and perturbation analysis. |
| single-cell-foundation-model-scrna-seq-langcell | LangCell for zero-shot and few-shot cell type annotation with multimodal cell-text matching. |
| single-cell-foundation-model-scrna-seq-scgpt | scGPT for preprocessing, binning, cell embedding extraction, fine-tuning, and reference mapping. |
| spatial-transcriptomics-foundation-model-stofm | SToFM for spatial transcriptomics preprocessing and cell embedding generation. |
| single-cell-scrna-seq-analysis-scanpy | Complete scRNA-seq analysis workflow with Scanpy including QC, normalization, clustering, and marker gene identification. |
| single-cell-multi-omics-analysis-scvi | Probabilistic deep learning for single-cell multi-omics analysis including scVI, scANVI, totalVI. |
| cellxgene-census-query | Query CZ CELLxGENE Census (61M+ cells) for single-cell expression data. |
| spatial-transcriptomics-spatial-data-io | Load spatial transcriptomics data from Visium, Xenium, MERFISH, Slide-seq, and other platforms. |
| single-cell-atac-seq-qc-processing | Trim adapters, align reads, remove duplicates, and evaluate chromatin accessibility data quality. |
| single-cell-atac-seq-peak-calling-annotaion | Call accessible chromatin peaks with MACS2 and identify differentially accessible regions. |
| single-cell-proteomics-data-processing | Load, inspect, centroid, and extract features from raw LC-MS/MS data files using pyOpenMS. |
| single-cell-proteomics-peptide-identification | Search MS2 spectra against protein databases with MSFragger/Comet. |
| single-cell-multi-omics-data-harmonization | Prepare multi-omics datasets for joint integration with normalization and batch correction. |
| Skill | Description |
|---|---|
| pubchem-query | Query PubChem database for chemical structures, similar compounds, and bioactivity data. |
| biomedical-literature-search | Search PubMed and bioRxiv for biomedical research papers. |
| uniprot-query | Query UniProt database for protein sequences, metadata, and search by criteria. |
| kegg-query | Query KEGG database for drug information, pathway analysis, and disease-drug-target discovery. |
| ppi-string-query | Query STRING database for protein-protein interactions with confidence scores. |
| chembl-query | Query ChEMBL database for bioactivity data on drug-like compounds. |
| Skill | Description |
|---|---|
| iupac-name-identification-biot5 | Identify the IUPAC name of a molecule using BioT5 question answering model. |
| molecule-biochemical-significance-query-biot5 | Query a molecule's biochemical significance and roles in biology and chemistry. |
| Skill | Description |
|---|---|
| disease-drug-intelligence | Comprehensive disease-to-innovative-drug analysis: targets, drugs, clinical progress, and mechanism trends. |
| target-drug-report | Generate comprehensive drug development progress reports for disease therapeutic targets. |
| Skill | Description |
|---|---|
| biomed-skill-creator | Create new biomedical skills or improve existing ones. |
When a user asks for help finding a skill:
User: "I want to design a drug for BCL2 protein"
Agent: Based on your request to design drugs for the BCL2 protein, I recommend:
target-based-lead-design - This skill generates diverse lead compounds for a specific protein target using structure-based drug design. You'll need a PDB ID for BCL2. Would you like me to help you find one?
User: "What can I use to analyze my scRNA-seq data?"
Agent: For scRNA-seq analysis, several skills are available depending on your goal:
single-cell-scrna-seq-analysis-scanpy - Complete analysis workflow (QC, clustering, marker genes)single-cell-foundation-model-scrna-seq-langcell - Cell type annotationsingle-cell-foundation-model-scrna-seq-geneformer - Embedding extraction and perturbation analysissingle-cell-foundation-model-scrna-seq-scgpt - Embedding extraction and reference mappingWhat specific analysis do you want to perform?
User: "I have a molecule and want to check if it's toxic"
Agent: For toxicity analysis, use admet-prediction. This skill predicts comprehensive ADMET properties including toxicity (LD50), side effects, and BBB penetration. Please provide your molecule as a SMILES string.
skills_overview.md - Full skill catalog with detailed descriptionsbiomed-skill-creator - Create new skills when none match your needs