Claude Skills by FreedomIntelligence

--> --- name: bio-workflows-imc-pipeline description: End-to-end imaging mass cytometry workflow from raw acquisitions to spatial cell analysis. Orchestrates image preprocessing, segmentation, phenotyping, and spatial statistics. Use when analyzing imaging mass cytometry data end-to-end. tool_type: python primary_tool: steinbock workflow: true depends_on: - imaging-mass-cytometry/data-preprocessing - imaging-mass-cytometry/cell-segmentation - imaging-mass-cytometry/phenotyping - imaging-mass-...

--> --- name: bio-workflows-longread-sv-pipeline description: End-to-end workflow for detecting structural variants from long-read sequencing data. Covers ONT/PacBio alignment with minimap2 and SV calling with Sniffles or cuteSV. Use when detecting structural variants from long reads. tool_type: cli primary_tool: Sniffles workflow: true depends_on: - long-read-sequencing/long-read-alignment - long-read-sequencing/long-read-qc - long-read-sequencing/structural-variants qc_checkpoints: - after_...

--> --- name: bio-workflows-merip-pipeline description: End-to-end MeRIP-seq analysis from FASTQ to m6A peaks and differential methylation. Use when analyzing epitranscriptomic m6A modifications from immunoprecipitation data. tool_type: mixed primary_tool: exomePeak2 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-workflows-metabolic-modeling-pipeline description: End-to-end genome-scale metabolic modeling from genome sequence to flux predictions. Covers automated reconstruction with CarveMe, model validation with memote, FBA/FVA analysis, and gene essentiality prediction. Use when building metabolic models or predicting metabolic phenotypes from genomic data. tool_type: mixed primary_tool: cobrapy workflow: true depends_on: - systems-biology/metabolic-reconstruction - systems-biology...

--> --- name: bio-workflows-metabolomics-pipeline description: End-to-end metabolomics workflow from raw MS data to pathway analysis. Orchestrates XCMS preprocessing, annotation, normalization, statistical analysis, and pathway mapping. Use when processing LC-MS metabolomics data. tool_type: r primary_tool: XCMS workflow: true depends_on: - metabolomics/xcms-preprocessing - metabolomics/metabolite-annotation - metabolomics/normalization-qc - metabolomics/statistical-analysis - metabolomics/pa...

--> --- name: bio-workflows-metagenomics-pipeline description: End-to-end metagenomics workflow from FASTQ to taxonomic and functional profiles. Covers Kraken2 classification, Bracken abundance estimation, and HUMAnN functional profiling. Use when profiling metagenomic samples. tool_type: cli primary_tool: Kraken2 workflow: true depends_on: - read-qc/fastp-workflow - metagenomics/kraken-classification - metagenomics/metaphlan-profiling - metagenomics/abundance-estimation - metagenomics/functi...

--> --- name: bio-workflows-methylation-pipeline description: End-to-end bisulfite sequencing workflow from FASTQ to differentially methylated regions. Covers Bismark alignment, methylation calling, and DMR detection with methylKit. Use when analyzing bisulfite sequencing data. tool_type: mixed primary_tool: Bismark workflow: true depends_on: - read-qc/fastp-workflow - methylation-analysis/bismark-alignment - methylation-analysis/methylation-calling - methylation-analysis/methylkit-analysis -...

--> --- name: bio-workflows-microbiome-pipeline description: End-to-end 16S amplicon workflow from FASTQ reads to differential abundance. Orchestrates DADA2 ASV inference, taxonomy assignment, diversity analysis, and compositional testing with ALDEx2. Use when processing 16S/ITS amplicon data. tool_type: r primary_tool: dada2 workflow: true depends_on: - microbiome/amplicon-processing - microbiome/taxonomy-assignment - microbiome/diversity-analysis - microbiome/differential-abundance measurab...

--> --- name: bio-workflows-multi-omics-pipeline description: End-to-end multi-omics integration workflow. Orchestrates data harmonization, MOFA/mixOmics integration, factor interpretation, and downstream analysis across transcriptomics, proteomics, metabolomics, and other modalities. Use when integrating multiple omics datasets. tool_type: r primary_tool: MOFA2 workflow: true depends_on: - multi-omics-integration/data-harmonization - multi-omics-integration/mofa-integration - multi-omics-int...

--> --- name: bio-workflows-multiome-pipeline description: End-to-end multiome workflow for joint scRNA-seq + scATAC-seq analysis. Covers data loading, separate modality processing, and WNN integration with Seurat/Signac. Use when analyzing joint scRNA+scATAC data. tool_type: r primary_tool: Seurat workflow: true depends_on: - single-cell/data-io - single-cell/preprocessing - single-cell/clustering - single-cell/multimodal-integration - single-cell/scatac-analysis qc_checkpoints: - after_load...

--> --- name: bio-workflows-neoantigen-pipeline description: End-to-end neoantigen discovery from somatic variants to ranked vaccine candidates. Integrates HLA typing, MHC binding prediction, pVACtools neoantigen calling, and immunogenicity scoring. Use when identifying tumor neoantigens for personalized vaccine design or checkpoint biomarkers. tool_type: mixed primary_tool: pVACtools workflow: true depends_on: - clinical-databases/hla-typing - immunoinformatics/mhc-binding-prediction - immun...

--> --- name: bio-workflows-outbreak-pipeline description: End-to-end outbreak investigation from pathogen isolates to transmission networks. Orchestrates MLST typing, AMR surveillance, phylodynamic dating, and transmission inference with TransPhylo. Use when investigating disease outbreaks or tracking pathogen transmission chains. tool_type: mixed primary_tool: mlst workflow: true depends_on: - epidemiological-genomics/pathogen-typing - epidemiological-genomics/amr-surveillance - epidemiolog...

--> --- name: bio-workflows-proteomics-pipeline description: End-to-end proteomics workflow from MaxQuant output to differential protein abundance. Orchestrates data import, normalization, imputation, and statistical testing with MSstats or limma. Use when processing mass spectrometry proteomics. tool_type: mixed primary_tool: MSstats workflow: true depends_on: - proteomics/data-import - proteomics/proteomics-qc - proteomics/quantification - proteomics/protein-inference - proteomics/different...

--> --- name: bio-workflows-riboseq-pipeline description: End-to-end Ribo-seq analysis from FASTQ to translation efficiency and ORF detection. Use when analyzing ribosome profiling data to study translation. tool_type: mixed primary_tool: Plastid measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-workflows-rnaseq-to-de description: End-to-end RNA-seq workflow from FASTQ files to differential expression results. Covers QC, quantification (Salmon or STAR+featureCounts), and DESeq2 analysis with visualization. Use when running RNA-seq from FASTQ to DE results. tool_type: mixed primary_tool: DESeq2 workflow: true depends_on: - read-qc/fastp-workflow - rna-quantification/alignment-free-quant - rna-quantification/tximport-workflow - differential-expression/deseq2-basics - ...

--> --- name: bio-workflows-scrnaseq-pipeline description: End-to-end single-cell RNA-seq workflow from 10X Genomics data to annotated cell types. Covers QC, normalization, clustering, marker detection, and cell type annotation. Use when analyzing single-cell RNA-seq data. tool_type: mixed primary_tool: Seurat workflow: true depends_on: - single-cell/data-io - single-cell/preprocessing - single-cell/doublet-detection - single-cell/clustering - single-cell/markers-annotation qc_checkpoints: - ...

--> --- name: bio-workflows-smrna-pipeline description: End-to-end small RNA-seq analysis from FASTQ to differential miRNA expression. Use when analyzing miRNA, piRNA, or other small RNA sequencing data. tool_type: mixed primary_tool: miRDeep2 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-workflows-somatic-variant-pipeline description: End-to-end somatic variant calling from tumor-normal paired samples using Mutect2 or Strelka2. Covers preprocessing, variant calling, filtering, and annotation for cancer genomics. Use when calling somatic mutations from tumor-normal pairs. tool_type: cli primary_tool: GATK Mutect2 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Comp...

--> --- name: bio-workflows-spatial-pipeline description: End-to-end spatial transcriptomics workflow for Visium/Xenium data. Covers data loading, preprocessing, spatial analysis, domain detection, and visualization with Squidpy. Use when analyzing spatial transcriptomics data. tool_type: python primary_tool: Squidpy workflow: true depends_on: - spatial-transcriptomics/spatial-data-io - spatial-transcriptomics/spatial-preprocessing - spatial-transcriptomics/spatial-neighbors - spatial-transcr...

--> --- name: bio-workflows-tcr-pipeline description: End-to-end TCR/BCR repertoire analysis from FASTQ to clonotype diversity metrics. Use when analyzing immune repertoire sequencing data from bulk or single-cell experiments. tool_type: cli primary_tool: MiXCR measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

Write biological sequences to files (FASTA, FASTQ, GenBank, EMBL) using Biopython Bio.SeqIO. Use when saving sequences, creating new sequence files, or outputting modified records.

--> --- name: bioinformatics-singlecell description: "Advanced single-cell multi-omics analysis including scRNA-seq, scCITE-seq, scATAC-seq, and TARGET-seq. Use when analyzing single-cell data, cell type identification, trajectory analysis, differential expression, UMAP/clustering, integrating protein and RNA modalities (TotalVI), or working with Scanpy, Seurat, scvi-tools. Includes workflows for MPN, hematologic malignancies, megakaryocyte biology." license: Proprietary ---

--> --- name: biokernel description: Biomedical OS Core & MCP Server keywords: - kernel - os - mcp - api - system measurable_outcome: Routes 100% of API requests to correct sub-agent with <200ms latency. license: MIT metadata: author: Biomedical OS Team version: "1.0.0" compatibility: - system: Python 3.10+ allowed-tools: - run_shell_command - read_file --- The BioKernel is the central orchestration layer of the Biomedical OS, managing context, routing tasks to specialized agents via MCP (Mod...

--> --- name: biomaster-workflows description: Pipeline maestro keywords: - workflows - RNAseq - ChIPseq - automation - YAML measurable_outcome: Execute a configured pipeline end-to-end (including QC report + summary) within 24 hours of receiving inputs, logging every tool/parameter. license: MIT metadata: author: BioMaster Team version: "1.0.0" compatibility: - system: Python 3.9+ allowed-tools: - run_shell_command - read_file --- Orchestrate BioMaster’s multi-agent pipelines (RNA-seq, ChIP-...

--> --- name: biomcp-server description: MCP bio bridge keywords: - MCP - PubMed - ClinicalTrials - server - uv measurable_outcome: Stand up a working BioMCP endpoint (pip or uv) and return ≥1 PubMed + ≥1 ClinicalTrials.gov response to the client within 10 minutes. license: MIT metadata: author: BioMCP Team version: "1.0.0" compatibility: - system: MCP-compliant clients allowed-tools: - web_fetch --- Deploy and operate the BioMCP server so MCP-compatible clients (Claude Desktop, LobeChat, etc...

--> --- name: biomedical-data-analysis description: Omics data forge keywords: - pandas - R-tidyverse - SQL - visualization - reproducible measurable_outcome: Deliver a cleaned dataset + statistical summary + at least one visualization or dashboard spec for each request within 1 working session (≤30 minutes). license: MIT metadata: author: BioSkills Team version: "1.0.0" compatibility: - system: Python 3.9+ / R 4.0+ allowed-tools: - run_shell_command - read_file - python_repl --- Run the cros...

Complete biomedical information search combining PubMed, preprints, clinical trials, and FDA drug labels. Powered by Valyu semantic search.

--> --- name: biomni-general-agent description: Use the local Biomni checkout to orchestrate its 150+ biomedical tools, databases, and know-how workflows for complex research questions. allowed-tools: - read_file - run_shell_command ---

--> --- name: biomni-research-agent description: Bio-Research Generalist license: MIT metadata: author: Stanford (Snap Lab) source: "https://github.com/snap-stanford/Biomni" version: "1.0.0" compatibility: - system: Python 3.9+ allowed-tools: - run_shell_command - web_fetch - python_repl keywords: - biomni - automation - biomedical - reasoning - tools measurable_outcome: Execute complex research tasks with >95% success rate and validated tool usage. --- A general-purpose biomedical AI agent c...

--> --- name: 'bone-marrow-ai-agent' description: 'AI-powered bone marrow morphology analysis, cell classification, and hematologic disorder diagnosis using deep learning on aspirate and biopsy images.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- The **Bone Marrow AI Agent** provides comprehensive AI-driven analysis of bone marrow aspirate and biopsy specimens. It performs automated cell identi...

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

Use omicverse's pyComBat wrapper to remove batch effects from merged bulk RNA-seq or microarray cohorts, export corrected matrices, and benchmark pre/post correction visualisations.

Guide Claude through omicverse's bulk RNA-seq DEG pipeline, from gene ID mapping and DESeq2 normalization to statistical testing, visualization, and pathway enrichment. Use when a user has bulk count matrices and needs differential expression analysis in omicverse.

Walk Claude through PyDESeq2-based differential expression, including ID mapping, DE testing, fold-change thresholding, and enrichment visualisation.

Help Claude query STRING for protein interactions, build PPI graphs with pyPPI, and render styled network figures for bulk gene lists.

Turn bulk RNA-seq cohorts into synthetic single-cell datasets using omicverse's Bulk2Single workflow for cell fraction estimation, beta-VAE generation, and quality control comparisons against reference scRNA-seq.

Extend scRNA-seq developmental trajectories with BulkTrajBlend by generating intermediate cells from bulk RNA-seq, training beta-VAE and GNN models, and interpolating missing states.

Assist Claude in running PyWGCNA through omicverse—preprocessing expression matrices, constructing co-expression modules, visualising eigengenes, and extracting hub genes.

--> --- name: 'cancer-metabolism-agent' description: 'AI-powered analysis of cancer metabolic reprogramming including Warburg effect, glutamine addiction, lipid metabolism, and metabolic vulnerabilities for therapeutic targeting.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- The **Cancer Metabolism Agent** analyzes tumor metabolic reprogramming to identify vulnerabilities for therapeutic targeti...

--> --- name: 'care-coordination' description: 'Care Coordination agent for healthcare workflows.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- This skill implements the Care Coordination workflow using Anthropic's Claude.

--> --- name: 'cart-design-optimizer-agent' description: 'AI-guided CAR-T cell design for solid tumors using antigen prioritization, safety-by-design architectures, and exhaustion-resistant engineering.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- The **CAR-T Design Optimizer Agent** provides end-to-end AI-guided design of chimeric antigen receptor T-cells. It integrates antigen prioritization,...

Query cBioPortal for cancer genomics data including somatic mutations, copy number alterations, gene expression, and survival data across hundreds of cancer studies. Essential for cancer target validation, oncogene/tumor suppressor analysis, and patient-level genomic profiling.

--> --- name: cellagent-annotation description: Cell tagger keywords: - single-cell - markers - annotation - confidence - tissue measurable_outcome: Label every provided cluster with a cell type + confidence + marker evidence (or "ambiguous") within 15 minutes per dataset. license: MIT metadata: author: CellAgent Team version: "1.0.0" compatibility: - system: Python 3.9+ allowed-tools: - run_shell_command - read_file --- Use CellTypeAgent to interpret marker genes, annotate scRNA-seq clusters...

--> --- name: 'cellfree-rna-agent' description: 'AI-powered cell-free RNA analysis from liquid biopsy for cancer detection, tissue-of-origin identification, and non-invasive transcriptomic profiling.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- The **Cell-Free RNA Analysis Agent** provides comprehensive analysis of circulating cell-free RNA (cfRNA) from plasma and other biofluids for cancer det...

--> --- name: 'cellular-senescence-agent' description: 'AI-powered analysis of cellular senescence for aging research, cancer therapy response, and senolytic drug development.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- The **Cellular Senescence Agent** provides comprehensive AI-driven analysis of cellular senescence signatures for aging research, cancer biology, and senolytic therapeutic deve...

--> --- name: chatehr-clinician-assistant description: EHR Chat Assistant keywords: - EHR - clinical - summarization - patient-records - FHIR measurable_outcome: Answer 5 clinical queries and generate a discharge summary from a patient record with <10s latency. license: Apache-2.0 metadata: author: Stanford Medicine version: "1.0.0" compatibility: - system: Python 3.10+ allowed-tools: - run_shell_command - read_file --- AI software for clinicians to interact with patient medical records via n...

--> --- name: chematagent-drug-discovery description: Chemical Lab Agent keywords: - chemistry - drug-discovery - tools - synthesis - property-prediction measurable_outcome: Plan a synthesis route and predict ADMET properties for a candidate molecule with >80% validity. license: MIT metadata: author: CheMatAgent Team version: "1.0.0" compatibility: - system: Python 3.9+ allowed-tools: - run_shell_command - read_file --- A two-tiered agent system with access to 137 Python-wrapped chemical tool...

Search ChEMBL bioactive molecules database with natural language queries. Find compounds and assay data with Valyu semantic search.

--> --- name: 'chemcrow-drug-discovery' description: 'An LLM chemistry agent with expert-designed tools for organic synthesis, drug discovery, and materials design.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- ChemCrow is an open-source package for the accurate integration of Large Language Models (LLMs) with chemistry tools. It is designed to autonomously plan and execute chemical syntheses, r...

--> --- name: chemical-property-lookup description: Compute RDKit-driven molecular properties (MW, logP, TPSA, QED, Lipinski) for a SMILES string to support downstream drug discovery tools. allowed-tools: - read_file - run_shell_command ---