Topic: openclaw
3,425 skills in this topic.
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bio-alignment-validation
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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bio-pdb-structure-modification
Modify protein structures using Biopython Bio.PDB. Use when transforming coordinates, removing atoms or residues, adding new entities, modifying B-factors and occupancies, or building structures programmatically.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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bio-flow-cytometry-differential-analysis
Differential abundance and state analysis for cytometry data. Compare cell populations between conditions using statistical methods. Use when testing for significant changes in cell frequencies or marker expression between groups.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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uniprot-database
Direct REST API access to UniProt. Protein searches, FASTA retrieval, ID mapping, Swiss-Prot/TrEMBL. For Python workflows with multiple databases, prefer bioservices (unified interface to 40+ services). Use this for direct HTTP/REST work or UniProt-specific control.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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bio-proteomics-protein-inference
Protein grouping and inference from peptide identifications. Use when resolving protein ambiguity from shared peptides. Handles protein groups and protein-level FDR control using parsimony and probabilistic approaches.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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single-cell-clustering-and-batch-correction-with-omicverse
Guide Claude through omicverse's single-cell clustering workflow, covering preprocessing, QC, multimethod clustering, topic modeling, cNMF, and cross-batch integration as demonstrated in t_cluster.ipynb and t_single_batch.ipynb.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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tooluniverse-clinical-trial-matching
AI-driven patient-to-trial matching for precision medicine and oncology. Given a patient profile (disease, molecular alterations, stage, prior treatments), discovers and ranks clinical trials from ClinicalTrials.gov using multi-dimensional matching across molecular eligibility, clinical criteria, drug-biomarker alignment, evidence strength, and geographic feasibility. Produces a quantitative Trial Match Score (0-100) per trial with tiered recommendations and a comprehensive markdown report. Use when oncologists, molecular tumor boards, or patients ask about clinical trial options for specific cancer types, biomarker profiles, or post-progression scenarios.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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bio-microbiome-taxonomy-assignment
Taxonomic classification of ASVs using reference databases like SILVA, GTDB, or UNITE. Covers naive Bayes classifiers (DADA2, IDTAXA) and exact matching approaches. Use when assigning taxonomy to ASVs after DADA2 amplicon processing.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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genome-compare
Compare your genome to George Church (PGP-1) and estimate ancestry composition via IBS and EM admixture
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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clinical-trials-search
Search ClinicalTrials.gov with natural language queries. Find clinical trials, enrollment, and outcomes using Valyu semantic search.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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bio-chipseq-differential-binding
Differential binding analysis using DiffBind. Compare ChIP-seq peaks between conditions with statistical rigor. Requires replicate samples. Outputs differentially bound regions with fold changes and p-values. Use when comparing ChIP-seq binding between conditions.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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bio-spatial-transcriptomics-spatial-statistics
Compute spatial statistics for spatial transcriptomics data using Squidpy. Calculate Moran's I, Geary's C, spatial autocorrelation, co-occurrence analysis, and neighborhood enrichment. Use when computing spatial autocorrelation or co-occurrence statistics.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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bio-multi-omics-similarity-network
Similarity Network Fusion (SNF) for patient stratification using multi-omics data. Integrates multiple data types into a unified patient similarity network. Use when performing patient stratification or integrating multi-omics data into unified similarity networks.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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cellular-senescence-agent
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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bio-atac-seq-atac-qc
Quality control metrics for ATAC-seq data including fragment size distribution, TSS enrichment, FRiP, and library complexity. Use when assessing ATAC-seq library quality before or after peak calling to identify problematic samples.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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bio-entrez-search
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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bio-workflows-metagenomics-pipeline
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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bio-expression-matrix-gene-id-mapping
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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bio-genome-engineering-base-editing-design
Design guides for cytosine and adenine base editing using editing window optimization and BE-Hive outcome prediction. Select optimal positions for C-to-T or A-to-G conversions without double-strand breaks. Use when designing base editor experiments for precise nucleotide changes.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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bio-data-visualization-heatmaps-clustering
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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bio-single-cell-doublet-detection
Detect and remove doublets (multiple cells captured in one droplet) from single-cell RNA-seq data. Uses Scrublet (Python), DoubletFinder (R), and scDblFinder (R). Essential QC step before clustering to avoid artificial cell populations. Use when identifying and removing doublets from scRNA-seq data.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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bio-comparative-genomics-hgt-detection
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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bio-genome-assembly-assembly-polishing
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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bio-population-genetics-linkage-disequilibrium
FreedomIntelligence/OpenClaw-Medical-Skills 2,009