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Topic: skills

17,247 skills in this topic.

bio-machine-learning-prediction-explanation
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-clinical-databases-dbsnp-queries Query dbSNP for rsID lookups, variant annotations, and cross-references to other databases. Use when mapping between rsIDs and genomic coordinates or retrieving basic variant information.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-pdb-structure-navigation Navigate protein structure hierarchy using Biopython Bio.PDB SMCRA model. Use when accessing models, chains, residues, and atoms, iterating over structure levels, or extracting sequences from PDB files.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-machine-learning-model-validation
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-imaging-mass-cytometry-data-preprocessing Load and preprocess imaging mass cytometry (IMC) and MIBI data. Covers MCD/TIFF handling, hot pixel removal, and image normalization. Use when starting IMC analysis from raw MCD files or preparing images for segmentation.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-genome-assembly-hifi-assembly
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-hi-c-analysis-hic-differential Compare Hi-C contact matrices between conditions to identify differential chromatin interactions. Compute log2 fold changes, statistical significance, and visualize differential contact maps. Use when comparing Hi-C contacts between conditions.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-metagenomics-visualization Visualize metagenomic profiles using R (phyloseq, microbiome) and Python (matplotlib, seaborn). Create stacked bar plots, heatmaps, PCA plots, and diversity analyses. Use when creating publication-quality figures from MetaPhlAn, Bracken, or other taxonomic profiling output.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-basecalling Convert raw Nanopore signal data (FAST5/POD5) to nucleotide sequences using Dorado basecaller. Covers model selection, GPU acceleration, modified base detection, and quality filtering. Use when processing raw Nanopore data before alignment. Guppy is deprecated; use Dorado for all new analyses.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
autonomous-oncology-agent
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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
bio-longread-structural-variants Detect structural variants from long-read alignments using Sniffles, cuteSV, and SVIM. Use when detecting deletions, insertions, inversions, translocations, or complex rearrangements from ONT or PacBio data, especially those missed by short-read methods.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-experimental-design-power-analysis
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
agent-browser Browse the web for any task — research topics, read articles, interact with web apps, fill forms, take screenshots, extract data, and test web pages. Use whenever a browser would be useful, not just when the user explicitly asks.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-differential-splicing Detects differential alternative splicing between conditions using rMATS-turbo (BAM-based) or SUPPA2 diffSplice (TPM-based). Reports events with FDR-corrected significance and delta PSI effect sizes. Use when comparing splicing patterns between treatment groups, tissues, or disease states.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-metabolomics-xcms-preprocessing XCMS3 workflow for LC-MS/MS metabolomics preprocessing. Covers peak detection, retention time alignment, correspondence (grouping), and gap filling. Use when processing raw LC-MS data into a feature table for untargeted metabolomics.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-alignment-pairwise Perform pairwise sequence alignment using Biopython Bio.Align.PairwiseAligner. Use when comparing two sequences, finding optimal alignments, scoring similarity, and identifying local or global matches between DNA, RNA, or protein sequences.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-copy-number-gatk-cnv Call copy number variants using GATK best practices workflow. Supports both somatic (tumor-normal) and germline CNV detection from WGS or WES data. Use when following GATK best practices or integrating CNV calling with other GATK variant pipelines.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-atac-seq-nucleosome-positioning Extract nucleosome positions from ATAC-seq data using NucleoATAC, ATACseqQC, and fragment analysis. Use when analyzing chromatin organization, identifying nucleosome-free regions at promoters, or characterizing nucleosome occupancy patterns from ATAC-seq fragment size distributions.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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
bio-clinical-databases-somatic-signatures Extract and analyze mutational signatures from somatic variants using SigProfiler or MutationalPatterns to characterize mutagenic processes. Use when identifying DNA damage mechanisms or etiology in cancer genomes.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-multi-omics-data-harmonization Preprocessing and harmonization of multi-omics data before integration. Covers normalization, batch correction, feature alignment, and missing value handling across data types. Use when preparing multi-omics datasets for integration analysis.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-data-visualization-upset-plots
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-metabolomics-statistical-analysis Statistical analysis for metabolomics data. Covers univariate testing, multivariate methods (PCA, PLS-DA), and biomarker discovery. Use when identifying differentially abundant metabolites or building classification models.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009