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Topic: skills

17,247 skills in this topic.

bio-flow-cytometry-doublet-detection Detect and remove doublets from flow and mass cytometry data. Covers FSC/SSC gating and computational doublet detection methods. Use when filtering out cell aggregates before clustering or quantitative analysis.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-cfdna-preprocessing Preprocesses cell-free DNA sequencing data including adapter trimming, alignment optimized for short fragments, and UMI-aware duplicate removal using fgbio. Applies cfDNA-specific quality thresholds and fragment length filtering. Use when processing plasma cfDNA sequencing data before downstream analysis.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bayesian-optimizer
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-causal-genomics-mendelian-randomization Estimate causal effects between exposures and outcomes using genetic variants as instrumental variables with TwoSampleMR. Implements IVW, MR-Egger, weighted median, and MR-PRESSO methods for robust causal inference from GWAS summary statistics. Use when testing whether an exposure causally affects an outcome using genetic instruments.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-metagenomics-abundance Species abundance estimation using Bracken with Kraken2 output. Redistributes reads from higher taxonomic levels to species for more accurate estimates. Use when accurate species-level abundances are needed from Kraken2 classification output.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-population-genetics-scikit-allel-analysis
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-spatial-transcriptomics-spatial-proteomics Analyzes spatial proteomics data from CODEX, IMC, and MIBI platforms including cell segmentation and protein colocalization. Use when working with multiplexed imaging data, analyzing protein spatial patterns, or integrating spatial proteomics with transcriptomics.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-spatial-transcriptomics-spatial-preprocessing Quality control, filtering, normalization, and feature selection for spatial transcriptomics data. Calculate QC metrics, filter spots/cells, normalize counts, and identify highly variable genes. Use when filtering and normalizing spatial transcriptomics data.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-genome-engineering-off-target-prediction Predict CRISPR off-target sites using Cas-OFFinder and CFD scoring algorithms. Identify potential unintended cleavage sites genome-wide and assess guide specificity. Use when evaluating guide RNA specificity or selecting guides with minimal off-target risk.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-compressed-files Read and write compressed sequence files (gzip, bzip2, BGZF) using Biopython. Use when working with .gz or .bz2 sequence files. Use BGZF for indexable compressed files.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-alignment-msa-parsing Parse and analyze multiple sequence alignments using Biopython. Extract sequences, identify conserved regions, analyze gaps, work with annotations, and manipulate alignment data for downstream analysis. Use when parsing or manipulating multiple sequence alignments.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
binding-characterization Guidance for SPR and BLI binding characterization experiments. Use when: (1) Planning binding kinetics experiments, (2) Troubleshooting poor/no binding signal, (3) Interpreting kinetic data artifacts, (4) Choosing between SPR vs BLI platforms.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
aeon 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.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-hi-c-analysis-loop-calling Detect chromatin loops and point interactions from Hi-C data using cooltools, chromosight, and HiCCUPS-like methods. Identify CTCF-mediated loops and enhancer-promoter contacts. Use when detecting chromatin loops from Hi-C data.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-longread-alignment Align long reads using minimap2 for Oxford Nanopore and PacBio data. Supports various presets for different read types and applications. Use when aligning ONT or PacBio reads to a reference genome for variant calling, SV detection, or coverage analysis.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-sequence-slicing
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-restriction-fragment-analysis
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-comparative-genomics-positive-selection
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-clip-seq-clip-motif-analysis
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-long-read-sequencing-nanopore-methylation Calls DNA methylation from Oxford Nanopore sequencing data using signal-level analysis. Use when detecting 5mC or 6mA modifications directly from nanopore reads without bisulfite conversion.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-imaging-mass-cytometry-interactive-annotation Interactive cell type annotation for IMC data. Covers napari-based annotation, marker-guided labeling, training data generation, and annotation validation. Use when manually annotating cell types for training classifiers or validating automated phenotyping results.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-spatial-transcriptomics-image-analysis Process and analyze tissue images from spatial transcriptomics data using Squidpy. Extract image features, segment cells/nuclei, and compute morphological features from H&E or IF images. Use when processing tissue images for spatial transcriptomics.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-microbiome-diversity-analysis Alpha and beta diversity analysis for microbiome data. Calculate within-sample richness, evenness, and between-sample dissimilarity with phyloseq and vegan. Use when comparing community composition across samples or testing for group differences in microbiome structure.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
bio-admet-prediction Predicts ADMET properties using ADMETlab 3.0 API or DeepChem models. Estimates bioavailability, CYP inhibition, hERG liability, and 119 toxicity endpoints with uncertainty quantification. Filters for PAINS and other structural alerts. Use when filtering compounds for drug-likeness or prioritizing leads by predicted safety.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009