Topic: awesome
1,258 skills in this topic.
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bio-alignment-validation
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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bio-machine-learning-model-validation
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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zarr-python
Chunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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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
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drug-photo
Medication photo to personalised PGx dosage card via Claude vision — snap a pill, get genotype-informed guidance
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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bio-restriction-mapping
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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bio-spatial-transcriptomics-spatial-multiomics
Analyze high-resolution spatial platforms like Slide-seq, Stereo-seq, and Visium HD. Use when working with subcellular resolution or high-density spatial data.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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bio-vcf-statistics
Generate variant statistics, sample concordance, and quality metrics using bcftools stats and gtcheck. Use when evaluating variant quality, comparing samples, or summarizing VCF contents.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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bio-data-visualization-color-palettes
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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bio-microbiome-functional-prediction
Predict metagenome functional content from 16S rRNA marker gene data using PICRUSt2. Infer KEGG, MetaCyc, and EC abundances from ASV tables. Use when functional profiling is needed from 16S data without shotgun metagenomics sequencing.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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bio-workflows-somatic-variant-pipeline
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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medical-imaging-review
Write comprehensive literature reviews for medical imaging AI research. Use when writing survey papers, systematic reviews, or literature analyses on topics like segmentation, detection, classification in CT, MRI, X-ray, ultrasound, or pathology imaging. Triggers on requests for "review paper", "survey", "literature review", "综述", "systematic review", or mentions of writing academic reviews on deep learning for medical imaging.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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autonomous-oncology-agent
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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hmdb-database
Access Human Metabolome Database (220K+ metabolites). Search by name/ID/structure, retrieve chemical properties, biomarker data, NMR/MS spectra, pathways, for metabolomics and identification.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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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
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tooluniverse-rare-disease-diagnosis
Provide differential diagnosis for patients with suspected rare diseases based on phenotype and genetic data. Matches symptoms to HPO terms, identifies candidate diseases from Orphanet/OMIM, prioritizes genes for testing, interprets variants of uncertain significance. Use when clinician asks about rare disease diagnosis, unexplained phenotypes, or genetic testing interpretation.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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tooluniverse-crispr-screen-analysis
Comprehensive CRISPR screen analysis for functional genomics. Analyze pooled or arrayed CRISPR screens (knockout, activation, interference) to identify essential genes, synthetic lethal interactions, and drug targets. Perform sgRNA count processing, gene-level scoring (MAGeCK, BAGEL), quality control, pathway enrichment, and drug target prioritization. Use for CRISPR screen analysis, gene essentiality studies, synthetic lethality detection, functional genomics, drug target validation, or identifying genetic vulnerabilities.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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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
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ai-analyzer
AI驱动的综合健康分析系统，整合多维度健康数据、识别异常模式、预测健康风险、提供个性化建议。支持智能问答和AI健康报告生成。
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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data-viz-plots
Create publication-quality plots and visualizations using matplotlib and seaborn. Works with ANY LLM provider (GPT, Gemini, Claude, etc.).
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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tcr-repertoire-analysis-agent
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-hi-c-analysis-hic-visualization
Visualize Hi-C contact matrices, TADs, loops, and genomic features using matplotlib, cooltools, and HiCExplorer. Create triangle plots, virtual 4C, and multi-track figures. Use when visualizing contact matrices or genomic features.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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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