Agent skills
Skills you can use with AI coding agents, indexed from public GitHub repositories.
-
bio-expression-matrix-sparse-handling
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
-
bio-entrez-link
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
-
drugbank-database
Access and analyze comprehensive drug information from the DrugBank database including drug properties, interactions, targets, pathways, chemical structures, and pharmacology data. This skill should be used when working with pharmaceutical data, drug discovery research, pharmacology studies, drug-drug interaction analysis, target identification, chemical similarity searches, ADMET predictions, or any task requiring detailed drug and drug target information from DrugBank.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
-
occupational-health-analyzer
分析职业健康数据、识别工作相关健康风险、评估职业健康状况、提供个性化职业健康建议。支持与睡眠、运动、心理健康等其他健康数据的关联分析。
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
-
spatial-epigenomics-agent
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
-
exploratory-data-analysis
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
-
doc-coauthoring
Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
-
hipaa-compliance
Ensure HIPAA compliance when handling PHI (Protected Health Information). Use when writing code that accesses user health data, check-ins, journal entries, or any sensitive information. Activates for audit logging, data access, security events, and compliance questions.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
-
bio-crispr-screens-library-design
CRISPR library design for genetic screens. Covers sgRNA selection, library composition, control design, and oligo ordering. Use when designing custom sgRNA libraries for knockout, activation, or interference screens.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
-
bio-proteomics-differential-abundance
Statistical testing for differentially abundant proteins between conditions. Covers limma and MSstats workflows with multiple testing correction. Use when identifying proteins with significant abundance changes between experimental groups.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
-
statsmodels
Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric analysis.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
-
single-cell-multi-omics-integration
Quick-reference sheet for OmicVerse tutorials spanning MOFA, GLUE pairing, SIMBA integration, TOSICA transfer, and StaVIA cartography.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
-
pyzotero
Interact with Zotero reference management libraries using the pyzotero Python client. Retrieve, create, update, and delete items, collections, tags, and attachments via the Zotero Web API v3. Use this skill when working with Zotero libraries programmatically, managing bibliographic references, exporting citations, searching library contents, uploading PDF attachments, or building research automation workflows that integrate with Zotero.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
-
cbioportal-database
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.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
-
torch-geometric
Graph Neural Networks (PyG). Node/graph classification, link prediction, GCN, GAT, GraphSAGE, heterogeneous graphs, molecular property prediction, for geometric deep learning.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
-
bio-tcr-bcr-analysis-mixcr-analysis
Perform V(D)J alignment and clonotype assembly from TCR-seq or BCR-seq data using MiXCR. Use when processing raw immune repertoire sequencing data to identify clonotypes and their frequencies.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
-
tooluniverse-structural-variant-analysis
Comprehensive structural variant (SV) analysis skill for clinical genomics. Classifies SVs (deletions, duplications, inversions, translocations), assesses pathogenicity using ACMG-adapted criteria, evaluates gene disruption and dosage sensitivity, and provides clinical interpretation with evidence grading. Use when analyzing CNVs, large deletions/duplications, chromosomal rearrangements, or any structural variants requiring clinical interpretation.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
-
bio-genome-intervals-proximity-operations
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
-
biomni-research-agent
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
-
bio-longread-qc
Quality control for long-read sequencing data using NanoPlot, NanoStat, and chopper. Generate QC reports, filter reads by length and quality, and visualize read characteristics. Use when assessing ONT or PacBio run quality or filtering reads before assembly or alignment.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
-
mpn-research-assistant
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
-
bio-proteomics-quantification
Protein quantification from mass spectrometry data including label-free (LFQ, intensity-based), isobaric labeling (TMT, iTRAQ), and metabolic labeling (SILAC) approaches. Use when extracting protein abundances from MS data for differential analysis.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
-
bio-phasing-imputation-genotype-imputation
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
-
bio-imaging-mass-cytometry-spatial-analysis
Spatial analysis of cell neighborhoods and interactions in IMC data. Covers neighbor graphs, spatial statistics, and interaction testing. Use when analyzing spatial relationships between cell types, testing for neighborhood enrichment, or identifying cell-cell interaction patterns in imaging mass cytometry data.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009