FreedomIntelligence/OpenClaw-Medical-Skills

AAV Vector Design Agent designs adeno-associated virus vectors for gene therapy by selecting serotypes, engineering capsids, optimizing promoters and transgenes, and predicting immunogenicity. Use it when designing tissue-specific AAV vectors, engineering capsid variants, or optimizing expression cassettes.

ADHD Daily Planner provides ADHD-specific daily planning, time estimation, and executive function support. Use it to create realistic schedules and build flexible systems that work with ADHD brains.

ADMET Prediction predicts ADMET properties (absorption, distribution, metabolism, excretion, toxicity) using ADMETlab 3.0 API or DeepChem models, including bioavailability, CYP inhibition, hERG liability, and 119 toxicity endpoints with uncertainty quantification. It filters compounds for PAINS and other structural alerts to assess drug-likeness and prioritize leads by predicted safety.

AI Health Analyzer integrates multi-dimensional health data to identify abnormal patterns, predict health risks, and provide personalized recommendations. Use it for intelligent health Q&A and AI-generated health reports.

Adaptyv is a cloud laboratory platform for automated protein testing and validation. Use it to submit protein sequences for binding assays, expression testing, thermostability measurements, enzyme activity assays, and sequence optimization.

Aeon Time Series ML provides scikit-learn compatible algorithms for time series classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use it when working with temporal data or sequential patterns that require specialized approaches beyond standard ML.

Agent Browser navigates web pages, fills forms, clicks elements, extracts data, and takes screenshots via a command-line tool. Use whenever a browser would be useful, not just when the user explicitly asks.

AgentD Drug Discovery mines literature and databases for known ligands, generates candidate molecules, and ranks them with SAR and ADMET annotations for early drug discovery tasks. It delivers SMILES, property scores, and reproducibility manifests for in silico hypothesis generation.

AlphaFold validates protein designs using AlphaFold2 structure prediction. Use it to predict folding, compute confidence metrics (pLDDT, pTM, ipTM), and validate multi-chain complexes.

AlphaFold Database provides access to over 200 million AI-predicted protein structures from DeepMind and EMBL-EBI. Use it to retrieve predictions by UniProt ID, download PDB/mmCIF files, analyze pLDDT and PAE confidence metrics, and integrate structures into drug discovery or structural biology workflows.

AnnData handles annotated data matrices for single-cell genomics in Python. Use it to read, write, manipulate, and analyze large biological datasets such as h5ad files and single-cell RNA-seq data with metadata.

Antibody Design Agent designs de novo antibodies against viral or tumoral targets using protein language models (MAGE) and RFdiffusion, then validates candidates with AlphaFold3 or ESMFold. Use it when you need binder sequences for a specific antigen or epitope.

Arboreto infers gene regulatory networks from gene expression data using scalable algorithms such as GRNBoost2 and GENIE3. Use it to identify transcription factor–target relationships in bulk or single-cell RNA-seq, with optional distributed computing for large datasets.

Armored CAR-T Design Agent designs armored CAR-T cells with cytokine/chemokine expression for enhanced solid tumor efficacy. It selects armoring payloads like IL-12, IL-15, IL-18, and IL-7, optimizes expression, and integrates safety switches.

Autonomous Oncology Agent combines LLM reasoning with vision transformer analysis of H&E slides to predict biomarkers, retrieve evidence, and generate ranked treatment options. Use it for tumor board style precision oncology decision support.

BAM Statistics generates alignment statistics from BAM files using samtools and pysam. Use it when assessing alignment quality, calculating coverage, or generating QC reports.

BGPT Paper Search searches scientific papers and retrieves structured experimental data extracted from full-text studies via the BGPT MCP server. Use it for literature reviews, evidence synthesis, and finding experimental details not available in abstracts alone.

Bayesian Optimizer proposes the next best experiment parameters to maximize a target metric such as yield, purity, or binding affinity, using Gaussian Processes and Upper Confidence Bound acquisition. Use it when experiments are expensive or time-consuming, or to tune machine learning hyperparameters.

Benchling Integration connects agents to Benchling's R&D platform via the Python SDK and REST API for managing DNA/protein entities, inventory, ELN entries, and workflow automation. Use it when automating lab data management or building Benchling Apps.

BindCraft designs protein binders using end-to-end optimization with built-in AF2 validation. Use it for production-quality binder campaigns with joint backbone and sequence optimization.

Binder Design helps choose between BoltzGen, BindCraft, RFdiffusion, and Germinal for de novo protein binder design, then guides target preparation, hotspot selection, generation, and filtering with validation metrics.

Binding Characterization provides guidance for SPR and BLI binding kinetics experiments, helping researchers plan experiments, troubleshoot poor signal, interpret artifacts, and choose between SPR and BLI platforms.

BindingDB Database queries BindingDB for measured drug-target binding affinities (Ki, Kd, IC50, EC50) by target (UniProt ID), compound (SMILES/name), or pathogen. Use it for drug discovery, lead optimization, polypharmacology analysis, and structure-activity relationship (SAR) studies.

Bio Alignment Filtering filters SAM/BAM alignments by flags, mapping quality, and genomic regions using samtools and pysam. Use it to extract specific reads, remove low-quality or duplicate alignments, or subset to target regions.

arXiv Search searches the complete arXiv database of preprints across physics, mathematics, computer science, and quantitative biology using natural language queries. It's useful for finding preprints before peer review, tracking emerging research trends, or conducting cross-disciplinary searches.