Retrieve and analyze biological sequences -- gene/protein sequences from NCBI, Ensembl, and UniProt. Search nucleotide databases, fetch by accession, find orthologs, get gene summaries. Use when users ask about DNA/RNA/protein sequences, gene lookups, ortholog searches, or sequence retrieval.
Analyze post-translational modifications (PTMs) of proteins — modification sites, types, proteoforms, functional effects at PTM sites, and PTM-dependent protein interactions. Integrates iPTMnet, ProtVar, UniProt, and STRING databases. Use when asked about protein phosphorylation, ubiquitination, acetylation, glycosylation, methylation, SUMOylation, or other PTMs; proteoform diversity; PTM-regulated interactions; or functional impact of PTM sites.
Comprehensive patient stratification for precision medicine by integrating genomic, clinical, and therapeutic data. Given a disease/condition, genomic data (germline variants, somatic mutations, expression), and optional clinical parameters, performs multi-phase analysis covering disease disambiguation, genetic risk assessment, disease-specific molecular stratification, pharmacogenomic profiling, comorbidity/DDI risk, pathway analysis, clinical evidence and guideline mapping, clinical trial matching, and integrated outcome prediction. Generates a quantitative Precision Medicine Risk Score (0-100) with risk tier assignment, treatment algorithm, pharmacogenomic guidance, clinical trial matches, and monitoring plan.
Analyze metabolomics data including metabolite identification, quantification, pathway analysis, and metabolic flux. Processes LC-MS, GC-MS, NMR data from targeted and untargeted experiments. Performs normalization, statistical analysis, pathway enrichment, metabolite-enzyme integration, and biomarker discovery. Use when analyzing metabolomics datasets, identifying differential metabolites, studying metabolic pathways, integrating with transcriptomics/proteomics, discovering metabolic biomarkers, performing flux balance analysis, or characterizing metabolic phenotypes in disease, drug response, or physiological conditions.
Inorganic chemistry, physical chemistry, and materials science — crystal structures, coordination chemistry, bonding theory (covalency, orbital mixing), symmetry/point groups, thermodynamics, kinetics, spectroscopy interpretation, noble gas compounds, lanthanide/actinide chemistry. Use for questions about crystal systems, unit cells, density calculations, metal complexes, solid-state chemistry, or physical chemistry calculations.
Neuroscience research and reasoning workflows using ToolUniverse tools. Covers computational neuroscience (rate models, integrate-and-fire neurons, synaptic plasticity, network dynamics), neuroanatomy (cortical regions, basal ganglia, cerebellum, brainstem, model organism connectomes), neurophysiology (ion channels, action potentials, synaptic transmission), neural circuits (E/I balance, oscillations, central pattern generators), synaptic dynamics (STDP, short-term plasticity, neuromodulation), neurodegenerative diseases (Alzheimer's, Parkinson's, ALS, Huntington's), and clinical neurology (cranial nerves, stroke localization, neuromuscular disorders). Use when researchers ask about brain regions, neural computation, firing rates, synaptic plasticity, connectomics, neurodegeneration, or clinical neurological questions.
Identify and prioritize causal variants at GWAS loci using statistical fine-mapping and locus-to-gene predictions. Computes posterior probabilities for causal variants, links variants to genes via L2G predictions, annotates functional consequences, and suggests validation strategies. Use when asked to fine-map GWAS loci, prioritize causal variants, identify credible sets, or link GWAS signals to causal genes.
Retrieves gene expression and omics datasets from ArrayExpress and BioStudies with gene disambiguation, experiment quality assessment, and structured reports. Creates comprehensive dataset profiles with metadata, sample information, and download links. Use when users need expression data, omics datasets, or mention ArrayExpress (E-MTAB, E-GEOD) or BioStudies (S-BSST) accessions.
Improve academic paper writing quality for ML/CV/NLP-style papers with clear section structure, paragraph flow, and reviewer-facing presentation. Use when drafting or revising Abstract, Introduction, Related Work, Method, Experiments, or Conclusion; polishing figures/tables; checking claim-support alignment; or performing self-review before submission.
General response quality evaluation. Always applicable regardless of domain. Covers response structure, actionability, clarity, and hallucination detection.
Write publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, reviewer guidelines, and citation verification workflows.
Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.
GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.
High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.
Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform
Use when syncing or simplifying example code in docs/articles or README.md so embedded docs samples stay aligned with examples tests while removing compatibility noise from the published snippets.
Performs placebo-in-time sensitivity analysis with hierarchical null model and optional Bayesian assurance. Use when checking model robustness, verifying lack of pre-intervention effects, or estimating study power.
Look up documentation and source code for libraries and packages. Use when the user asks a question about a library, needs to understand a library's API, or when you need information about a library that you don't know about. Triggers on questions like "How do I use X library?", "What's the API for Y?", "Show me how Z library handles this", or when encountering unfamiliar library usage.
Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.
去除文本中的 AI 生成痕迹。适用于编辑或审阅文本，使其听起来更自然、更像人类书写。 基于维基百科的"AI 写作特征"综合指南。检测并修复以下模式：夸大的象征意义、 宣传性语言、以 -ing 结尾的肤浅分析、模糊的归因、破折号过度使用、三段式法则、 AI 词汇、否定式排比、过多的连接性短语。
