All Skills

PDF解析工具，将复杂PDF文档转换为LLM友好的Markdown/JSON格式。适用于：(1) 将PDF转换为Markdown或JSON格式，(2) 提取PDF中的文本、表格、公式、图像，(3) 处理学术论文、技术文档、商业报告的PDF解析，(4) 为RAG应用准备高质量文档数据，(5) 批量处理PDF文件。触发关键词包括："PDF解析"、"PDF转Markdown"、"PDF转JSON"、"提取PDF表格"、"提取PDF公式"、"MinerU"、"文档解析"、"PDF extraction"、"convert PDF"、"parse PDF"等。

Generate AI podcasts using Volcano Engine's Podcast AI Model. Use when user wants to create podcast audio from text input, generate conversational audio content, or transform written content into multi-speaker podcast format. Supports Chinese dual-speaker podcasts with customizable voice options.

专业的微信公众号文章创作助手。当用户提供网站链接、文本素材或图像，需要创作微信公众号文章时使用。支持多种写作风格——企业官号文案、个人技术博客、社区活动回顾、产品评测等。擅长AI/技术话题的深度内容创作，也能产出规范的企业宣传文案。通过搜索工具丰富内容，优化标题和结构。当用户提到"公众号"、"微信文章"、"写一篇推文"、"帮我写篇文章发公众号"等场景时触发。即使用户没有明确说"公众号"，但在要求创作中文长文（技术博客、产品介绍、活动回顾等）时也应考虑触发。

资深高考命题专家助手,提供专业的命题指导和评审服务。适用于创作高考试题、评审试题质量、分析试卷结构、了解命题趋势等场景。结合文档工具提取解压文件,使用网络搜索了解最新命题趋势,使用分析工具评估题目质量和试卷结构。涵盖"一核四层四翼"评价体系、2025年命题趋势、题型规范、评分标准、命题流程等多个维度,符合高考命题最佳实践。

Universal data access reference for scientific research. Teaches how to download bulk data, parse any scientific file format (VCF, h5ad, mzML, PDB, FASTA, XPT, NIfTI, and 30+ more), paginate REST APIs, and handle authentication. Covers 24 domain API patterns across ALL life science data sources — genomics, proteomics, clinical, imaging, ecology, and more. Use this skill whenever you need to download raw data, parse a file format, access a bulk API, write a multi-step data retrieval workflow, or when a ToolUniverse tool returns metadata but you need the actual data. Also use when the data source has no ToolUniverse tool at all. Even if the user doesn't say "data wrangling" — if their task requires getting data from a scientific database or parsing a scientific file format, this is the skill to use.

Help researchers select and characterize cancer cell lines for experiments. Given a cancer type, gene of interest, or cell line name, profiles molecular features (mutations, expression, CNV), gene dependencies (CRISPR screens), drug sensitivities (IC50/AUC), and genetic backgrounds using DepMap, Cellosaurus, PharmacoDB, COSMIC, CellMarker, CLUE, and SYNERGxDB. Generates a decision-support report for cell line selection. Use when researchers ask about which cell line to use, cell line characterization, DepMap dependencies, drug sensitivity profiles, or cancer model selection.

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.

Production-ready RNA-seq differential expression analysis using PyDESeq2. Performs DESeq2 normalization, dispersion estimation, Wald testing, LFC shrinkage, and result filtering. Handles multi-factor designs, multiple contrasts, batch effects, and integrates with gene enrichment (gseapy) and ToolUniverse annotation tools (UniProt, Ensembl, OpenTargets). Supports CSV/TSV/H5AD input formats and any organism. Use when analyzing RNA-seq count matrices, identifying DEGs, performing differential expression with statistical rigor, or answering questions about gene expression changes.

Analyze microbiome and metagenomics data using MGnify, GTDB, ENA, and literature tools. Search studies by biome/keyword, retrieve taxonomic profiles and functional annotations, classify genomes with GTDB taxonomy, and find related publications. Use for human gut microbiome, soil/ocean metagenomics, and environmental microbiology research.

Construct and analyze compound-target-disease networks for drug repurposing, polypharmacology discovery, and systems pharmacology. Builds multi-layer networks from ChEMBL, OpenTargets, STRING, DrugBank, Reactome, FAERS, and 60+ other ToolUniverse tools. Calculates Network Pharmacology Scores (0-100), identifies repurposing candidates, predicts mechanisms, and analyzes polypharmacology. Use when users ask about drug repurposing via network analysis, multi-target drug effects, compound-target-disease networks, systems pharmacology, or polypharmacology.

Code quality patterns and guidelines for ToolUniverse tool development. Apply when writing, fixing, or refactoring tool Python code in the ToolUniverse project. Encodes lessons from 80+ debug rounds. Use alongside devtu-fix-tool and devtu-self-evolve. Triggers: implementing tool fixes, writing new tool classes, reviewing tool code quality, checking schema correctness, looking up API-specific bug fixes.

Comprehensive ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) profiling of drug candidates using ADMETAI predictions, SwissADME drug-likeness, PubChemTox experimental toxicity, ChEMBL clinical data, and PubChem properties. Generates a structured ADMET scorecard with pass/fail verdicts per category. Use when asked about drug-likeness, ADMET properties, bioavailability, toxicity prediction, BBB penetration, CYP interactions, pharmacokinetic profiling, Lipinski rule of five, or ADME/PK assessment of a compound.

GWAS Trait-to-Gene Discovery

Skill: Gene Regulatory Network Analysis

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.

Analyze lipids, lipid metabolism, and lipid-disease associations using LIPID MAPS, HMDB, PubChem, KEGG, and CTD. Covers lipid identification, classification, pathway mapping, biomarker discovery, and disease links. Distinct from general metabolomics — focuses on lipid-specific biology (membrane composition, signaling lipids, lipoproteins, sphingolipids, eicosanoids). Use when asked about lipid profiling, lipidomics data interpretation, lipid biomarkers, lipid metabolism disorders, or lipid-disease connections.

Optimize ToolUniverse skills for better report quality, evidence handling, and user experience. Apply patterns like tool verification, foundation data layers, disambiguation-first, evidence grading, quantified completeness, and report-only output. Use when reviewing skills, improving existing skills, or creating new ToolUniverse research skills.

Create high-quality ToolUniverse skills following test-driven, implementation-agnostic methodology. Integrates tools from ToolUniverse's 1,264+ tool library, creates missing tools when needed, tests thoroughly, and produces skills with Python SDK + MCP support.

Strategic clinical trial design feasibility assessment using ToolUniverse. Evaluates patient population sizing, biomarker prevalence, endpoint selection, comparator analysis, safety monitoring, and regulatory pathways. Creates comprehensive feasibility reports with evidence grading, enrollment projections, and trial design recommendations. Use when planning Phase 1/2 trials, assessing trial feasibility, or designing biomarker-driven studies.

Solve quantitative problems in biophysics, pharmacokinetics, epidemiology, toxicology, population genetics, and statistical mechanics. Provides reasoning strategies and Python templates for calculations alongside ToolUniverse data lookups. Use when users ask about drug dosing, half-life decay, radioactive tracers, R0, herd immunity, diffusion, Hardy-Weinberg, binding equilibria, or any computation-heavy biology/chemistry question.

Systematic ACMG/AMP variant classification using ToolUniverse tools. Given a genetic variant (HGVS, rsID, or gene+change), applies all 28 ACMG criteria (PVS1, PS1-4, PM1-6, PP1-5, BA1, BS1-4, BP1-7) through automated database queries and computational predictions. Produces a final 5-tier classification (Pathogenic / Likely Pathogenic / VUS / Likely Benign / Benign) with evidence summary. Use when asked to classify a variant, interpret a VUS, apply ACMG criteria, assess pathogenicity, or determine clinical significance of a germline variant.

Transform GWAS signals into actionable drug targets and repurposing opportunities. Performs locus-to-gene mapping, target druggability assessment, existing drug identification, safety profile evaluation, and clinical trial matching. Use when discovering drug targets from GWAS data, finding drug repurposing opportunities from genetic associations, or translating GWAS findings into therapeutic leads.

Identify drug repurposing candidates using ToolUniverse for target-based, compound-based, and disease-driven strategies. Searches existing drugs for new therapeutic indications by analyzing targets, bioactivity, safety profiles, and literature evidence. Use when exploring drug repurposing opportunities, finding new indications for approved drugs, or when users mention drug repositioning, off-label uses, or therapeutic alternatives.

Design and evaluate vaccine candidates using computational immunology tools. Covers epitope prediction (MHC-I/II binding via IEDB), population coverage analysis, antigen selection, adjuvant matching, and immunogenicity assessment. Integrates IEDB for epitope prediction, UniProt for antigen sequences, PDB/AlphaFold for structural epitopes, BVBRC for pathogen proteomes, and literature for clinical precedent. Use when asked about vaccine design, epitope prediction, immunogenicity, MHC binding, T-cell epitopes, B-cell epitopes, or population coverage for vaccine candidates.