数据处理
数据转换、清洗与 ETL
agent-orchestrator
sickn33
Meta-skill que orquestra todos os agentes do ecossistema. Scan automatico de skills, match por capacidades, coordenacao de workflows multi-skill e registry management.
analytics-tracking
sickn33
Design, audit, and improve analytics tracking systems that produce reliable, decision-ready data. Use when the user wants to set up, fix, or evaluate analytics tracking (GA4, GTM, product analytics, events, conversions, UTMs). This skill focuses on measurement strategy, signal quality, and validation— not just firing events.
API Fuzzing for Bug Bounty
sickn33
This skill should be used when the user asks to "test API security", "fuzz APIs", "find IDOR vulnerabilities", "test REST API", "test GraphQL", "API penetration testing", "bug bounty API testing", or needs guidance on API security assessment techniques.
adhx
sickn33
"Fetch any X/Twitter post as clean LLM-friendly JSON. Converts x.com, twitter.com, or adhx.com links into structured data with full article content, author info, and engagement metrics. No scraping or browser required."
advanced-evaluation
sickn33
This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.
ai-analyzer
sickn33
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。
api-documentation-generator
sickn33
"Generate comprehensive, developer-friendly API documentation from code, including endpoints, parameters, examples, and best practices"
airflow-dag-patterns
sickn33
Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
angular-ui-patterns
sickn33
Modern Angular UI patterns for loading states, error handling, and data display. Use when building UI components, handling async data, or managing component states.
pptgen-drawio
xstongxue
根据论文或汇报内容生成多页 Draw.io 格式 PPT，支持论文答辩与通用汇报两种模式，自动导出为 .pptx。当用户提到论文答辩 PPT、答辩幻灯片、通用 PPT、汇报 PPT、根据模板生成 PPT、drawio2pptx 时使用。
drawio-diagram
xstongxue
为深度学习模型、网络架构、算法流程等生成标准 Draw.io (.drawio) 格式的可视化图表；支持从零生成与风格迁移两种模式。从零生成：模型架构图、流程图、感受野示意图等；风格迁移：参考图 + 内容描述/项目 → 按参考图风格生成新图。确保 XML 格式正确，可直接在 Draw.io 中打开编辑。
excalidraw-diagram
xstongxue
基于文字说明或结构信息，生成可直接在 Excalidraw 中打开的手绘风 .excalidraw 图表；支持系统架构图、流程图、数据结构图与自由白板草图，输出标准 Excalidraw JSON。
kb-retriever
ConardLi
面向本地知识库目录的检索和问答助手。核心流程：(1)分层索引导航 (2)遇到PDF/Excel时必须先读取references学习处理方法 (3)处理文件后再检索。按文件类型组合使用 grep、Read、pdfplumber、pandas 进行渐进式检索，避免整文件加载。用户问题涉及"从知识库目录回答问题/检索信息/查资料"时使用。
tooluniverse-binder-discovery
mims-harvard
Discover novel small molecule binders for protein targets using structure-based and ligand-based approaches. Creates actionable reports with candidate compounds, ADMET profiles, and synthesis feasibility. Use when users ask to find small molecules for a target, identify novel binders, perform virtual screening, or need hit-to-lead compound identification.
Protein Interaction Network Analysis
mims-harvard
Analyze protein-protein interaction networks using STRING, BioGRID, and SASBDB databases. Maps protein identifiers, retrieves interaction networks with confidence scores, performs functional enrichment analysis (GO/KEGG/Reactome), and optionally includes structural data. No API key required for core functionality (STRING). Use when analyzing protein networks, discovering interaction partners, identifying functional modules, or studying protein complexes.
tooluniverse-pharmacovigilance
mims-harvard
Analyze drug safety signals from FDA adverse event reports, label warnings, and pharmacogenomic data. Calculates disproportionality measures (PRR, ROR), identifies serious adverse events, assesses pharmacogenomic risk variants. Use when asked about drug safety, adverse events, post-market surveillance, or risk-benefit assessment.
devtu-optimize-skills
mims-harvard
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.
tooluniverse-polygenic-risk-score
mims-harvard
Build and interpret polygenic risk scores (PRS) for complex diseases using GWAS summary statistics. Calculates genetic risk profiles, interprets PRS percentiles, and assesses disease predisposition across conditions including type 2 diabetes, coronary artery disease, and Alzheimer's disease. Use when asked to calculate polygenic risk scores, interpret genetic risk for complex diseases, build custom PRS from GWAS data, or answer questions like "What is my genetic predisposition to breast cancer?"
tooluniverse-image-analysis
mims-harvard
Production-ready microscopy image analysis and quantitative imaging data skill for colony morphometry, cell counting, fluorescence quantification, and statistical analysis of imaging-derived measurements. Processes ImageJ/CellProfiler output (area, circularity, intensity, cell counts), performs Dunnett's test, Cohen's d effect size, power analysis, Shapiro-Wilk normality tests, two-way ANOVA, polynomial regression, natural spline regression with confidence intervals, and comparative morphometry. Supports CSV/TSV measurement tables, multi-channel fluorescence data, colony swarming assays, and neuron counting datasets. Use when analyzing microscopy measurement data, colony area/circularity, cell count statistics, swarming assays, co-culture ratio optimization, or answering questions about imaging-derived quantitative data.
tooluniverse-gwas-finemapping
mims-harvard
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.
tooluniverse-crispr-screen-analysis
mims-harvard
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.
devtu-create-tool
mims-harvard
Create new scientific tools for ToolUniverse framework with proper structure, validation, and testing. Use when users need to add tools to ToolUniverse, implement new API integrations, create tool wrappers for scientific databases/services, expand ToolUniverse capabilities, or follow ToolUniverse contribution guidelines. Supports creating tool classes, JSON configurations, validation, error handling, and test examples.
tooluniverse-gwas-study-explorer
mims-harvard
Compare GWAS studies, perform meta-analyses, and assess replication across cohorts. Integrates NHGRI-EBI GWAS Catalog and Open Targets Genetics to compare study designs, effect sizes, ancestry diversity, and heterogeneity statistics. Use when comparing GWAS studies for a trait, performing meta-analysis of genetic loci, assessing replication across cohorts, or exploring the genetic architecture of complex diseases.
tooluniverse-multiomic-disease-characterization
mims-harvard
Comprehensive multi-omics disease characterization integrating genomics, transcriptomics, proteomics, pathway, and therapeutic layers for systems-level understanding. Produces a detailed multi-omics report with quantitative confidence scoring (0-100), cross-layer gene concordance analysis, biomarker candidates, therapeutic opportunities, and mechanistic hypotheses. Uses 80+ ToolUniverse tools across 8 analysis layers. Use when users ask about disease mechanisms, multi-omics analysis, systems biology of disease, biomarker discovery, or therapeutic target identification from a disease perspective.