Skills
96,770 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Design and audit Revenue Operations CRM governance covering account/contact/opportunity objects, lifecycle stages, source attribution, owner routing, required fields, forecast hygiene, pipeline stages, handoffs, duplicate management, integrations, permissions, automation, reporting, data quality, and change control. Use when revenue teams need one trustworthy operating system instead of spreadsheet truth.
Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use anndata.
Machine learning in Python with scikit-learn. Use for supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
Scrapling web scraping reference for this project. Provides fetcher selection (Fetcher/DynamicFetcher/StealthyFetcher), selector patterns, session management, anti-bot bypass, adaptive/self-healing selectors, Spider framework for crawling, proxy rotation, CLI commands, and MCP server for AI integration. Use when implementing web scraping, handling protected sites, or building data extraction pipelines.
Deep generative models for single-cell omics. Use when you need probabilistic batch correction (scVI), transfer learning, differential expression with uncertainty, or multi-modal integration (TOTALVI, MultiVI). Best for advanced modeling, batch effects, multimodal data. For standard analysis pipelines use scanpy.
This skill should be used when working with single-cell omics data analysis using scvi-tools, including scRNA-seq, scATAC-seq, CITE-seq, spatial transcriptomics, and other single-cell modalities. Use this skill for probabilistic modeling, batch correction, dimensionality reduction, differential expression, cell type annotation, multimodal integration, and spatial analysis tasks.
Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization.
Use 山西证券 Tushare (sxsc_tushare) to obtain professional structured historical financial market data. Characteristics: data is T-1 day (no real-time quotes), and the data conventions suit batch processing and export. Triggers: when the user asks to obtain/pull/export/batch-fetch tushare, sxsc_tushare, or 山西证券 tushare data, or mentions historical quotes batch pulling, batch export of financial statements, backtest data preparation, data export to CSV/Parquet, macro historical data (CPI/PPI/PMI/社融/利率), index constituents, 申万/中信 industry classification, convertible bond data, futures data, bond data, historical fund flows, historical K-lines, limit-up/limit-down statistics, dividends and stock dividends, restricted share unlocking, fund NAV history, Hong Kong stock data, treasury bond yields, Shibor rates, suspension/resumption information, or the top ten traded stocks of 沪深股通, or says "拉份数据", "导出CSV", "批量取数". This skill only covers data fetching and data delivery, not analysis of instruments; if the user requests analysis/evaluation/comparison/deep research on instruments, that should be handled by the sxsc-tushare-analysis skill. Applicable scenarios: batch retrieval of historical data, preparation of backtest data tables, data export.
Process-based discrete-event simulation framework in Python. Use this skill when building simulations of systems with processes, queues, resources, and time-based events such as manufacturing systems, service operations, network traffic, logistics, or any system where entities interact with shared resources over time.
Guidance for recovering data from corrupted or truncated SQLite database files through binary analysis and manual parsing. This skill applies when working with damaged SQLite databases that cannot be opened with standard tools, particularly when corruption is due to binary truncation, incomplete writes, or filesystem errors.
Expert guidance for SQLite database with better-sqlite3 Node.js driver including database setup, queries, transactions, migrations, performance optimization, and integration with TypeScript. Use this when working with embedded databases, better-sqlite3 driver, or SQLite operations.
A Yahoo Finance (yfinance) powered financial analysis tool. Get real-time quotes, generate high-resolution charts with moving averages + indicators (RSI/MACD/Bollinger/VWAP/ATR), summarize fundamentals, and run a one-shot report that outputs both a text summary and a Pro chart.
Critical guardrail for Supabase database operations ensuring multi-tenant isolation with publication_id filtering, proper use of supabaseAdmin, avoiding SELECT *, error handling patterns, and secure server-side database access. Use when writing database queries, working with supabase, accessing newsletter_campaigns, articles, rss_posts, or any tenant-scoped data.
Answer questions and produce mappings for Elastic Common Schema (ECS): Elastic/Elasticsearch fields, fieldsets, data types, allowed values, categorization (`event.kind`, `event.category`, `event.type`, `event.outcome`), custom fields, field reuse, normalization, and OpenTelemetry/OTel/OTLP/SemConv alignment. Use when mapping logs, security telemetry, SIEM detections, network flows, IAM/user activity, cloud/service context, threat intel, or observability metrics/traces into ECS, Elastic Security, Elastic Observability, or Elasticsearch mappings/templates.
Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.
Guide for building Graph Neural Networks with PyTorch Geometric (PyG). Use this skill whenever the user asks about graph neural networks, GNNs, node classification, link prediction, graph classification, message passing networks, heterogeneous graphs, neighbor sampling, or any task involving torch_geometric / PyG. Also trigger when you see imports from torch_geometric, or the user mentions graph convolutions (GCN, GAT, GraphSAGE, GIN), graph data structures, or working with relational/network data. Even if the user just says 'graph learning' or 'geometric deep learning', use this skill.
This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.
Direct REST API access to UniProt. Protein searches, FASTA retrieval, ID mapping, Swiss-Prot/TrEMBL. For Python workflows with multiple databases, prefer bioservices (unified interface to 40+ services). Use this for direct HTTP/REST work or UniProt-specific control
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas
Build or rebuild a private, local-first macOS system that measures daily Git commits, active AI-agent sessions, and instruction prompts, then sends aggregate charts to Discord. Use when someone asks to set up an agentic productivity tracker, install daily agent reports, recreate this architecture on a Mac, or understand how to build it safely. Differentiator: creates a fresh system from generic contracts and never copies another person's data, paths, credentials, repositories, prompts, or private configuration.
Optimize e-commerce search relevance across the full pipeline from query understanding to result presentation. Use this skill when the user needs to improve search quality, implement query processing features, or diagnose search relevance issues — even if they say 'search results are bad', 'improve product search', or 'search relevance optimization'.
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
Comprehensive guide for AntV L7 geospatial visualization library. Use when users need to: (1) Create interactive maps with WebGL rendering (2) Visualize geographic data (points, lines, polygons, heatmaps) (3) Build location-based data dashboards (4) Add map layers, interactions, or animations (5) Process and display GeoJSON, CSV, or other spatial data (6) Integrate maps with AMap (GaodeMap), Mapbox, Maplibre, or standalone L7 Map (7) Optimize performance for large-scale geographic datasets
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
Perform multi-touch attribution analysis using Markov chains, Shapley values, and custom attribution models. Use when you need to analyze marketing channel effectiveness, calculate conversion attribution, optimize marketing budgets, or understand customer journey paths. Supports channel transition analysis, ROI calculation, and marketing optimization insights with Chinese language support.
Automatically locate and fetch authoritative public data (preferring APIs/official bulk downloads) and output a reproducible scraping and cleaning plan. Invoke when the user needs authoritative data, official statistics, API downloads or data source scraping.
Structure a cohort analysis for retention, LTV, or behavioural patterns. Use when asked to run a cohort analysis, analyse retention by cohort, segment users by behaviour over time, or calculate lifetime value by acquisition period. Produces a complete cohort analysis framework with methodology, cohort definitions, retention curves, and prioritised interventions.
Harmonises data classification across jurisdictions mapping GDPR special categories vs CCPA sensitive PI (1798.140(ae)) vs HIPAA PHI (160.103) vs LGPD sensitive data (Art. 5-II). Provides cross-regulation mapping matrix for multinational compliance. Keywords: cross-jurisdiction, GDPR, CCPA, HIPAA, LGPD, sensitive data, multinational, classification mapping.
Use this skill to build, implement, and test Vizro dashboards (Phase 2). Activate when the user wants to create a working app, says "just build it", or has data ready for implementation. Requires spec files from the dashboard-design skill (Phase 1), or user confirmation to skip design.
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
Guides systematic mapping of international personal data flows across an organisation. Covers system-by-system inventory methodology, third-party identification, transfer mechanism assignment, gap analysis, and data flow visualisation. Keywords: data flow mapping, international transfers, data inventory, transfer register, cross-border data flows.
Implements data lineage tracking for privacy compliance including origin tracking, transformation logging, access auditing, deletion verification, and cross-system lineage graphs. Covers source-to-sink mapping, GDPR Art. 30 RoPA integration, automated lineage discovery, and breach impact scoping. Keywords: data lineage, data provenance, data flow mapping, transformation logging, deletion verification.
PostgreSQL database specialist for query optimization, schema design, security, and performance. Use PROACTIVELY when writing SQL, creating migrations, designing schemas, or troubleshooting database performance. Incorporates Supabase best practices.
Work with Data Commons, a platform providing programmatic access to public statistical data from global sources. Use this skill when working with demographic data, economic indicators, health statistics, environmental data, or any public datasets available through Data Commons. Applicable for querying population statistics, GDP figures, unemployment rates, disease prevalence, geographic entity resolution, and exploring relationships between statistical entities.
Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.
Query the Cancer Dependency Map (DepMap) for cancer cell line gene dependency scores (CRISPR Chronos), drug sensitivity data, and gene effect profiles. Use for identifying cancer-specific vulnerabilities, synthetic lethal interactions, and validating oncology drug targets.
Blockchain analytics via Dune REST API — execute DuneSQL queries against live on-chain data, discover decoded contract tables, and monitor credit usage. Use when the user asks about on-chain data, wallet activity, DEX trades, token transfers, smart contract events, or says "query Dune", "run a Dune query", or "search Dune datasets". Pairs with MoonPay to analyze wallets you create and fund.
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.