Skills
97,222 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.
Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.
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.
Python library for accessing, analyzing, and extracting data from SEC EDGAR filings. Use when working with SEC filings, financial statements (income statement, balance sheet, cash flow), XBRL financial data, insider trading (Form 4), institutional holdings (13F), company financials, annual/quarterly reports (10-K, 10-Q), proxy statements (DEF 14A), 8-K current events, company screening by ticker/CIK/industry, multi-period financial analysis, or any SEC regulatory filings.
Opt-in writer (the /claude-db:fix command). Applies the safe, reversible schema/migration changes from an audit — adding indexes, NOT NULL/CHECK/UNIQUE constraints, missing FKs, type corrections, default/generated columns — as expand-contract migrations. Dry-run preview by default; writes only after explicit per-change confirmation, and only via the migration-writer subagent. Runs only when the user invokes it — never auto-triggered.
Comprehensive geospatial science skill covering remote sensing, GIS, spatial analysis, machine learning for earth observation, and 30+ scientific domains. Supports satellite imagery processing (Sentinel, Landsat, MODIS, SAR, hyperspectral), vector and raster data operations, spatial statistics, point cloud processing, network analysis, cloud-native workflows (STAC, COG, Planetary Computer), and 8 programming languages (Python, R, Julia, JavaScript, C++, Java, Go, Rust) with 500+ code examples. Use for remote sensing workflows, GIS analysis, spatial ML, Earth observation data processing, terrain analysis, hydrological modeling, marine spatial analysis, atmospheric science, and any geospatial computation task.
GSLIB-inspired geostatistics library for variogram analysis, kriging, and simulation. Use when the agent needs to: (1) Calculate experimental variograms, (2) Fit variogram models, (3) Perform simple/ordinary kriging, (4) Run sequential Gaussian simulation (SGSIM), (5) Apply normal score transforms, (6) Decluster spatial data, (7) Generate multiple realizations for uncertainty.
When the user wants traffic data, conversion tracking, user behavior on location pages, GBP traffic attribution, or geographic traffic patterns. Trigger on "Google Analytics," "GA4," "traffic," "conversions," "how many leads," "phone calls," "form submissions," "where is my traffic coming from," or "is my website converting."
Query GTEx (Genotype-Tissue Expression) portal for tissue-specific gene expression, eQTLs (expression quantitative trait loci), and sQTLs. Essential for linking GWAS variants to gene regulation, understanding tissue-specific expression, and interpreting non-coding variant effects.
Query the OFR (Office of Financial Research) Hedge Fund Monitor API for hedge fund data including SEC Form PF aggregated statistics, CFTC Traders in Financial Futures, FICC Sponsored Repo volumes, and FRB SCOOS dealer financing terms. Access time series data on hedge fund size, leverage, counterparties, liquidity, complexity, and risk management. No API key or registration required. Use when working with hedge fund data, systemic risk monitoring, financial stability research, hedge fund leverage or leverage ratios, counterparty concentration, Form PF statistics, repo market data, or OFR financial research data.
Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.
Manage CloudRobo data processing (proc-tasks) and data evaluation (eval-tasks) — query algorithms, create/list/show/update/delete/restart tasks; poll task status until terminal; retrieve system and job logs; download logs; preview output data and frames; discover available algorithms from the asset marketplace; orchestrate processing→evaluation pipelines; batch-manage tasks across workspaces; diagnose failures via log analysis. Triggers include: data processing task management, data evaluation task management, task status polling, log retrieval for troubleshooting, output data preview, algorithm discovery, task pipeline orchestration, batch task management, failure diagnosis, dataset, proc-tasks, eval-tasks.
Query and download public cancer imaging data from NCI Imaging Data Commons using idc-index. Use for accessing large-scale radiology (CT, MR, PET) and pathology datasets for AI training or research. No authentication required. Query by metadata, visualize in browser, check licenses.
Strategic guidance for operationalizing machine learning models from experimentation to production. Covers experiment tracking (MLflow, Weights & Biases), model registry and versioning, feature stores (Feast, Tecton), model serving patterns (Seldon, KServe, BentoML), ML pipeline orchestration (Kubeflow, Airflow), and model monitoring (drift detection, observability). Use when designing ML infrastructure, selecting MLOps platforms, implementing continuous training pipelines, or establishing model governance.
Generate comprehensive market research reports (50+ pages) in the style of top consulting firms (McKinsey, BCG, Gartner). Features professional LaTeX formatting, extensive visual generation with scientific-schematics and generate-image, deep integration with research-lookup for data gathering, and multi-framework strategic analysis including Porter Five Forces, PESTLE, SWOT, TAM/SAM/SOM, and BCG Matrix.
Create and customize MATLAB charts and plots. Plot types (line, scatter, bar, histogram, heatmap, surface), axes configuration, annotations, data tips, interactive plots, animation, multiple axes with tiledlayout, colororder, and performance optimization. Works for standalone figures, Live Scripts, and uifigure apps. Use when plotting data, customizing axes, adding annotations or interactivity, animating charts, or arranging multiple axes. Triggers: plot, chart, graph, axes, uiaxes, annotation, data tips, animation, tiledlayout, colororder, heatmap, scatter, figure, visualization, export.
Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF/SVG for publication. For quick statistical plots use seaborn; for interactive plots use plotly; for publication-ready multi-panel figures with journal styling, use scientific-visualization.
Plan and execute production ML engineering work — classifier versus generative model selection, zero-shot baselines, coding specialization, model training and fine-tuning (LoRA/QLoRA), evaluation and eval-set design, quantization decisions, inference deployment, lineage, feature parity, drift response, and regression triage, grounded in practical engineering patterns for production ML systems. Do not use for statistical modeling and experimental design (that's the data scientist) or for operating a specific inference engine (that's a tool skill such as llama-cpp or vllm).
Multi-Omics Factor Analysis v2 (MOFA+) with mofapy2. Jointly decompose omics layers (scRNA, ATAC, proteomics, methylation) into latent factors capturing major variation. Multi-group designs. AnnData views → MOFA object → train → variance explained → correlate factors with metadata → visualize/cluster → enrich top loadings.
Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use for network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.
Comprehensive biosignal processing toolkit for analyzing physiological data including ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals. Use this skill when processing cardiovascular signals, brain activity, electrodermal responses, respiratory patterns, muscle activity, or eye movements. Applicable for heart rate variability analysis, event-related potentials, complexity measures, autonomic nervous system assessment, psychophysiology research, and multi-modal physiological signal integration.
Neuropixels neural recording analysis. Load SpikeGLX/OpenEphys data, preprocess, perform motion correction, Kilosort4 spike sorting, quality metrics, Allen/IBL curation, and AI-assisted visual analysis for Neuropixels 1.0/2.0 extracellular electrophysiology. Use when working with neural recordings, spike sorting, extracellular electrophysiology, or when the user mentions Neuropixels, SpikeGLX, Open Ephys, Kilosort, quality metrics, or unit curation.
Query RCSB PDB (200K+ structures) via the public REST + GraphQL APIs with plain `requests` (no SDK). Search by text, attribute, sequence, or 3D structure similarity (Search API); retrieve metadata via GraphQL (Data API); download PDB/mmCIF from files.rcsb.org. For AlphaFold predictions use alphafold-database-access; for protein sequences only use uniprot-protein-database.
Interactive scientific visualization with Plotly. Two APIs: plotly.express (px) for one-liner DataFrame plots, plotly.graph_objects (go) for trace-level control. 40+ chart types with hover, zoom, pan, animation. Exports HTML or static PNG/SVG/PDF via kaleido. Use for volcano plots with gene hover, dose-response dashboards, expression heatmaps, 3D molecular views. Use seaborn for stats; matplotlib for publication figures.
Write, execute, and optimize DAX queries and measures for Power BI semantic models using pbi-cli. Invoke this skill whenever the user mentions DAX, queries data in Power BI, writes calculations, creates measures, asks about EVALUATE, SUMMARIZECOLUMNS, CALCULATE, time intelligence, or wants to analyze/aggregate data from a semantic model. Also invoke when the user asks to run a query, test a formula, or check row counts. This skill contains critical guidance on passing DAX expressions via CLI arguments -- multi-line DAX (VAR/RETURN) requires special handling.
This skill should be used when analyzing PostgreSQL performance reports, including pasted PostgreSQL reports, or when users mention db report, database performance, dead rows, VACUUM, sequential scans, unused indexes, or cache hit ratio. It provides PostgreSQL triage heuristics, priority ranking, and action-plan guidance for Drizzle ORM codebases.
Query PubChem (110M+ compounds) directly via the PUG-REST/JSON API with plain `requests` — no SDK install required. Search by name/CID/SMILES/InChIKey/formula, retrieve properties (MW, XLogP, TPSA, H-bond counts), do similarity/substructure searches with async ListKey polling, fetch synonyms, descriptions, assay summaries, and download SDF/PNG. For local cheminformatics use rdkit; for bioactivity-centric workflows use chembl-database-bioactivity.
Direct REST API access to PubMed. Advanced Boolean/MeSH queries, E-utilities API, batch processing, citation management. For Python workflows, prefer biopython (Bio.Entrez). Use this for direct HTTP/REST work or custom API implementations.
High-performance reinforcement learning framework optimized for speed and scale. Use when you need fast parallel training, vectorized environments, multi-agent systems, or integration with game environments (Atari, Procgen, NetHack). Achieves 2-10x speedups over standard implementations. For quick prototyping or standard algorithm implementations with extensive documentation, use stable-baselines3 instead.
Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).
Python Materials Genomics library for structure analysis, thermodynamics, and electronic properties. Parse/create crystal structures (CIF, POSCAR), query Materials Project for DFT-computed properties, analyze phase and Pourbaix diagrams, compute XRD patterns, generate DFT inputs for VASP, Quantum ESPRESSO, CP2K. Alternatives: ASE (MD/geometry), AFLOW (high-throughput), OVITO (visualization).
Guides Redis usage including data structures (strings, hashes, lists, sets, sorted sets), caching patterns, pub/sub, persistence (RDB/AOF), clustering, and Lua scripting. Use when the user needs to implement caching, session storage, rate limiting, queues, or any Redis-based data layer.
Convert Lanelet2 maps (.osm) to RoadRunner HD Map (.rrhd) format using MATLAB. Use when converting Lanelet2 maps into RoadRunner Scene Builder, building driving scenes from open-source map data, or transforming road network definitions for simulation.
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.
Sharpe ratio inference for ONE strategy under non-Normal, serially correlated returns. Implements the ρ-corrected (AR(1), skew, kurtosis) Sharpe variance of López de Prado, Lipton & Zoonekynd (2026, Eq 2-3) and the single-test tools built on it: PSR, MinTRL, critical Sharpe ratio and test power. Numba JIT. Use when computing Sharpe significance, a Sharpe confidence band, minimum track record length or power with autocorrelation and fat tails. TRIGGERS - PSR, probabilistic sharpe ratio, MinTRL, minimum track record length, sharpe variance, non-iid sharpe, autocorrelated sharpe, sharpe significance, sharpe power.
Best practices for Snowflake SQL, semi-structured data, and data pipelines built with Dynamic Tables, Streams, Tasks, and Snowpipe. Use when writing Snowflake SQL, designing ingestion or transformation pipelines, tuning warehouse performance and cost, or working with Time Travel, cloning, RBAC, or Iceberg tables on Snowflake.
Use when you need to turn supplied social performance data into a decision about what to keep, test, change, or stop. Trigger phrases include "report on this campaign", "what worked on social?", "analyse these post results", "make a weekly social report", and "what should we do next from these metrics?". Do not use this to invent data access, claim causation from a dashboard, or replace a campaign brief; use campaign-brief to define success before launch and launch-retrospective for the broader after-action review.
Use when you need to turn a supplied set of comments, DMs, reviews, community posts, or call notes into an evidence-backed picture of what people ask, resist, and repeat. Trigger phrases include "what are people saying about this?", "mine these comments", "what language should we use?", "find recurring objections", and "summarise this community feedback". Do not use this to scrape platforms, infer demographics, or write positioning from a handful of anecdotes; use agent-reach to collect public evidence first, audience-personas to group people, and positioning-audit to decide the message.
Use this skill first for any SpacetimeDB task; it routes to focused skills for modules, tables, reducers, procedures, views, clients, subscriptions, CLI commands, auth, RLS, HTTP APIs, SQL, deployment, serialization, tutorials, quickstarts, and upgrades. Triggers on: spacetime, spacetimedb, SpacetimeDB, stdb, module, reducer, table, procedure, view, subscription, DbConnection, spacetime generate, spacetime publish, spacetime sql, BSATN, SATS, row-level security, RLS, Maincloud, standalone, Unity, Unreal.
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.