Skills
97,222 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Audit foreign-key integrity — missing FKs that allow orphan rows (financial/auth = severity 5), absent or unsafe ON DELETE/ON UPDATE actions, reference cycles, and composite-FK column/order mismatches. Module M3. Feeds both the Design & Integrity (Integridad referencial) and Performance & Scale (Query) scores.
Audit column types and precision — money stored as float/double (severity 5), naive timestamp vs timestamptz/UTC, jsonb used to evade schema, enum-vs-lookup-table choice, and charset/collation (utf8mb4, case-insensitive collation). Module M4. Feeds the Design & Integrity score (Tipos category).
Query DDInter drug-drug interactions via REST API (1.7M+ interactions, 2,400+ drugs). Search by drug name/ID for severity (major/moderate/minor), mechanisms, and clinical recommendations. No auth. For FDA labeling use dailymed-database; for pharmacogenomics use clinpgx-database.
Run and operate the DeepStream Multi-View 3D Tracking reference app, also known as MV3DT. Use when the user asks to set up prerequisites, run shipped MV3DT samples, run Multi-View 3D Tracking on custom synchronized MP4 datasets, import camera calibration, delegate missing calibration to AutoMagicCalib, inspect OSD or BEV visualization, consume MV3DT Kafka metadata, or clean up MV3DT run state in the DeepStream MV3DT app directory.
Greenfield database design — pick the right engine for a new project, compare it against the boring default, and hand back a starter data model with a diagram. Recommendation mode (module M0); never scored, never destructive. Use when the user is starting fresh and asks what database to use, how to model a new app, which engine fits, or for a schema/data-model from scratch.
Access and analyze comprehensive drug information from the DrugBank database including drug properties, interactions, targets, pathways, chemical structures, and pharmacology data. This skill should be used when working with pharmaceutical data, drug discovery research, pharmacology studies, drug-drug interaction analysis, target identification, chemical similarity searches, ADMET predictions, or any task requiring detailed drug and drug target information from DrugBank.
Look up EMDB cryo-EM density maps and fitted atomic models via the entry REST API + EBI Search WS. Fetch entry metadata (resolution, method, organism, sample), map download URLs, fitted PDB IDs, and citations. Keyword search via EBI Search. No auth. For atomic coordinates use pdb-database; for AlphaFold predictions use alphafold-database-access.
Extract keywords from documents using YAKE algorithm with support for 34 languages (Arabic to Chinese). Use when users request keyword extraction, key terms, topic identification, content summarization, or document analysis. Includes domain-specific stopwords for AI/ML and life sciences. Optional deeper extraction mode (n=2+n=3 combined) for comprehensive coverage.
Query openFDA REST API for adverse events (FAERS), labeling, product info, recalls, enforcement. Search by drug name, ingredient, MedDRA, or NDC. 1k req/day no key; 120k with free key. For trials use clinicaltrials-database-search; for structures use drugbank-database-access or chembl-database-bioactivity.
Selects a fine-tuning technique (SFT, DPO, RLVR, or RLAIF) for the user's use case and validates it against the selected model's available recipes. Use when the user has decided to finetune and needs to choose a technique, or when the technique needs to be validated against a model. Requires a base model to already be selected (via model-selection skill).
Team-management dashboard skill in the FlowAI aesthetic — three tabs (Team Members, Team Details, Activity Log), KPI stat row, member table, role distribution bar chart, online presence and activity sparklines, and a top-contributors panel, all in a single self-contained HTML file with light/dark theming, hoverable chart tooltips, click-to-zoom panels, and CSV export. Use when the brief asks for a team / workspace admin dashboard, an interactive admin dashboard with charts, or names FlowAI.
Use this skill when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.
Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving reading/writing/analyzing vector geographic data. Supports PostGIS databases, interactive maps, and integration with matplotlib/folium/cartopy. Use for tasks like buffer analysis, spatial joins between datasets, dissolving boundaries, clipping data, calculating areas/distances, reprojecting coordinate systems, creating maps, or converting between spatial file formats.
This skill should be used at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory, disk space). It creates a JSON file with resource information and strategic recommendations that inform computational approach decisions such as whether to use parallel processing (joblib, multiprocessing), out-of-core computing (Dask, Zarr), GPU acceleration (PyTorch, JAX), or memory-efficient strategies. Use this skill before running analyses, training models, processing large datasets, or any task where resource constraints matter.
Query gnomAD (Genome Aggregation Database) for population allele frequencies, variant constraint scores (pLI, LOEUF), and loss-of-function intolerance. Essential for variant pathogenicity interpretation, rare disease genetics, and identifying loss-of-function intolerant genes.
High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.
Helius Solana RPC and API expert. High-performance infrastructure for Solana including RPC nodes, DAS API for NFTs/tokens, LaserStream real-time streaming, webhooks, Priority Fee API, Enhanced Transactions, and ZK Compression. Triggers on Helius, Solana RPC, DAS API, Digital Asset Standard, NFT metadata, Solana webhooks, priority fees, LaserStream, ZK compression.
Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.
Make live IndyKite authorization decisions (AuthZEN/KBAC) and run ContX IQ graph queries from an AI agent over the Model Context Protocol - self-contained Bearer-token JSON-RPC calls on the stateless protocol (revision 2026-07-28), no session handshake and no bespoke REST wiring. Use when calling the IndyKite MCP server (server/discover, tools/list, authzen_evaluate, authzen_evaluations, authzen_search_*, ciq_execute), configuring an MCP server, or debugging its single Bearer-token auth.
Query InterPro for protein family, domain, and functional site annotations. Integrates Pfam, PANTHER, PRINTS, SMART, SUPERFAMILY, and 11 other member databases. Use for protein function prediction, domain architecture analysis, evolutionary classification, and GO term mapping.
Tier-1 live read-only introspection. Connects to the running database through a DB MCP server's read-class tools (preferred) or a least-privilege read-only connection string, reads catalogs only (no table data, no writes), and upgrades affected findings to confidence established. Enforces the read-only contract; when no live access is available it marks the dependent checks needs_api — never a silent pass. Invoked by db-orchestrator and the auditors when a check needs runtime truth.
Query JASPAR for transcription factor binding site (TFBS) profiles (PWMs/PFMs). Search by TF name, species, or class; scan DNA sequences for TF binding sites; compare matrices; essential for regulatory genomics, motif analysis, and GWAS regulatory variant interpretation.
Direct REST API access to KEGG (academic use only). Pathway analysis, gene-pathway mapping, metabolic pathways, drug interactions, ID conversion. For Python workflows with multiple databases, prefer bioservices. Use this for direct HTTP/REST work or KEGG-specific control.
Operate KWeaver knowledge networks and Decision Agents — build knowledge networks, query schemas/instances, semantic search, execute Actions, Agent CRUD and conversations, Trace data analysis. Operate Dataflow document processes — list flows, trigger runs, query run history, view step logs. Operate the Skill management module — register Skills, market lookup, progressive reading, download and install. Operate Toolbox / Tool — create toolboxes, upload OpenAPI tools, publish and start/stop. Operate the Vega observability platform — query Catalogs/resource/connector types, health checks. Operate the model factory — LLM/small model CRUD (mf-model-manager), OpenAI-compatible chat and small model invocation (mf-model-api), adapter registration. Automatically used when the user mentions intents such as "knowledge network", "knowledge graph", "query object class", "execute Action", "which Agents exist", "create Agent", "chat with Agent", "list all Agent templates", "list Agents I created", "list Agents in my private space", "dataflow", "data flow", "process orchestration", "process run records", "process logs", "trigger dataflow", "view dataflow run history", "Skill", "skill package", "register Skill", "install Skill", "read SKILL.md", "toolbox", "upload tool", "register tool", "OpenAPI tool", "enable tool", "publish toolbox", "data source", "data view", "atomic view", "Catalog", "Vega", "health check", "inspection", "trace", "evidence chain", "data flow tracking", "data source", "how the data was obtained", "BKN metric", "metric management", "metric query", "metric dry-run", "model factory", "small model", "embedding", "rerank", "mf-model", "model chat".
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies
Spectral similarity and compound identification for metabolomics. Use for comparing mass spectra, computing similarity scores (cosine, modified cosine), and identifying unknown compounds from spectral libraries. Best for metabolite identification, spectral matching, library searching. For full LC-MS/MS proteomics pipelines use pyopenms.
MS spectral matching and metabolite ID with matchms. Import spectra (mzML, MGF, MSP, JSON), filter/normalize peaks, score similarity (cosine, modified cosine, fingerprint), build reproducible pipelines, identify unknowns vs spectral libraries. Use pyopenms for full LC-MS/MS proteomics.
Use when a Materialize object is behind wall-clock time — high lag in mz_wallclock_global_lag, a stale or lagging materialized view, index, or sink, a freshness alert, results that keep showing old data, or a dataflow whose output cannot keep up with its input.
Query Metabolomics Workbench REST API (4,200+ NIH studies) for metabolite ID, study discovery, RefMet standardization, m/z precursor searches, and gene/protein annotations. Quirks: compound input_item rejects `name` (use pubchem_cid/kegg_id/inchi_key/etc.); free-text → compound is a two-step refmet/match→refmet/name flow; moverz endpoint returns TSV text, not JSON. Use hmdb-database for local XML; pubchem-compound-search for general compound lookup.
Query the Monarch Initiative knowledge graph for disease-gene-phenotype associations across species. Integrates OMIM, ORPHANET, HPO, ClinVar, and model organism databases. Use for rare disease gene discovery, phenotype-to-gene mapping, cross-species disease modeling, and HPO term lookup.
Guides adding, changing, and reviewing telemetry through the `@n8n/telemetry` event registry. Use when working on telemetry, analytics, tracking, product events, `track()` calls, or RudderStack/PostHog product events, in frontend or backend code — and whenever you need to find which registered telemetry events exist, what an event means, or what properties it carries.
Use when searching, extracting, ingesting, or querying a document collection with the NeMo Retriever 26.8.1 CLI, including local LanceDB indexes and deployed Retriever services. Use for PDFs, images, Office files, HTML, text, audio, and video; not for editing documents or web search.
Graph and network analysis toolkit. Four graph types (directed, undirected, multi-edge), centrality, shortest paths, community detection, generators, I/O (GraphML, GML, edge list), matplotlib viz. For large graphs (100K+ nodes) use igraph or graph-tool; for GNNs use PyG.
Python toolkit for neurophysiological signal processing: ECG (HR, HRV, R-peaks), EEG (complexity, PSD), EMG (activation onset), EDA/GSR (SCR decomposition), PPG, and RSP. Includes synthetic signal simulation. Alternatives: BioSPPy (less maintained), MNE (EEG/MEG specialist), heartpy (ECG only), scipy.signal (raw DSP).
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.