Skills
97,222 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
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
Python interface to OpenMS for mass spectrometry data analysis. Use for LC-MS/MS proteomics and metabolomics workflows including file handling (mzML, mzXML, mzTab, FASTA, pepXML, protXML, mzIdentML), signal processing, feature detection, peptide identification, and quantitative analysis. Apply when working with mass spectrometry data, analyzing proteomics experiments, or processing metabolomics datasets
Non-coding RNA analysis — miRNAs (miRBase, miRDB targets), lncRNAs (LNCipedia, RNAcentral), circRNAs, snoRNAs, and other ncRNA classes. Distinct mechanisms per class — miRNAs repress mRNA; lncRNAs scaffold/decoy/enhance. Use for ncRNA function prediction, miRNA-target prediction, lncRNA functional annotation, and ncRNA-disease association queries.
Use when the user wants to run SQL—especially analytical SQL—on local files (parquet/csv/json), URLs, S3 paths, or remote databases (Postgres, MySQL, MongoDB, ClickHouse Cloud, Iceberg, Delta Lake) without setting up a server. Provides chDB—embedded ClickHouse SQL in Python with 1000+ functions, Session for stateful multi-step pipelines, parametrized queries, and cross-source joins via `s3()`, `mysql()`, `postgresql()`, `iceberg()`, `deltaLake()`, `remoteSecure()` table functions. TRIGGER when: the user wants SQL on parquet/csv/files or across remote analytical sources; uses ClickHouse SQL features (window functions, windowFunnel, geoToH3, JSON path ops, Session, parametrized queries); imports `chdb` or calls `chdb.query()`. SKIP this skill for pandas-style DataFrame method-chaining (use chdb-datastore instead) or ClickHouse server administration
Build single-purpose, throwaway HTML editors for one specific piece of data — drag-and-drop boards, form-based config editors, side-by-side prompt tuners, dataset curators, annotation tools. Always end with a Submit button (calls `submitToClaude`) so the result can be sent back to the agent. Secret values (API keys, tokens, passwords) are never embedded verbatim — they are masked to references; the artifact and submit payload carry key names and masked previews only.
Extract per-molecule embeddings from any encoder-bearing KERMT checkpoint (grover_base / cmim / hybrid / finetuned). Writes one .npy per readout type (atom_from_atom, bond_from_atom, atom_from_bond, bond_from_bond) plus canonical_smiles.npy and validity.npy. Calls task/extract_embeddings.py (which featurizes SMILES on the fly — no pre-computed features needed).
Run predictions with a finetuned KERMT checkpoint on a SMILES-only CSV. The skill validates that the input ckpt has task FFN heads (refuses pretrain ckpts with a redirect to kermt-finetune), validates the CSV, prepares the data (clean + rdkit_2d features), then launches main.py predict inside the kermt container (blocking, minutes-scale).
Builds and tunes PostgreSQL retrieval with pgvector, full-text search, pg_trgm, hybrid fusion, ParadeDB BM25 and reranking. Use for search/RAG implementation, weak results, filtered ANN scans, non-English corpora or retrieval evaluation. For general schema, RLS and query tuning unrelated to retrieval, use supabase-postgres-best-practices.
Expert knowledge for Azure Databricks development, including troubleshooting, best practices, decision-making, architecture and design patterns, limits and quotas, security, configuration, integrations and coding patterns, and deployment. Use with Unity Catalog, Lakeflow pipelines, Lakebase, AI Runtime/agents, external connectors/BI tools, and other Azure Databricks development tasks. Not for Azure Synapse Analytics (use azure-synapse-analytics), Azure HDInsight (use azure-hdinsight), Azure Data Factory (use azure-data-factory), or Azure Machine Learning (use azure-machine-learning).
Expert knowledge for Azure AI Document Intelligence development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using AnalyzeDocument APIs, custom/classification/template models, batch analysis, containers, or v4.0 migration, and other Azure AI Document Intelligence related development tasks. Not for Azure AI Vision (use azure-ai-vision), Azure AI Custom Vision (use azure-custom-vision), Azure AI Search (use azure-cognitive-search), Azure AI Video Indexer (use azure-video-indexer).
Use when an agent needs to call the Buildin API through the Buildin CLI, authenticate Buildin, upload a file, create or update a page, query a database, configure native relation/formula/rollup fields, search content, edit Markdown page content, or perform any task involving the `buildin` command.
MiniQMT quantitative trading interface, based on the XtQuant Python library (xtdata market data / xttrader trading), for external Python programs to connect to the MiniQMT client. Supports market data retrieval (K-lines, tick-by-tick data, financial data, etc.) and trade order placement (order submission, cancellation, and queries for assets, orders, and positions) for A-shares, futures, and options. Use when users mention miniqmt, xtquant, real-time market data retrieval, quantitative trade order placement, backtesting data retrieval, or need to connect an external Python program to the MiniQMT client for programmatic trading. To write or backtest built-in strategies inside the QMT client (ContextInfo/passorder), use the qmt-strategy skill.
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.
Use when planning and synthesizing product/user research as a method-and-repository discipline—selecting the right method for the goal (generative interviews vs usability test vs concept test vs validation), computing method-based saturation/sample size with an explicit confidence level, or synthesizing coded observations into insights while flagging single-source anecdotes. Never fabricates user insight; an insight requires recurrence across independent participants. Distinct from product-team/ux-researcher-designer (persona/journey artifacts), product-discovery (discovery-sprint planning), and experiment-designer (live A/B)—this is the research-ops method + insight-repository layer.
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.
Query TradingView market data through the bundled tradingview MCP server without a desktop app or login. Use whenever the user wants a technical analysis readout (RSI, MACD, Bollinger rating, BUY/SELL summary), multi-timeframe alignment (weekly→daily→4h→1h→15m), exchange-wide scans (Bollinger squeeze, volume breakout, consecutive candles, smart volume), top gainers/losers by exchange, futures market data (NQ/ES/CL/GC overview, overnight movers, category snapshots), pre-market / after-hours quotes, a quick options chain or unusual options activity (volume/OI by strike), bulk stock prices, a global market snapshot, strategy backtests (Sharpe, drawdown, win rate), or crypto exchange scans. Triggers include “TA on X”, “multi-timeframe read”, “BB squeeze scan”, “futures movers”, “NQ overnight”, “pre-market price”, “unusual options activity”, “backtest RSI on AAPL”, and “top gainers on NASDAQ”. For options greeks, watchlists, alerts, or chart screenshots use tradingview-reader instead.
Use when the user explicitly asks for Flink/Ververica/Realtime Compute Console workspace operations: draft, SQL validation/execution, deployment, job, Session Cluster, namespace, table, member, variable, or checkpoint timeout diagnosis, especially with workspace/deployment/job IDs (w-*, d-*, j-*, sc-*, draft-*). Also use when the prompt asks to test/verify Flink Console lifecycle flow, safety guardrails, or parameter validation for these operations. This includes prompts such as create draft, deploy draft, list deployments, start/stop job, create/list session cluster, get tables, list variables. Also use when the prompt explicitly asks to run `python scripts/flink_ververica_ops.py` for Flink Console workspace operations. Do not trigger for unrelated "workspace" contexts or generic cloud/platform tasks (ECS, OSS, RDS, Kafka, Spark, Kubernetes, billing, weather). Do not trigger for Flink instance lifecycle operations (create/scale/delete/renew); those belong to alibabacloud-flink-instance-manage.
Parse cyber threat intelligence reports (Mandiant, CrowdStrike, Talos, Microsoft) with stix2, mitreattack-python, and spaCy to extract adversary behaviors, map them to MITRE ATT&CK technique IDs, and build a searchable STIX 2.1 attack-pattern library with detection templates. Use when cataloging attack patterns from CTI reports for threat-informed detection engineering, or generating Sigma/YARA templates from documented behaviors.
Reviews DataHub connector implementations against 22 golden standards for compliance, code quality, silent failures, test coverage, type design, and merge readiness. Use when reviewing connector code, checking a PR, auditing a connector implementation, or verifying connector standards compliance.
Use this skill when the user wants to explore lineage, trace data dependencies, perform impact analysis, find root causes, map data pipelines, or understand how data flows between systems. Triggers on: "what feeds into X", "what depends on X", "show lineage for X", "impact analysis", "trace the pipeline", "root cause", "upstream of X", "downstream of X", or any request involving data lineage and dependency tracking.
Digital pathology image processing toolkit for whole slide images (WSI). Use when working with histopathology slides, processing H&E- or IHC-stained tissue images, extracting tiles from gigapixel pathology images, detecting tissue regions, segmenting tissue masks, or preparing datasets for computational pathology deep learning pipelines. Applies to WSI formats (SVS, TIFF, NDPI), tile-based analysis, and histological image preprocessing workflows.
Automated hypothesis generation and testing using large language models. Use this skill when generating scientific hypotheses from datasets, combining literature insights with empirical data, testing hypotheses against observational data, or conducting systematic hypothesis exploration for research discovery in domains like deception detection, AI content detection, mental health analysis, or other empirical research tasks.
Explore PostHog's Inbox and act on what it surfaces — the place where signal reports cluster into actionable issues and trends. Use when the user asks "what's in my inbox?", "what should I look at?", "which reports are actionable?", "what's PostHog flagged recently?", asks about a specific report by ID or title, wants to act on / fix / implement a report (turn it into a PR), wants to resolve, dismiss, or snooze a report, or wants to see which signal sources are configured. Covers listing, filtering, drilling into, and acting on reports, plus pointers to the deeper `signals` skill when raw signals or semantic search are needed.
Implements knowledge graphs for AI-enhanced relational knowledge. Covers ontology design, graph database selection (Neo4j, Neptune, ArangoDB, TigerGraph), entity extraction, hybrid graph-vector architecture, query patterns, and AI integration. Use when implementing knowledge graphs, designing ontologies, extracting entities and relationships, selecting a graph database, or building hybrid graph-vector search. Use for knowledge graph, ontology design, entity resolution, graph RAG, hallucination detection. For architecture selection and governance, use the knowledge-base-manager skill. For document retrieval pipelines, use the rag-implementer skill.
Alibaba Cloud PolarDB Database AI Assistant. For PolarDB MySQL/PostgreSQL cluster management, instance inspection, performance diagnostics, parameter explanation and change assessment, SQL/log analysis, backup, security audit, HA/DR, event analysis, and other PolarDB O&M operations. Use when user questions involve PolarDB, cluster IDs starting with pc-, PolarDB kernel/proxy versions, parameters, primary-standby switchover, IMCI columnar storage, or PolarDB console operations. This Skill is PolarDB-only.
Compare two Google Search Console page exports (current vs previous period) to flag pages whose clicks or impressions fell by a chosen threshold, with a triage hint (refresh, query shift, consolidate, prune). The 20% default is a screening heuristic, not proof of decay. Use for "/blog decay", "content decay", "QoQ decline", "refresh declining posts". Needs two GSC exports; not for diagnosing a single traffic drop live (blog-google) or rewriting posts (blog-rewrite).
Use when the user asks for a Databricks lakehouse architecture diagram — medallion architecture (Bronze/Silver/Gold), Delta Lake, Unity Catalog, workspace deployment, data-plane/control-plane, or any diagram built with Databricks icons. Builds with the declarative layout engine using ground-truth stencils, validates (stencils/colors/nesting/geometry), runs a render-based vision self-check. Default output is .drawio; PNG/SVG only on request.
Best practices for building charting applications using MMT real-time crypto market data with TradingView Lightweight Charts. Use when creating price charts, volume charts, indicator overlays, multi-chart dashboards, or any visualization that renders MMT data using the TradingView Lightweight Charts library.
Cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Enables building and training quantum circuits with automatic differentiation, seamless integration with PyTorch/JAX/TensorFlow, and device-independent execution across simulators and quantum hardware (IBM, Amazon Braket, Google, Rigetti, IonQ, etc.). Use when working with quantum circuits, variational quantum algorithms (VQE, QAOA), quantum neural networks, hybrid quantum-classical models, molecular simulations, quantum chemistry calculations, or any quantum computing tasks requiring gradient-based optimization, hardware-agnostic programming, or quantum machine learning workflows
Fetch website traffic estimates (monthly visits, traffic sources, top countries, keywords, engagement, ranks) for any domain from SimilarWeb. Use when the user asks about a domain's traffic, monthly visits, traffic sources, audience countries, or wants to compare or benchmark sites against competitors.
Implements and customizes the Syncfusion .NET MAUI DataGrid (SfDataGrid) for displaying tabular data. Use when working with MAUI data grids, SfDataGrid, tabular data display, data binding to grids, or column configuration. Covers cell editing, sorting, filtering, grouping, paging, exporting to Excel/PDF, row operations, selection, and summaries.
Systematic chart reading and technical analysis for NSE/BSE Indian equities. Covers trend identification, support/resistance mapping, volume analysis, indicator dashboards, and probabilistic scenario building. Uses Groww MCP for live data and yfinance for history. Use when the user asks to: read a chart, identify trends, find support/resistance levels, check indicators (RSI, MACD, Bollinger, ADX, Stochastic), analyze volume, or build trade scenarios. Triggers on: “what's the trend on [stock],” “support resistance for [stock],” “indicators for [stock],” “is [stock] bullish or bearish,” “volume analysis,” “chart analysis,” or any request to interpret price action on Indian equities.
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.