Skills
97,222 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Generate viewable HTML or a complete spec for host consumption with VChart, modify and diagnose chart configurations, recreate styles from screenshots, and support React-VChart and chart interactions. Use for VChart chart generation, configuration, troubleshooting, and application integration.
A Webflow University guided activity that walks you through migrating existing content into a Webflow CMS, including support for target sites with partial or existing Collections, and generates a custom, portable skill you can use to build and populate it well with any compatible agent
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
A curated collection of 23,000+ agent skills for empirical research across 8 social science disciplines. CoPaper.AI completes a reproducible, rigorous empirical paper in 20 minutes and supports user-uploaded Skills. Maintained by CoPaper.AI from Stanford REAP.
Installs 425 bioinformatics skills covering sequence analysis, RNA-seq, single-cell, variant calling, metagenomics, structural biology, and 56 more categories. Use when setting up bioinformatics capabilities or when a bioinformatics task requires specialized skills not yet installed.
Django migration patterns and safety workflow for PostHog. Use when creating, adjusting, or reviewing Django/Postgres migrations, including non-blocking index/constraint changes, multi-phase schema changes, data backfills, migration conflict rebasing, and product model moves that require SeparateDatabaseAndState. Also use for any deletion or removal of a model, table, column, product, or app — including deleting migration files or retiring a feature — even when no migration is written.
Drizzle ORM for type-safe SQL with PostgreSQL, MySQL, and SQLite. Use when defining schemas, writing queries, managing relations, running migrations, or using drizzle-kit. Use for drizzle, orm, schema, query, migration, pgTable, relations, drizzle-kit, drizzle-zod.
How to read experiment results without fooling yourself. Confidence intervals, p-values, multiple testing, sequential testing, CUPED, heterogeneous treatment effects, ratio metrics, network effects, dashboard reconciliation, and the interpretation failures that produce confidently wrong shipping decisions. Use this skill whenever the user is reading a finished experiment result panel and about to make a ship, kill, or iterate decision, or when an experiment number does not match the dashboard number. Triggers on read experiment results, result panel, ship or kill decision, p-value, confidence interval, statistical significance, multiple testing, peeking, sequential testing, CUPED, variance reduction, heterogeneous treatment effects, ratio metric, network effects, inconclusive test, experiment versus dashboard mismatch. Use `experiment-design` instead when the test has not run yet and the question is hypothesis, sample size, duration, or what to test.
Integrates Power Pages Web API into a site's frontend code with proper permissions and deployment. Orchestrates the full integration lifecycle: code integration, table permissions setup, and deployment for Dataverse CRUD operations. Also supports PAC-downloaded non-SPA sites by preferring native Forms, Lists, and Liquid before custom Web API JavaScript. Use when the user wants to add Web API calls, connect to Dataverse, add data fetching to their frontend, or integrate a non-SPA page with Dataverse
Amazon reverse product selection: an indicator data pool compiled from historical business insights reports. Reverse-screen Amazon niches and keywords across 30+ business dimensions, including market size and growth, price ranges and tier shares, competition density and top-player concentration, audience profiles such as age, gender, and income, and review selling points and pain points. Trigger this skill when users mention reverse product selection, metric-based filtering, niche-market lookup, blue-ocean niche discovery, low-competition niches, beginner-friendly niches, fragmented-brand markets, pain-point entry points, selling-point lookup, pricing-tier opportunities, audience-profile-based product selection, Amazon niche reverse search, niche metrics filter, low-competition niche, blue ocean niche, demographic-based selection, pain-point niche, price tier opportunity, sweet spot pricing, or brand fragmentation. Even if the user does not explicitly say “reverse product selection,” trigger this skill whenever they want to filter Amazon niches based on business criteria.
Use when doing upstream market-research methodology — sizing a market as TAM/SAM/SOM computed BOTH top-down and bottoms-up (never a single unsourced number), planning a survey sample size with finite-population correction and per-segment minimums, or scoring candidate market segments against Kotler's measurable/substantial/accessible/differentiable/actionable criteria. Outputs always show the method and the assumptions. For market-research analysts and product-marketing at the sizing/survey/segmentation moment. Distinct from marketing-skill (campaign analytics, attribution, demand-gen) — this is the evidence-building methodology, not live-campaign optimization.
Generate multiple sequence alignments (MSAs) for protein sequences using the ColabFold MSA-Search NIM. Use for homolog searches, UniRef30/ColabFold environment searches, A3M or FASTA alignments, paired MSA searches for complexes, PDB70 structural templates, hosted NVIDIA API calls, or local Docker deployment. For local deployment, download databases in parallel with aria2c and launch via NIM_MODEL_NAME (the recommended fast path, ~14 min vs. over 80 min for the built-in downloader); a plain docker run uses the slow built-in downloader.
Build, revise, and debug Bayesian models with PyMC. Use when choosing likelihoods and priors, specifying model/data dimensions, fitting hierarchical models, GPs, time series, mixtures, BART or splines, using pymc-extras distributions, marginalization, state-space models or model-building tools, selecting inference methods, resolving failed initial evaluation or model/data shape errors, generating predictions, or testing model code with simulated data and simulation-based calibration (SBC). Covers PyMC 6+, PyTensor 3+ and ArviZ 1.0 DataTree workflows.
Execute Python code in a safe sandboxed environment via [inference.sh](https://inference.sh). Pre-installed: NumPy, Pandas, Matplotlib, requests, BeautifulSoup, Selenium, Playwright, MoviePy, Pillow, OpenCV, trimesh, and 100+ more libraries. Use for: data processing, web scraping, image manipulation, video creation, 3D model processing, PDF generation, API calls, automation scripts. Triggers: python, execute code, run script, web scraping, data analysis, image processing, video editing, 3D models, automation, pandas, matplotlib
Transcription factor binding, cis-regulatory elements (cCREs), chromatin accessibility, and regulatory annotation using JASPAR (motifs), UniBind, ENCODE (cCREs, ChIP-seq), RegulomeDB (regulatory variant scoring), UCSC, and the Ensembl Regulatory Build (motif instances, evolutionarily constrained elements, TF binding matrices) — plus sequence-based deep-learning prediction of regulatory activity and non-coding variant effects (AlphaGenome, Enformer, Borzoi, ChromBPNet, Evo 2). Use for regulatory element annotation, TF-binding-site prediction, regulatory-region functional impact assessment, and predicting how a non-coding variant or a raw DNA sequence affects expression/chromatin/accessibility. Use this whenever a user asks what regulates a gene, whether a SNP hits a regulatory element, or to predict a non-coding variant's functional effect from sequence.
A Webflow University guided activity that walks you through planning a new Webflow CMS collection architecture from scratch, including support for sites with existing Collections, and generates a custom, portable skill you can use to build it well with any compatible agent.
Guide the user to add a data source, connection, or API connector to a Canvas App via Power Apps Studio, then verify and continue. Use when the user asks to add a data source, connection, API, or connector; connect to SharePoint / Dataverse / SQL / Excel / OneDrive / Teams / Office 365; or make similar requests to make new data available to the app. Do not use when the user asks to list or describe existing data sources—call list_data_sources or list_apis directly instead.
Expert knowledge for Azure Data Factory development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring IR/SSIS/SHIR, mapping data flows, SAP/SaaS connectors, CI/CD deployments, or secure VNets/Key Vault, and other Azure Data Factory related development tasks. Not for Azure Data Explorer (use azure-data-explorer), Azure Synapse Analytics (use azure-synapse-analytics), Azure Stream Analytics (use azure-stream-analytics), Azure Databricks (use azure-databricks)
Expert SurrealDB 3 architect and developer skill (tracks v3.1.4+). SurrealQL mastery, multi-model data modeling (document, graph, vector, time-series, geospatial), schema design, security, deployment, performance tuning, SDK integration (JS, Python, Go, Rust, Java, .NET, C, PHP, Swift, Kotlin, Ruby), Surrealism WASM extensions, SurrealML scope coverage (preview), built-in MCP (v3.1+) and standalone SurrealMCP, LangChain Python integration, editor tooling (LSP v0.1.6, tree-sitter, CodeMirror v1.0.6, VS Code/JetBrains/Neovim/Zed), and ecosystem integrations (Surrealist v3.9, Surreal-Sync, SurrealFS, SurrealKit v0.7, n8n, Agent Skills, setup-surreal). Universal skill for 30+ AI agents.
A skill for starting the ByteHouse MCP Server locally and invoking its tools. Use it to connect to a ByteHouse database and query data, interact with ByteHouse via the MCP protocol, and generate data asset catalogs and lineage analyses. Use this skill when users need to connect to a ByteHouse database, query data, interact with ByteHouse via MCP, or generate data asset catalogs and lineage analyses.
Create, manage, and query Databricks Genie Agents — curated, per-data natural-language agents (formerly Genie Spaces): build, export/import, migrate across workspaces, and ask questions of a specific Agent via the Conversation API. For general data questions or finding data across your workspace, use databricks-data-discovery (Genie One) instead
Query the DatoCMS Content Delivery API (CDA) — the read-only GraphQL API — using @datocms/cda-client. Use when users ask for GraphQL content reads: fetching posts/pages/projects, filtering by date/text/fields, sorting/order, pagination/load-more, text pattern matching via regex filters, localization and fallback locales, modular content fragments, Structured Text (DAST) with blocks/inline records, responsive images (srcset/blur-up/imgix), SEO metadata (_seoMetaTags, favicons, global SEO), video/Mux fields, draft or preview reads, environment-targeted reads, cache tags via rawExecuteQuery, why responses miss the CDN cache or hit rate limits, and Content Link metadata for visual editing. Also use for CDA query type generation with gql.tada or GraphQL Code Generator
Turning research artifacts into actionable PM insight. Customer interviews, user research notes, support ticket reviews, sales call transcripts, survey data, in-app feedback, all synthesized into the decisions they are meant to inform. The discipline of moving from raw discovery data to clear product direction without losing signal in the synthesis or fabricating insight that was not actually there. Triggers on research synthesis, customer interview synthesis, user research analysis, discovery readout, research insights, sales call analysis, support ticket analysis, qualitative data analysis. Also triggers when a team has done research but cannot turn it into decisions, when synthesis is producing pretty decks but no roadmap movement, or when an upcoming PM decision needs to be grounded in research already conducted.
Convert natural language questions into safe executable SQL to query Ascend PyTorch Profiler / msprof database for operator time consumption, communication, dispatch, and other performance data. Supports table schema extraction from official documentation. Use this skill when the user wants to: (1) analyze Ascend profiling database, (2) query operator performance data, (3) analyze communication and dispatch bottlenecks, (4) check table schema for profiling data. Trigger: user mentions "profiler db", "sqlite", "sql", "table", "schema", "ascend-pytorch-profiler", "msprof", "operator time", "communication time", "dispatch analysis", "性能分析", "算子耗时", "数据库查询", "性能数据", "性能瓶颈"
Builds a custom lead scoring model for a business. Takes ICP definition, historical win/loss data, CRM export. Analyzes which attributes correlate with closed-won deals. Generates lead-scoring-model.md with scoring dimensions, point values, thresholds, CRM implementation guide, and validation methodology. Can also score a batch of current leads against the model.
Integrates computed statistical results (DEGs, GWAS hits, associations) with biological context from ToolUniverse databases (UniProt, GO, Reactome, ClinVar, OpenTargets). Use to add gene function, pathway, and disease annotations to a result list, build a biological narrative around statistical findings, and go beyond p-values to mechanisms.
Protein 3D structure prediction from sequence — ESMFold de novo prediction, AlphaFold database retrieval, experimental structures from RCSB, ProtVar variant impact assessment, ProtParam sequence properties. Use for structure prediction when no experimental structure exists, fold-confidence scoring, and structure-guided variant interpretation.
Designs and builds ETL/ELT data pipelines. Takes data sources, destination, transformation requirements. Generates pipeline code (Python/SQL), scheduling config, error handling, monitoring setup, and data quality checks. Outputs data-pipeline-spec.md + implementation files.
Write and revise HelixDB queries with the published Python SDK v0.3.4 (package `helix-db`, import `helixdb`, Python 3.10+). Use for `read_batch`, `write_batch`, direct `to_query_request`/`to_query_json` payloads, row bindings, traversal builders, projections, parameters, vector and BM25 search with traversal-scoped prefiltering, synchronous `Client`, and asynchronous server or embedded execution with `AsyncClient`. Stored routes and query bundles are not v3 SDK APIs. When the target is Helix Cloud, always use helix-mcp first.
Use SellerSprite data to find and analyze competitors on Amazon across 12 marketplaces, including product metrics such as sales, BSR, pricing, ratings, and growth trends. Trigger when users mention competitor lookup or analysis, ASIN reverse lookup, competitive product research, finding similar products, market competitor discovery, product benchmarking, competitor sales estimates, analyzing competing listings, competitor analysis, ASIN reverse lookup, competitor sales, competitor research, SellerSprite, market competitor discovery, or competitor trends. Also trigger even without explicitly mentioning “SellerSprite” or “competitor lookup” if the request involves discovering and analyzing Amazon competitors using ASINs, keywords, seller names, brands, or categories.
Build and operate multi-tenant AI agent platforms on Neon. Use this skill whenever the user is designing an agent/app builder, provisioning a Neon project or database per user/app/agent run, managing thousands of tenant projects, separating sponsored free users from paid customers, moving projects between orgs, choosing `@neon/sdk` vs `@neon/tools`, choosing personal vs organization vs project-scoped API keys, tracking fleet consumption or Agent Plan costs, creating compound checkpoints that combine DB snapshots with source revisions/secrets/deploy metadata, or orchestrating snapshot/restore flows for generated apps. Also use it for Neon Agent Program, Agent Plan, org/project limits, HIPAA, co-marketing, support, or neondatabase/neon-for-agent-platforms examples.
Production Python async patterns including asyncio TaskGroup, FastAPI dependency injection and middleware, SQLAlchemy 2.0 async sessions, and database connection pool tuning. Python 3.11+ runtime concerns such as ExceptionGroup, cancellation semantics, and session rollback. Use when building async services, wiring FastAPI dependencies, or tuning database connection pools. Runtime implementation layer, not the API wire contract.
Calculate comprehensive ROI for AI implementation projects. Takes current costs, manual process time, team size, and hourly rates. Generates detailed roi-analysis.md with executive summary, cost-benefit tables, sensitivity analysis, break-even timeline, and comparison scenarios. Use when evaluating AI investments, building business cases, or justifying automation spend.
Use when the user wants to add a new model, database table, or REST API endpoint to a Spree 6 project. Covers `spree:api_resource` (model + migration + Store/Admin API controllers + serializers + factory + controller specs + routes + permission scope) and `spree:model` (model + migration only). Common phrasings include “add a Brand model”, “create a new resource”, “expose X as an API endpoint”, “add an Admin API for Y”, “scaffold a Spree resource”, “create a Spree model without an API”, “internal model”, “make my model store-scoped”.
Universal data access patterns for downloading and parsing scientific data when ToolUniverse tools don't cover the source, only return metadata, or you need bulk records. Use for VCF/h5ad/BAM/SDF/GCT parsing, multi-step API workflows (search to filter to download to parse), thousands of records at once, or sources with no dedicated tool. Write Python code via Bash for every step
Build professional 3D maps and GIS applications with MapV-Three—a 3D mapping library based on the Z-up coordinate system, with geospatial visualization features including map editing, measurement tools, feature drawing, and data management. Suitable for creating Web-GIS applications such as map editors, measurement tools, and spatial data visualizations.
Exa.ai company and people search for lead generation, competitive intelligence, and data enrichment. Use when searching for companies, finding people profiles, building lead gen tools, or implementing Websets for data collection at scale. Triggers on: Exa company search, Exa people search, category company, lead generation, company research, profile search, LinkedIn profiles, Websets API, data enrichment, company lookup, find companies, competitive intelligence, recruiting, talent search, 1B profiles.
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.