Skills
97,222 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Plan and review MySQL/InnoDB schema, indexing, query tuning, transactions, and operations. Use when creating or modifying MySQL tables, indexes, or queries; diagnosing slow/locking behavior; planning migrations; or troubleshooting replication and connection issues. Load when using a MySQL database.
Analyze a Materialize environment via the MCP Developer endpoint, and/or configure an MCP client (Claude Code, Cursor, VS Code, Zed, Continue, Windsurf, Claude Desktop) to connect to the materialize-developer server. For analysis: check environment health, investigate performance, troubleshoot stale materialized views, diagnose memory pressure, find paused clusters holding back compaction, audit resource utilization, run EXPLAIN ANALYZE on user objects, and get optimization recommendations. For client connection: configure/connect/set up an MCP client to materialize-developer (Emulator, Cloud, or self-managed), control which user/role is used, and switch between identities. Trigger even if the user just says “check my environment,” “why is my MV stale,” “why is my cluster slow,” “what can I optimize,” “explain analyze my materialized view,” “how do I connect Claude Code to materialize-developer,” or “configure Cursor for the Materialize MCP.”
Tracking and metrics design generator. Starting from product requirements or the core user journey, output a complete tracking plan document (events, fields, trigger timing, metric definitions, QA validation checklist). Trigger conditions: the user mentions keywords such as "tracking", "event design", "data collection", "reporting plan", "tracking plan", "event spec", "field design", "metric definitions", "data acceptance", or "QA validation". Also applies when: the user provides a product PRD or requirements document and asks for a tracking plan; the user provides a core user journey and asks to break down events; the user asks to standardize event naming or field definitions; the user asks to design a data acceptance plan. Typical input: event naming spec + field dictionary + core journey description/flowchart. Not applicable to: pure data analysis (use pm-analytics), pure BI dashboard building, or pure SQL query writing.
MS data processing with PyOpenMS for LC-MS/MS proteomics and metabolomics — mzML/mzXML I/O, signal processing (smoothing, peak picking, centroiding), feature detection/linking, peptide/protein ID with FDR, untargeted metabolomics. Use matchms for simple spectral matching.
Interpret factor/stock-selection logic from strategy descriptions, research report PDFs, or screenshots, and generate a QMT built-in daily-frequency factor backtest script based on the scripts/daily-factors-backtest.py framework. Use when the user mentions QMT built-in backtesting, factor stock-selection backtesting, cross-sectional factors, research report replication, handlebar backtesting, or after_init precomputed signals.
Evaluate a public GitHub repository's momentum and maintenance health. Use when asked whether a repository is healthy, growing, declining, abandoned, or well maintained, or when vetting dependencies and OSS projects. Do NOT use for code or architecture review.
RoadRunner asset path lookup tables for map format conversions in MATLAB. Maps lane markings, signs, signals, barriers, objects, and lane types to RoadRunner asset paths. Use when converting map formats to RRHD, resolving asset paths, or assigning visual assets to HD Map objects.
Use when adding, changing, testing, or reviewing Postgres persistence in Rust services that use sqlx, especially when designing migrations, query! or query_as! calls, PgPool injection, transactions, compile-time checked SQL, SQLx offline mode, repository boundaries, or database-backed integration tests.
Geostatistical analysis with scikit-learn style API. Compute variograms, kriging interpolation, and spatial correlation analysis. Use when the agent needs to: (1) Compute experimental variograms from spatial data, (2) Fit variogram models (spherical, exponential, gaussian, matern), (3) Perform Ordinary or Universal Kriging interpolation, (4) Assess spatial anisotropy with directional variograms, (5) Cross-validate spatial models, (6) Analyze spatio-temporal data, (7) Export variogram parameters for other geostatistical software.
Python image processing for microscopy and bioimage analysis. Read/write images, filter (Gaussian, median, LoG), segment (thresholding, watershed, active contours), measure region properties, detect features. SciPy/NumPy ecosystem. Use OpenCV for real-time video; CellPose for DL cell segmentation; napari for visualization.
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.
Scrape pages locally with anti-bot bypass, TLS impersonation, and adaptive element tracking — no API keys, no cloud. Handles Cloudflare protection, CSS/XPath element extraction, and survives site redesigns. Complements firecrawl (cloud) with 100% local execution. Triggers on Cloudflare bypass, anti-bot scraping, stealth fetch, local scraping, Scrapling.
Live SEO data via DataForSEO MCP server. SERP analysis (Google, Bing, Yahoo, YouTube, Google Images), keyword research (volume, difficulty, intent, trends), backlink profiles, on-page analysis (Lighthouse, content parsing), competitor analysis, content analysis, business listings, AI visibility (ChatGPT scraper, LLM mention tracking), and domain analytics. Requires DataForSEO extension installed. Use when user says "dataforseo", "live SERP", "keyword volume", "backlink data", "competitor data", "AI visibility check", "LLM mentions", "image SERP", "google images", "image rankings", or "real search data".
Ad-hoc web scraping, site mapping, and full-site crawling via Firecrawl MCP. Returns raw HTML, parsed metadata (og:*, twitter:*, JSON-LD, canonical, robots), JS-rendered DOM, and screenshots that WebFetch cannot. Distinct from the SE Ranking skills (which give keyword/traffic/SERP data) and from WebFetch (which gives markdown prose only). Use when the user says "scrape this page", "crawl this site", "map this site", "find all pages on", "get the OG tags", "get the JSON-LD", "render this JS-heavy page", or any task where raw HTML head metadata, structured-data scripts, or post-JS DOM are the actual deliverable. Also invoked as a sub-step from other skills that need raw HTML.
Register, segment, filter, resample 3D medical images (MRI, CT, microscopy) via SimpleITK Python; DICOM, NIfTI, multi-modal. Rigid/affine/deformable registration, threshold/region-growing segmentation, Gaussian/morph filtering, label stats, format conversion. Use to align volumes across timepoints/modalities, segment fluorescence, or convert DICOM→NIfTI.
Process-based discrete-event simulation. Model queues, shared resources, timed events: manufacturing, service ops, network traffic, logistics. Processes are Python generators yielding events. Resources: capacity-limited (Resource/Priority/Preemptive), bulk (Container), objects (Store, FilterStore). For continuous use SciPy ODEs; for agent-based use Mesa.
Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-analysis.
This skill should be used when user asks to "use supabase CLI", "supabase init", "supabase start", "run migrations", "deploy edge functions", "manage Supabase project", or works with the supabase command-line tool for local development and project management.
Vector database implementation for AI/ML applications, semantic search, and RAG systems. Use when building chatbots, search engines, recommendation systems, or similarity-based retrieval. Covers Qdrant (primary), Pinecone, Milvus, pgvector, Chroma, embedding generation (OpenAI, Voyage, Cohere), chunking strategies, and hybrid search patterns.
Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like “the xlsx in my downloads”) — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
Read, download or archive known web/social media links, URL lists, confirmed candidates or explicit bounded containers according to the user's request in the current turn. Supports explicitly bounded site archiving; not for keyword discovery, recommendation expansion or private bookmark export.
Public opinion monitoring skill that ultimately produces a public opinion report. Use this skill when the user asks "how is the public opinion on an event/topic", "what's the sentiment", "do a public opinion analysis", "run a public opinion analysis", or crawls by keyword/platform and generates a public opinion report. Convention: any public-opinion-related intent triggers the full flow (crawl → sentiment analysis → generate report). Crawling is done by this skill; sentiment analysis is judged autonomously by the main Agent based on the prompt; the report is generated by generate_report.
Access real-time and historical stock market data, forex rates, cryptocurrency prices, commodities, economic indicators, and 50+ technical indicators via the Alpha Vantage API. Use when fetching stock prices (OHLCV), company fundamentals (income statement, balance sheet, cash flow), earnings, options data, market news/sentiment, insider transactions, GDP, CPI, treasury yields, gold/silver/oil prices, Bitcoin/crypto prices, forex exchange rates, or calculating technical indicators (SMA, EMA, MACD, RSI, Bollinger Bands). Requires a free API key from alphavantage.co.
Orchestrates matplotlib, seaborn, and plotly with opinionated publication styles to produce journal-ready figures. Use when preparing journal-submission figures that need multi-panel layouts with bold panel labels, statistical significance annotations, error bars, colorblind-safe palettes (Okabe-Ito), or specific journal formatting (Nature, Science, Cell). Does NOT cover raw low-level plotting or fine-grained control of individual plot elements; for building custom plots from scratch or tuning every artist and rcParam prefer alterlab-matplotlib instead. Part of the AlterLab Academic Skills suite.
Statistical modeling in Python with statsmodels — OLS/WLS/GLS, GLM, discrete-choice and count models, mixed models, ARIMA/SARIMAX/VAR, with diagnostics, robust standard errors, and coefficient-level inference. Use when fitting specific model classes for econometrics, time series, or rigorous inference with coefficient tables and confidence intervals, or when updating code for statsmodels 0.15 (result_object named results, rng keyword). For guided statistical test selection with APA reporting prefer alterlab-statistical-analysis. Part of the AlterLab Academic Skills suite.
Chunked, compressed N-dimensional arrays for cloud storage with Zarr — parallel I/O, S3/GCS integration, and NumPy/Dask/Xarray compatibility. Use when storing or reading large N-D scientific arrays, streaming chunked data to/from cloud object stores, or building large-scale scientific computing pipelines. Part of the AlterLab Academic Skills suite.
This skill is designed to help users automatically extract product data from Amazon search results. The Agent should proactively apply this skill when users request searching for products related to keywords, finding best-selling items from specific brands, monitoring product prices and availability on Amazon, extracting product listings for market research, collecting product ratings and review counts for competitive analysis, finding specific products with a maximum count, searching Amazon in different languages for localized results, tracking monthly sales estimates for brand products, gathering product URLs and titles for a product catalog, scanning Amazon for Best Seller tags in a specific category, monitoring shipping and delivery information for brand items, building a structured dataset of Amazon search results.
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.
Search scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server. Returns 25+ fields per paper including methods, results, sample sizes, quality scores, and conclusions. Use for literature reviews, evidence synthesis, and finding experimental details not available in abstracts alone.
Use when the user asks for BingX perpetual futures technical analysis, indicator values, candlestick pattern recognition, multi-timeframe scoring, support/resistance levels, divergence detection, or trend analysis. Depends on bingx-swap-market for K-line data and computes 78 indicators plus 62 candlestick patterns locally via pandas-ta-classic.
Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use biopython.
Render a production-readiness GO/NO-GO grid for a database — runs a read-only audit, then maps the findings onto a fixed launch checklist (PITR/backups, RLS on tenant tables, FK indexes, money-as-numeric, timestamptz/UTC, connection pooling sized, migration reversibility, secrets-not-in-schema, charset/utf8mb4). Each row is PASS/WARN/FAIL/NEEDS-LIVE with the finding id, topped by a plain-language verdict. Read-only. Use when the user asks if a database is production-ready, ready to launch/ship, a go/no-go, a pre-launch or readiness checklist, or what to fix before going live.
Build accrual schedules for A-share companies tracking revenue recognition, expense accruals, and working capital timing. Adapted from the original accrual-schedule skill for CAS accounting and Chinese business practices. Triggers on "A股应计项目", "应计计提", "accrual schedule China", "应计账款分析", or "working capital accruals [company]".
Prepare for Chinese wealth management client meetings with performance summaries, talking points, and relationship context. Adapted from the original client-review skill for A-share portfolios and Chinese client conventions. Triggers on "A股客户回顾", "客户会议准备", "client review China", "wealth client review", "客户回访准备", or "prepare for client meeting [client]".
Competitive landscape analysis for A-share companies and Chinese industries. Maps competitors, compares positioning, and assesses relative strengths. Adapted from the original competitive-analysis skill for Chinese market context. Triggers on "A股竞争格局", "行业竞争分析", "competitive landscape China", "competitive analysis A-share", or "[company] competitors China".
Parse and extract data from Chinese identity documents for KYC (Know Your Customer) compliance. Adapts the original kyc-doc-parse skill for Chinese ID cards, business licenses, and domestic compliance requirements. Triggers on "A股KYC", "身份文件解析", "parse Chinese ID", "KYC document", "客户身份识别", or "extract [document type]".
Provides expertise on Chroma Cloud integration for semantic search and hybrid search applications. Use when the user is working with Chroma Cloud, CloudClient, managed collections, Schema(), Search(), hybrid search, or Chroma Cloud CLI workflows.
Constraint-based (COBRA) analysis of genome-scale metabolic models: FBA, FVA, knockouts, flux sampling, production envelopes, gapfilling, media optimization. Use for strain design, essential gene ID, flux analysis. For kinetic modeling use tellurium; for visualization use Escher.
TOM and ADOMD.NET guidance via PowerShell for connecting to Power BI Desktop's local Analysis Services instance. Covers model enumeration, DAX queries, metadata modification, annotations, calendar definitions, field parameters, query tracing, DAX library package management (daxlib.org), and the Desktop Bridge for reloading and screenshotting the report canvas. Automatically invoke when the user mentions "Power BI Desktop", "Analysis Services port", "TOM", "ADOMD", "daxlib", "DAX library", "DAX UDF package", or asks to "connect to PBI Desktop", "query PBI Desktop with DAX", "modify PBI Desktop model", "add a measure to PBI", "capture visual queries", "create a field parameter", "validate DAX", "intercept DAX queries", "install daxlib", "add DAX SVG", "add IBCS", "reload the report canvas", "screenshot a report page", "Desktop Bridge", or to work with the model and report in Power BI Desktop together.
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.