Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Drizzle ORM against PostgreSQL inside a Next.js App Router app. Covers client construction (globalThis singleton across HMR, serverless pool sizing, `prepare:false` behind PgBouncer/Supavisor, driver choice when you need interactive transactions, `server-only`), reads in Server Components under Next.js 16 Cache Components (`use cache` superseding `unstable_cache` and the `revalidate`/`dynamic` segment configs, Suspense boundaries, React `cache()` dedupe), Server Actions (authorization inside the action, `updateTag` vs `revalidateTag`, `after()`), Postgres schema types (timestamptz, identity vs serial, jsonb, numeric-as-string, bigint modes), drizzle-kit migrations (generate vs push, CONCURRENTLY outside the migrator, NOT VALID constraints, rename prompts), transactions and pooled connections, and Postgres query traps (keyset pagination, count cost, driver-dependent `db.execute()` shape, NOT IN nulls, prepared statements). Use when writing or reviewing Drizzle + Postgres code in Next.js.
Guides Elasticsearch usage including index mapping design, query DSL (match, term, bool, aggregations), bulk indexing, cluster management, and performance tuning. Use when the user needs to implement full-text search, design index mappings, write complex search queries, or manage Elasticsearch clusters.
Use when the user wants to design experiments, plan ablation studies, structure baselines, or create incremental evaluation strategies. Triggers on phrases like "design ablation", "plan experiment", "what experiments should I run", "baseline comparison", or "experiment matrix".
Academic research experiment design skill — a complete process from research hypothesis to a reproducible experiment plan. Use this skill whenever the user needs to plan experiments, design an ablation study, choose baselines, decide on evaluation metrics, or asks "which experiments should I run". Trigger words include: experiment design, ablation, baseline, what experiments to run, evaluation metric, how to validate a method. Applies to experiment planning in fields such as machine learning, NLP, and CV.
Design experiments and studies BEFORE data is collected — choosing a design, randomizing, blocking, and laying out treatment combinations so results are interpretable. Use whenever someone is planning a study, asks how to assign subjects/samples to groups, mentions randomization, blocking, stratification, controls, factorial or fractional-factorial designs, design of experiments (DOE), screening many factors, response-surface optimization, crossover or repeated-measures or split-plot designs, cluster/group randomization, Latin squares, plate layouts, batch/run-order effects, replication vs. pseudoreplication, or sequential/adaptive/group-sequential designs. Trigger even for informal phrasings like "how should I set up this experiment", "how do I avoid confounding", "what's the best way to test these 6 factors", or "assign these mice to conditions". For computing the sample size or power once the design is chosen, use statistical-power; for analyzing data already collected, use statistical-analysis.
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.
Scrape Google Maps for local businesses by category and location, output CSV ready for cold email enrichment. Best for SMB campaigns targeting restaurants, clinics, gyms, salons, contractors, etc. Uses RapidAPI Maps Data API. Output feeds directly into /blitz-list-builder (to find owner contacts) or //list-builder Phase 5 (if you have names already).
Use this skill when working on an Expo or React Native app that uses, adds, debugs, or migrates to Convex. It covers `npx convex dev`, `EXPO_PUBLIC_CONVEX_URL` and EAS envs, `ConvexReactClient` and provider wiring in `expo-router` or `App.tsx`, generated `api` imports, schema and index design, queries, mutations, actions, auth (Clerk, Convex Auth, JWT or OIDC), file uploads from Expo URIs, pagination, migrations, and common `useQuery` or `_generated` failures. Do not use it for generic Expo UI or navigation work, or for non-Expo Convex frontends unless the task is specifically about adapting them to this mobile stack.
MATLAB and GNU Octave numerical computing for matrix operations, data analysis, visualization, and scientific computing. Use when writing MATLAB/Octave scripts for linear algebra, signal processing, image processing, differential equations, optimization, statistics, or creating scientific visualizations. Also use when the user needs help with MATLAB syntax, functions, or wants to convert between MATLAB and Python code. Scripts can be executed with MATLAB or the open-source GNU Octave interpreter.
MaxQuant + Perseus proteomics pipeline: run MaxQuant for LFQ and SILAC; parse proteinGroups.txt in Python; filter contaminants/decoys; log2 + median-normalize; impute MNAR; t-test with FDR; volcano plot; GO/pathway enrichment. Use Proteome Discoverer for Thermo-native processing; FragPipe/MSFragger for GPU-accelerated DB search.
Based on the East Money database, it supports natural language queries for financial data, covering A-shares, Hong Kong and US stocks, funds, bonds and other assets, including real-time quotes, company information, valuation, and financial statements. It can be used for investment research, trading review, market monitoring, industry analysis, credit research, financial statement auditing, asset allocation and other scenarios, serving the diverse needs of institutions and individuals. Results include xlsx and Markdown files. Natural language query for financial data across all markets, including A-shares, ETFs, bonds, Hong Kong and US stocks, and funds. It provides L1/L2 data, financial indicators, company profiles and valuation metrics. Ideal for investment research, strategy backtesting, market monitoring and industry analysis. It meets the needs of diverse institutions and individuals.
Query and analyze scholarly literature using the OpenAlex database. This skill should be used when searching for academic papers, analyzing research trends, finding works by authors or institutions, tracking citations, discovering open access publications, or conducting bibliometric analysis across 240M+ scholarly works. Use for literature searches, research output analysis, citation analysis, and academic database queries.
Analyze experiment results and generate discussion paragraphs for academic papers. Two-phase workflow: identify measurable findings (Phase 1), confirm with user, then generate grounded discussion paragraphs (Phase 2). Accepts tables, statistics, or result descriptions. 实验分析与讨论段落生成。
Elicit and check Bayesian prior assumptions in meaningful units. Use for expert quantiles, PreliZ, constrained priors, prior-predictive plausibility and prior sensitivity, including power scaling, regularized horseshoe and R2D2 assumptions.
Professional data visualization using Python (matplotlib, seaborn, plotly). Create publication-quality static charts, statistical visualizations, and interactive plots. Use when generating charts/graphs/plots from data, creating infographics with data components, or producing scientific/statistical visualizations. Supports PNG/SVG (static) and HTML (interactive) export.
Run ClickHouse `system.query_log` analysis via the internal Metabase API. Use when investigating slow queries, materialization candidates, per-team query performance, ClickHouse cost or memory issues, or any system.query_log question. Covers prod-us and prod-eu, SSO-gated cookie auth via `hogli`, and ready-to-run query patterns.
Query Reactome pathways via REST: pathway queries, entity lookup, keyword search, gene list enrichment, hierarchy, cross-refs. Content + Analysis services. Python wrapper: reactome2py. For KEGG use kegg-database; for PPIs use string-database-ppi.
Evaluating scientific evidence and claims. Covers study design hierarchy (RCT to expert opinion), effect sizes (OR, RR, NNT, Cohen's d), confounding, p-value vs clinical significance, GRADE quality assessment, reproducibility, and bias types (selection, information, confounding, reporting). Use when reading a paper or assessing claims.
Time-to-event modeling with scikit-survival: Cox PH (elastic net), Random Survival Forests, Boosting, SVMs for censored data. C-index, Brier, time-dependent AUC; Kaplan-Meier, Nelson-Aalen, competing risks. Pipeline/GridSearchCV compatible. Use statsmodels for frequentist, pymc for Bayesian, lifelines for parametric.
SERP-based semantic topic clustering for content architecture planning. Groups keywords by actual Google SERP overlap (not text similarity), designs hub-and-spoke content clusters with internal link matrices, and generates interactive visualizations. Optionally executes content creation if codex-blog is installed. Use when user says "topic cluster", "content cluster", "semantic clustering", "pillar page", "hub and spoke", "content architecture", "keyword grouping", or "cluster plan".
Direct access to Google's own SEO data via Search Console (Search Analytics, URL Inspection, Sitemaps), PageSpeed Insights v5, CrUX field data with 25-week history, Indexing API v3, GA4 organic traffic, YouTube video search, Google NLP entity/sentiment analysis, Knowledge Graph entity verification, Web Risk safety, and Google Ads Keyword Planner. Bridges crawl-based analysis (the rest of this catalogue) with Google's real-time field data — actual Chrome user metrics, real indexation status, real search performance, real organic traffic. Use when the user asks "search console", "GSC", "PageSpeed", "CrUX", "field data", "indexing API", "GA4 organic", "URL inspection", "google api setup", "real CWV data", "impressions", "clicks", "CTR", "position data", "LCP", "INP", "CLS", "FCP", "TTFB", "Lighthouse scores", "youtube SEO", "knowledge graph", "keyword planner", or "real google data".
Maps intelligence for local SEO — geo-grid rank tracking, GBP profile auditing via API, review intelligence across Google/Tripadvisor/Trustpilot, cross-platform NAP verification (Google/Bing/Apple/OSM), competitor radius mapping, and LocalBusiness schema generation from API data. Three-tier capability: free (Overpass + Geoapify), DataForSEO (full intelligence), DataForSEO + Google (maximum coverage). Use when user says "maps", "geo-grid", "rank tracking", "GBP audit", "review velocity", "competitor radius", "maps analysis", "local rank tracking", "Share of Local Voice", or "SoLV".
Use when designing or implementing Spring Data JPA on Spring Boot 4 — repository boundaries, projections, query patterns, custom repositories, CQRS read models, entity relationships, or persistence performance fixes. Not for generic SQL help, database admin work, or project-wide migration (see the springboot-migration skill).
Use when choosing or evaluating a startup revenue model, pricing/value metric, packaging/tier design, or calculating unit economics (LTV, CAC, payback, gross margin, NRR), including usage-based/credit/AI pricing and variable compute/COGS constraints.
Python statistical modeling: regression (OLS, WLS, GLM), discrete (Logit, Poisson, NegBin), time series (ARIMA, SARIMAX, VAR), with rigorous inference, diagnostics, and hypothesis tests. Use scikit-learn for ML; statistical-analysis for test choice.
Guidance for open science practices — preregistration (OSF Registries, AsPredicted, PROSPERO, ClinicalTrials.gov), open data and FAIR principles, repository choice (Zenodo, Dryad, Figshare), open access routes (Green/Gold/Diamond), Creative Commons licensing, reproducible computational workflows (Docker, Binder, Code Ocean), registered reports, open peer review, and TOP Guidelines. Use when preregistering a study, writing the FAIR data-sharing and repository section of a grant data management plan (NSF, NIH, ERC, UKRI), choosing a data repository, navigating open access, or building reproducible analyses. For human-subjects ethics, IRB applications, informed consent, or GDPR/HIPAA compliance, defer to alterlab-research-ethics. Part of the AlterLab Academic Skills suite.
Diagnose ClickHouse Kafka engine health, stuck consumers, thread pool capacity, consumer lag, slow materialized views, and Kafka rack-awareness or cross-AZ traffic. Use for Kafka lag, consumer errors, rebalances, thread starvation, or unexpected cross-AZ Kafka cost.
Use this skill any time the user wants to analyze data, create charts, or build data visualizations. This includes: sales analysis, financial modeling, cohort analysis, funnel analysis, A/B test results, KPI tracking, data reports, revenue breakdowns, user retention analysis, conversion rate analysis, CSV summarization, and dashboard creation. Also trigger when: user says 分析这组数据, 做个图表, 数据可视化, 销售分析, 漏斗分析, 留存分析, 做个数据报表. If data needs to be analyzed or visualized, use this skill.
Audit a database schema, ORM model, or migrations for design and performance — produces two independent 0-100 scores (Design & Integrity + Performance & Scale) plus a prioritized, evidence-backed report. Read-only; never writes or migrates. Use when the user asks to audit, analyze, review, check, or score a database, schema, data model, migrations, indexes, keys, referential integrity, types, constraints, query patterns, partitioning, or connection pooling — for Postgres, MySQL, Mongo, DynamoDB, Cassandra, pgvector, ClickHouse, Neo4j, and friends.
Expert knowledge for Azure Content Understanding in Foundry Tools development, including troubleshooting, best practices, decision-making, architecture and design patterns, limits and quotas, security, configuration, integrations, and coding patterns. Use when designing Content Understanding analyzers, RAG document flows, agentic/RPA workflows, REST/SDK calls, quotas, and other Azure Content Understanding in Foundry Tools development tasks. Not for Azure Speech in Foundry Tools (use azure-speech), Azure AI Search (use azure-cognitive-search), Azure AI Document Intelligence (use azure-document-intelligence), or Azure AI Video Indexer (use azure-video-indexer).
Adds the Azure SQL Database container as a sidecar service in an existing Docker Compose stack or Dev Container. Use when wiring the local Azure SQL Database engine into compose or devcontainer.json, when an app needs a SQL backend via a service name (not localhost), or for prompts like "add SQL to my compose", "add a database service", "depends_on database", "devcontainer SQL sidecar", "compose healthcheck for SQL", "wait for the database before starting the app". Handles automatic architecture selection, the private registry login, the healthcheck wait-until-ready, and a one-shot init service that creates appdb (the engine does not auto-create databases). Not the SQL Server image. Prefer this for any compose or Dev Container SQL wiring.
Forensic financial analysis for A-share companies. Identifies discrepancies, potential irregularities, and earnings quality issues in Chinese financial statements. Adapted from the original break-trace skill for CAS accounting and Chinese market red flags. Triggers on "A股财务核查", "财务异常", "forensic analysis China", "财务舞弊", "earnings quality China", or "investigate [company] financials".
Build professional data packs for A-share investment banking deals — M&A due diligence, IC materials, company profiles. Adapted from the original datapack-builder skill for Chinese data sources (AkShare, 巨潮, exchange filings) and CAS accounting. Triggers on "A股数据包", "投行数据包", "datapack China", "due diligence pack A-share", or "IC data pack [company]".
Update A-share financial models with new quarterly data, management guidance, or macro changes. Reflects actuals, rolls estimates forward, flags material changes, and updates valuation. Adapted from the original model-update skill for Chinese market conventions and AkShare data. Triggers on "A股模型更新", "模型更新", "plug earnings into model", "update model for [company]", "刷新预测", or "更新财务模型".
Populate A-share investment banking pitch deck templates with data from source files. Adapted from the original pitch-deck skill for Chinese companies, A-share market data, and Chinese business context. Triggers on "A股Pitch Deck", "路演PPT", "pitch deck China", "pitch deck A-share", or "fill pitch template for [company]".
Query ClinicalTrials.gov API v2 for trial data. Search by condition, drug/intervention, location, sponsor, or phase; fetch details by NCT ID; filter by status; paginate; export CSV. For clinical research, patient matching, and trial portfolio analysis.
Expert guidance on building Swift database client libraries. Use when developers mention: (1) building a database driver, (2) wire protocol implementation, (3) connection pooling design, (4) state machines for protocol handling, (5) NIO channel handlers for databases, (6) backpressure in result streaming, (7) actor executor alignment with NIO.
Pythonic RDKit wrapper with sensible defaults for drug discovery. SMILES parsing, standardization, descriptors, fingerprints, similarity, clustering, diversity selection, scaffold analysis, BRICS/RECAP fragmentation, 3D conformers, and visualization. Returns native rdkit.Chem.Mol. Prefer datamol for standard workflows; use RDKit directly for advanced control.
Apache Doris production diagnostics router. Use when Apache Doris queries are slow, imports are failing or timing out, compaction is raising -235 errors, nodes are OOM or crashing, materialized views are not rewriting, or tablet/replica health is degraded. Routes to the appropriate doris-debug-* skill. Covers shared-nothing and cloud (storage-compute separation) deployments.
Excel data reading skill based on Alibaba EasyExcel (com.alibaba:easyexcel:4.0.3). Covers listener mode (ReadListener/AnalysisEventListener/PageReadListener), synchronous reading (doReadSync), multi-sheet reading, @ExcelProperty(index/name) matching, multi-row headers (headRowNumber), custom converters for dates and numbers (@DateTimeFormat/@NumberFormat/Converter), listener exception handling (onException + ExcelDataConvertException), reading extra information (comments/hyperlinks/merged cells, extraRead + CellExtra), formulas and cell types (CellData<T>), reading without creating objects (ReadListener<Map<Integer,String>>), and web upload reading (MultipartFile→InputStream). Use this skill when the user needs to parse Excel (import data/bulk insert/reconciliation files/user uploads/Excel to objects/Excel to Map). Not for filling in pre-made templates (use easyexcel-fill instead), and not for programmatically generating Excel (use easyexcel-write instead).
Always invoke when geospatial data must be acquired, prepared, repaired, scaled, or moved through a repeatable pipeline. Covers open-data/OSM/STAC acquisition, spatial formats, CRS transforms, quality checks, and batch ETL architecture for growing or recurring joins. Invoke alongside PostGIS for database execution and alongside SWE standards when code is delivered. Do not trigger merely because another specialist reads analysis-ready data.
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.