Skills
96,981 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
A-share financial data toolkit. Provides scripts to retrieve real-time A-share market quotes, financial indicators, share purchases and sales by directors, supervisors, and senior executives, northbound capital flows, and macroeconomic data (LPR, CPI/PPI, PMI, aggregate financing, M2). Use when investment analysis requires real-time A-share market data. All data sources are free; no API key is required.
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.
Software development engine for coding. A production-grade skills system for web apps and mobile architecture, APIs, databases, web, mobile, AI, security, UX, SaaS, and ERP engineering. Encodes reusable engineering judgment through layered baseline and specialist skills to help generate secure, scalable, maintainable, high-performance software systems
Checks Redis cluster metadata files (nodes-*.conf) for integrity, detecting 7 categories of metadata issues that cause Redis instance startup failures, connection anomalies, and fault alerts. 执行7项检查：slot槽位完整性、master/slave实例个数、文件格式(4个模块)、文件名端口一致性、端口关系、主备关系、myself标记。 Use this skill when the user mentions Redis metadata file checks, nodes-*.conf checks, or Redis cluster configuration checks. Use it whenever the user asks about Redis startup failures or connection anomalies. Trigger: "Redis元数据检查", "Redis nodes检查", "Redis cluster配置检查", "检查Redis元数据", "Redis元数据校验", "Redis启动失败排查"
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.
Implements and trains LLMs using Lightning AI's LitGPT with 20+ pretrained architectures (Llama, Gemma, Phi, Qwen, Mistral). Use when need clean model implementations, educational understanding of architectures, or production fine-tuning with LoRA/QLoRA. Single-file implementations, no abstraction layers.
Strategic guidance for setting up the right data integration -- workflow selection, field mapping, and configuration. Use when the user needs help choosing the right integration approach, wants advice on field mapping, or needs to plan a data integration.
Use when writing, reviewing, debugging, or documenting LanceDB pipelines in Python or TypeScript, especially code that should work across local LanceDB OSS tables and remote LanceDB Enterprise/Cloud tables. Helps avoid non-portable full-table materialization, choose idiomatic query/search patterns, apply LanceDB performance defaults for ingestion, indexing, filtering, and diagnostics, and resolve connections to the remote server for Enterprise-only operations such as jobs.
Add research-powered enrichment columns to Extruct company tables. Use when the user wants to add enrichment columns (e.g. funding, verticals, tech stack) to an existing Extruct table, run column configs from enrichment-design, or monitor enrichment progress. Triggers on: "enrich", "add column", "add data point", "research column", "enrich table", "enrichment", "add a field", "run enrichment", "monitor enrichment".
Manages existing Grafana dashboards via gcx: list, search, get, audit panels, queries, datasources, and variables; apply a finished manifest; delete; versions and restore; pull/push/validate files; folders; PNG snapshots. Not for new dashboards, panels, or variables (use create-dashboard).
MongoDB schema design patterns and anti-patterns. Use when designing data models, reviewing schemas, migrating from SQL, or troubleshooting performance issues caused by schema problems. Triggers on "design schema", "embed vs reference", "MongoDB data model", "schema review", "unbounded arrays", "one-to-many", "tree structure", "16MB limit", "schema validation", "JSON Schema", "time series", "schema migration", "polymorphic", "TTL", "data lifecycle", "archive", "index explosion", "unnecessary indexes", "approximation pattern", "document versioning".
When the user wants to solve single-period inventory problems, optimize stocking levels for perishable goods, or make one-time purchase decisions under demand uncertainty. Also use when the user mentions "newsvendor model," "newsboy problem," "single-period inventory," "perishable inventory," "overage and underage costs," "critical fractile," "fashion goods inventory," "seasonal products," "service level optimization," or "demand distribution matching." For multi-period problems, see dynamic-lot-sizing or stochastic-inventory-models.
(NS) GraphRAG process. Use for GraphRAG, knowledge graph over documents, multi-hop retrieval, entity/relationship extractors, indirect-link analytics, or grounded structured extraction — even if they only say RAG, NER, knowledge graph, or cited Q&A. Do NOT use for Postgres schema/mode alone (`ns-postgres-rag` first). Do NOT use for competing vector stores, free graph QL, or co-occurrence-as-edge.
Query blockchain data across 130+ networks with SQD Portal (EVM, Solana, Substrate, Bitcoin, Tron, Hyperliquid) and choose the right execution path: Portal MCP for bounded answers, Portal Stream API/curl for raw exports, or Pipes/Squid for durable pipelines. Use when the user asks for on-chain logs, transactions, traces, instructions, fills, or a Portal dataset name, needs a reproducible curl/NDJSON export, or asks to be set up for SQD Portal (connect the Portal MCP server, configure a Portal API key).
PostgreSQL query optimization, JSONB operations, advanced indexing strategies, partitioning, connection management, and database administration. Use this skill for PostgreSQL-specific optimizations, performance tuning, replication setup, and PgBouncer configuration.
Build services on PostgREST-compatible BaaS (Supabase / InsForge) — RLS, Auth, PostgREST queries, MCP settings. This skill MUST be loaded before writing any RLS policy or PostgREST query, and MUST NOT be skipped because the query looks like ordinary SQL. Triggers on: 用 Supabase / InsForge 做後端, 設計 RLS policy, 寫 PostgREST query, 處理 BaaS Auth, 設定 MCP, 推 Supabase production, 自架 Supabase, InsForge.
Set up and conduct media monitoring to track brand mentions, sentiment, and share of voice across news, social, and online channels. Use this skill when the user needs to track what's being said about their brand, monitor competitors' media presence, detect emerging PR issues early, or measure campaign reach — even if they say 'what are people saying about us', 'monitor our brand mentions', 'track competitor PR', or 'set up media alerts'.
When the user wants decision support systems, recommendation engines, or prescriptive analytics. Also use when the user mentions "decision optimization," "recommendation system," "what should we do," "action recommendations," "decision support," "intelligent recommendations," "automated decisions," or "prescriptive models." For pure optimization, see optimization-modeling. For forecasting, see demand-forecasting.
Protein Dynamics, Evolution, and Structure analysis. Specialized in Normal Mode Analysis (NMA) using Anisotropic (ANM) and Gaussian Network Models (GNM). Features tools for structural ensemble analysis, PCA, and co-evolutionary analysis (Evol). Use for protein flexibility prediction, collective motions, structural ensemble comparison, hinge region identification, binding site analysis, MD trajectory filtering, and evolutionary analysis.
Use when the user needs to understand, diagnose, or prevent Protheus record locks and deadlocks -- covering RecLock/MsUnlock/SoftLock/LockByName semantics, BeginTran/EndTran transaction scoping, lock leak and deadlock diagnostics (TopMemoStatus, MonitorActiveLocks, DBAccess monitor, SQL Server/PostgreSQL/Oracle lock queries), and prevention patterns (BEGIN SEQUENCE/RECOVER, lock ordering). Also triggers on Portuguese phrasing like "registro travado", "trava de registro", "deadlock", "erro de lock", "travamento no Protheus", "quem esta travando o registro", or "RecLock nao libera".
Execute read-only SQL queries against PostgreSQL databases. Use when: (1) querying PostgreSQL data, (2) exploring schemas/tables, (3) running SELECT queries for analysis, (4) checking database contents. Supports multiple database connections with descriptions for auto-selection. Blocks all write operations (INSERT, UPDATE, DELETE, DROP, etc.) for safety.
Plan safe database schema evolution and zero-downtime change rollout for Kotlin plus Spring systems using Flyway, Liquibase, or equivalent migration tooling. Use when changing tables, columns, constraints, indexes, or data shape in systems with live traffic, rolling deploys, large datasets, or backward-compatibility requirements between old and new application versions.
Guide users to build Zaps that use the official ScraperAPI Zapier integration. Use this skill whenever the user wants to add web scraping, SERP data, or e-commerce data to a Zapier workflow through ScraperAPI — even when they don't say "skill" or name the integration. Triggers include: "build a Zap that scrapes Amazon prices and emails me", "connect ScraperAPI to Zapier", "automate a daily Google search scrape in Zapier", "use ScraperAPI in a Zap to enrich new leads from a webhook", "how do I add ScraperAPI as a step in my Zap", "Zapier workflow to crawl a site with ScraperAPI", "extract data from a URL inside Zapier". Produces a step-by-step build guide for the Zap editor, since Zapier integrations are assembled in the web UI (not imported as JSON). Note: Transmits user-supplied URLs, queries, and page content to ScraperAPI.
Guide users through connecting to a ScyllaDB Cloud cluster, choosing the correct driver and version, and configuring authentication. Use this skill when a user needs to connect to ScyllaDB Cloud, pick a CQL driver, check a driver version, configure credentials, set the default consistency level, size the connection pool, or troubleshoot connection issues. Cluster management through an AI agent goes to scylladb-cloud-mcp. Triggers on "connect to ScyllaDB Cloud", "ScyllaDB connection", "ScyllaDB driver setup", "which driver", "driver version", "Java driver", "Python driver", "CQL connection", "DC-aware load balancing", "ScyllaDB credentials", "connection bundle", "shard-aware", "tablet-aware", "consistency level", "LOCAL_QUORUM", "too many connections".
World-class computer vision skill for image/video processing, object detection, segmentation, and visual AI systems. Expertise in PyTorch, OpenCV, YOLO, SAM, diffusion models, and vision transformers. Includes 3D vision, video analysis, real-time processing, and production deployment. Use when building vision AI systems, implementing object detection, training custom vision models, or optimizing inference pipelines.
A world-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.
Process-based discrete-event simulation framework in Python. Use this skill when building simulations of systems with processes, queues, resources, and time-based events such as manufacturing systems, service operations, network traffic, logistics, or any system where entities interact with shared resources over time.
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.