Skills
96,981 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Comprehensive Amazon profit analysis and revenue optimization. Revenue waterfall analysis, hidden fee identification, ACoS impact assessment, return cost calculation, and profitability optimization strategies. Use when the user asks about Amazon profit analysis, profitability calculation, fee analysis, margin optimization, or revenue breakdown.
Call Exa Contents directly with cURL or raw HTTP. Use when an agent already has URLs and needs POST /contents without an SDK for extracted text, highlights, summaries, links, image links, subpages, freshness-controlled crawling, or per-URL status handling.
MyBatis and MyBatis-Spring patterns for mapper design, XML and annotation SQL, result mapping, dynamic SQL safety, transactions, batching, pagination, and query performance. Use when building or reviewing Java persistence code with MyBatis, Spring Boot, MyBatis-Spring, or MyBatis-based legacy applications.
Create comprehensive industry and sector landscape reports covering market dynamics, competitive positioning, key players, and thematic trends. Use for client requests, sector initiations, thematic research pieces, or internal knowledge building. Triggers on "sector overview", "industry report", "market landscape", "sector analysis", "industry deep dive", or "thematic research".
AgentDB memory system with HNSW vector search. Provides 150x-12,500x faster pattern retrieval, persistent storage, and semantic search capabilities for learning and knowledge management. Use when: you need to store successful patterns, search for similar solutions, semantically look up past work, learn from previous tasks, share knowledge between agents, or build a knowledge base. Skip when: no learning is needed, tasks are ephemeral and one-off, external data sources are available, or exploration is read-only.
Multimodal AI-based image recognition and analysis. Trigger this skill when users want to analyze or describe images, extract information from image URLs, or ask about image recognition, image analysis, image description, image content understanding, OCR text recognition, or visual Q&A. Also trigger when users mention image recognition, image analysis, image description, identifying image content, analyzing product images, reading text from images, describing images, extracting visual content, or understanding photo content. If users provide an image URL and ask about its visual content, trigger this skill even if they do not explicitly say “image recognition”.
Systematic stock screening and investment idea sourcing. Combines quantitative screens, thematic research, and pattern recognition to surface new long and short ideas. Use when looking for new ideas, running screens, or conducting thematic sweeps. Triggers on "idea generation", "stock screen", "find ideas", "what looks interesting", "screen for", "new ideas", or "pitch me something".
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.
Builds and modifies dbt models, writes SQL transformations using ref() and source(), creates tests, and validates results with dbt show. Use when doing any dbt work - building or modifying models, debugging errors, exploring unfamiliar data sources, writing tests, or evaluating impact of changes.
Amazon Brand Analytics interpretation and strategic insights for Brand Registry owners. Decode Search Frequency Rank (SFR) data, analyze Market Basket patterns, interpret Item Comparison reports, and extract demographic insights to optimize product strategy and advertising spend. Works with Brand Analytics data from all Amazon marketplaces. Requires Brand Registry access. Use when: (1) analyzing Search Frequency Rank data for keyword opportunities, (2) interpreting Market Basket data for cross-sell and bundling, (3) understanding Item Comparison competitive positioning, (4) extracting customer demographic insights, (5) optimizing product portfolio based on customer behavior, (6) building data-driven advertising strategies.
Best practices for Apache Kafka event streaming and distributed messaging. Use when building event-driven architectures, implementing producer/consumer patterns, designing topic partitioning strategies, setting up Kafka Streams, configuring schema registries, or integrating change data capture pipelines.
A Tushare data research skill for natural-language requests in Chinese. It turns requests such as "how has this stock been doing lately", "help me check the earnings trend", "which sector is strongest recently", "what is northbound capital buying", "export a set of market data for me" into executable workflows for data retrieval, cleaning, comparison, filtering, export and brief analysis. Suitable for research on A-shares, indices, ETFs/funds, financials, valuations, capital flows, announcements and news, sector concepts and macroeconomic data.
Interact with the DX (getdx.com) APIs to get and manage information about the Software Catalog, manage Scorecards to track system health, analyze Snapshot survey results, and perform data analysis on Engineering productivity. Also answers questions about DX itself — product concepts, setup/admin procedures (SSO, connectors, backfills), and troubleshooting — by fetching DX's own documentation.
This skill should be used when analyzing CSV datasets, handling missing values through intelligent imputation, and creating interactive dashboards to visualize data trends. Use this skill for tasks involving data quality assessment, automated missing value detection and filling, statistical analysis, and generating Plotly Dash dashboards for exploratory data analysis.
Validate database integrity, test migrations forward and backward, verify schema constraints, manage seed data, detect migration drift, and identify query performance issues. Covers PostgreSQL, MySQL, MongoDB with Prisma, TypeORM, Drizzle, and SQLAlchemy, plus Testcontainers test databases. Use when: "database test," "migration test," "migration rollback," "rollback test," "data integrity," "SQL test," "schema validation," "seed data," "query performance," "Testcontainers." Not for: synthetic data generation/masking at scale — use test-data-management; Docker/IaC test-environment provisioning — use test-environments; SQL injection — use security-testing. Related: test-data-management, test-environments, security-testing, ci-cd-integration.
Complex DAG testing workflows with debugging and fixing cycles. Use for multi-step testing requests like "test this dag and fix it if it fails", "test and debug", "run the pipeline and troubleshoot issues". For simple test requests ("test dag", "run dag"), the airflow entrypoint skill handles it directly. This skill is for iterative test-debug-fix cycles.
Analyze Google Analytics data, review website performance metrics, identify traffic patterns, and suggest data-driven improvements. Use when the user asks about analytics, website metrics, traffic analysis, conversion rates, user behavior, or performance optimization.
Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use for semantic search, pattern matching, similarity queries, and knowledge retrieval. Skip for exact text matching, simple lookups, or when no semantic understanding is needed.
Computer vision engineering skill for object detection, image segmentation, and visual AI systems. Covers CNN and Vision Transformer architectures, YOLO/Faster R-CNN/DETR detection, Mask R-CNN/SAM segmentation, and production deployment with ONNX/TensorRT. Includes PyTorch, torchvision, Ultralytics, Detectron2, and MMDetection frameworks. Use when building detection pipelines, training custom models, optimizing inference, or deploying vision systems.
S2 multi-dimensional cross-analysis table development assistant (Expert Skill). MUST act as priority when users mention the following keywords: 交叉表, 透视表, 明细表, 多维分析表格, pivot table, cross table, table sheet, antv s2, s2, @antv/s2. Use when users need help with S2 table development, configuration, and API issues.
Analyze dashboards and notebooks using Davis analyzers — anomaly detection, novelty scoring, and correlation. Use when the user references a specific Dynatrace dashboard or notebook (by URL, UUID, or name) and asks what it shows, which DQL queries it runs, whether a tile looks off, or wants to find anomalies, score novelty, or correlate its metrics. The trigger is a dashboard or notebook as the data source, not a general DQL question. This skill extracts timeseries queries efficiently without reading the full raw document JSON, then optionally runs Davis analyzers on the extracted metrics. Trigger phrases: "what's wrong on this dashboard", "analyze this notebook", "find anomalies", "novelty score", "correlate metrics", "extract DQL from dashboard", "dashboard URL", "tile", "run-analyzer", "timeseries extraction", "Davis analyzer".
Query Funda AI financial data via two surfaces: the MCP server at https://funda.ai/api/mcp for analyst-grade research synthesis (DCF, comps, earnings previews/recaps, sector deep-dives, SEC filings, transcripts, supply-chain mapping, ownership flow, macro framing) via the agent_chat tool — OR the REST API at https://api.funda.ai/v1 with FUNDA_API_KEY for raw data (real-time quotes, intraday candles, EOD prices, financial statements, options chains/greeks/GEX, supply-chain KG, social sentiment, news, calendars, FRED, ESG, congressional trades, AI hiring signals). Triggers: "funda", "funda.ai", real-time quote, stock price, intraday, balance sheet, income statement, options chain, DCF, comps, earnings preview/recap, analyst estimates, 10-K/10-Q/8-K, transcript, ownership flow, gamma exposure, supply chain, sector deep-dive, congressional trades, FRED. Prefer MCP for synthesis/analysis questions; use REST for raw structured data the MCP declines.
Read the user's local TradingView desktop app through opencli: quotes, options chains with greeks and per-strike IV, options expiries, screener results across stocks, crypto, forex, futures, and bonds, gainers and losers, news headlines and full stories, alerts (active, fired, offline fires), watchlists including colored flag lists, symbol search, chart state, and chart screenshots. Use this skill whenever the user asks for any of these from TradingView ("TV") — for example an options chain, puts, or IV skew for a ticker, an oversold-stock screen, their red flag list, which alerts fired, or a screenshot of their chart. For plain quotes, TA readouts, scans, or futures without the desktop app, prefer tradingview-mcp. Read-only: it never places trades or edits watchlists, alerts, or layouts.
Merges, appends, joins, and deduplicates multiple CSV, TSV, and Excel files (including multi-sheet workbooks) with pandas—mapping mismatched column names to one schema, normalizing keys, resolving conflicting values, tracking which file each row came from, and verifying the row math. Use when the user wants to combine spreadsheets or exports, stack monthly files, consolidate contact or lead lists, VLOOKUP-style join two sheets on an ID or email, or dedupe records across sources, even if they only say “put these together” or “clean up these lists into one.”
Use this skill whenever the user wants to create, customize, or troubleshoot G6 v5 graph or network visualizations. Triggers include any mention of “G6,” “antv g6,” “@antv/g6,” “G6 graph,” “G6 图,” “网络图,” “关系图,” “拓扑图,” “树形图,” “流程图,” “思维导图,” “鱼骨图,” “力导向图,” “force graph,” “network visualization,” “node-edge diagram,” “graph layout,” “tree layout,” “dagre layout,” “mindmap,” or “social network,” as well as requests about G6 node styles, edge types, behaviors, plugins, layouts, combos, or data structures. Also use it to debug G6 rendering errors, v4-to-v5 migrations, or graph interaction issues. Do NOT use it for G2 statistical charts, X6 editor diagrams, or S2 pivot tables.
Apply when deciding whether and how VTEX IO apps should use Master Data v2 for custom data. Covers entity boundaries, schema lifecycle, indexing strategy, and when Master Data is the right storage mechanism versus another data approach. Use for reviews, wishlists, forms, or other custom data modeling decisions in VTEX IO apps.
Amazon keyword rank tracking and performance analysis. Comprehensive ranking factor analysis, change diagnosis, improvement strategy development, and competitive position monitoring. Use when the user asks about keyword rankings, position tracking, ranking changes, or competitive analysis
Use when a task requires writing code to fetch real A-share and related market data—quotes/K-lines/tick data (Tencent daily/weekly/monthly with pre- and post-adjustment, minute lines and same-day ticks; the full-market after-hours package from the official Tongdaxin site; Baidu), research reports (East Money, Sina, Tonghuashun, iwencai), signals (hot topics/northbound flows/Dragon-Tiger List/unlocks/industries/sector fund flows), fund data (margin trading/block trades/shareholder counts/dividends/fund flows/ETF shares), news (CLS/East Money/Wallstreetcn/Xinwen Lianbo), financial statements/F10/historical valuations/ST list, announcements (CNINFO), limit-up trading (limit-up pool/consecutive boards/failed-breakout rate/monitoring pool/unusual moves), ETF options, public opinion and interaction (Hudongyi/SSE e-interaction/trending list), chip distribution, adjustment factors, Shenwan industry changes, macro and rates (social financing/PMI/ChinaBond yield curve/repo fixing rate/LPR/global macro calendar), index constituents/weights/valuations/trading calendar, futures and commodities (daily quotes from five futures exchanges/commodity and equity index options/position rankings/futures daily K-lines including DCE/real-time futures/A50/Shanghai Gold), event-driven data (earnings forecasts/institutional research/share increases and decreases/repurchases/pledges of equity/new share subscriptions), convertible bonds, and other real data. Fifteen layers, 87 endpoints (including 5 backups), 34 sources, all runnable code embedded, self-contained with no external files; prioritizes sources that do not block IPs, such as Tencent and official exchange sources. The East Money interface has built-in rate limiting to prevent blocks; if the primary source is blocked, consult “备用源速查” for fallback. Use only when data interfaces need to be called to retrieve data. Do not load this skill for topics that do not require data retrieval, such as explaining A-share concepts, discussing investment views, or answering strategy questions.
Design and review Elasticsearch index mappings for stated access patterns: correct field types, text+keyword multi-fields, doc_values tuning, mapping-explosion avoidance, and explicit shard settings. Use when creating a new index, reviewing a mapping for storage or query performance, fixing wrong field types, or when the user asks which type to use for search, filter, sort, or aggregation on a field.
Best practices for analytics, data analysis, and visualization using Python, pandas, matplotlib, seaborn, and Jupyter notebooks. Use when performing exploratory data analysis, building data pipelines, creating statistical visualizations, writing Jupyter notebooks, cleaning and transforming datasets, or implementing analytics dashboards.
Diagnose slow Elasticsearch Query DSL searches and propose measured fixes. Use when a search is slow, profile output shows an expensive clause, exact-match filters sit in scoring context, or leading wildcards dominate latency. Ground every recommendation in search profiling: move non-scoring clauses to filter context, eliminate leading wildcards, and re-profile to confirm improvement
Neural pattern training with SONA (Self-Optimizing Neural Architecture), MoE (Mixture of Experts), and EWC++ for knowledge consolidation. Use when: pattern learning, model optimization, knowledge transfer, adaptive routing. Skip when: simple tasks, no learning required, one-off operations.
Queries the 1000 Genomes Project dataset (3,202 whole-genome-sequenced individuals, GRCh38) at the level of individual participants. Use when a question is about individuals or variants in the 1000 Genomes Project cohort: which individuals carry variants matching specific criteria in a gene or region, which individuals are homozygous-reference at a position, which variants exist in the dataset or carried by specified individuals in a gene or region, the relatedness between two specified individuals. Variants are returned with 1000 Genomes allele frequencies (AF), gnomAD v4.1 exome and genome AF, AlphaMissense score, and HGVSp annotations
Dune CLI for querying blockchain and on-chain data via DuneSQL, searching decoded contract tables, managing saved queries, visualizations, and dashboards, and monitoring credit usage on Dune. Use when the user asks about blockchain data, on-chain analytics, token transfers, DEX trades, smart contract events, wallet balances, Ethereum/EVM chain queries, DuneSQL, visualizations, charts, dashboards, or says "query Dune", "search Dune datasets", "run a Dune query", "create a dashboard", or "manage dashboard".
Apply when deciding where and how a VTEX IO app should store and read data. Covers when to use app settings, configuration apps, Master Data, VBase, VTEX core APIs, or external stores, and how to avoid duplicating sources of truth or abusing configuration stores for operational data. Use for new data flows, caching decisions, refactors, or reviewing suspicious storage and access patterns in VTEX IO apps.
Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.
Build composable, responsive React charts with Recharts library. Use when creating data visualizations including line charts, area charts, bar charts, pie charts, scatter plots, and composed charts. Handles chart customization, responsive sizing, tooltips, legends, axes configuration, performance optimization, and accessibility.
Guide for migrating Apache Airflow 2.x projects to Airflow 3.x. Use when the user mentions Airflow 3 migration, upgrade, compatibility issues, breaking changes, or wants to modernize their Airflow codebase. If you detect Airflow 2.x code that needs migration, prompt the user and ask if they want you to help upgrade. Always load this skill as the first step for any migration-related request.
Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, implementing data governance, or troubleshooting data issues.
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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.