Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Dereplicate a putative natural product and assign its chemical taxonomy. Use to answer "is [compound] a known natural product", "what microbe/organism produces [compound]", "what chemical class is [compound]", "dereplicate this metabolite (by formula/exact mass/InChIKey/SMILES)", or "classify this molecule into ChemOnt". Searches NPAtlas for known microbial natural products (producing organism + literature reference), assigns the ChemOnt kingdom→superclass→class→subclass hierarchy via ClassyFire, resolves systematic IUPAC names to structure via OPSIN, and cross-references identity in PubChem. NOT for general drug/compound identity or ADMET (use tooluniverse-chemical-compound-retrieval / tooluniverse-small-molecule-discovery) and NOT for metabolomics pathway/enrichment analysis (use tooluniverse-metabolomics skills).
Use when building with the yahoo-finance2 TypeScript/Deno/npm library, using its Yahoo Finance data modules, CLI, or MCP server, or contributing to the yahoo-finance2 repository including modules, validation schemas, cached fixtures, and npm/JSR build output.
Audit Amazon Payments report for fee errors. Finds incorrect FBA fulfillment fees (wrong size tier), missing referral fee credits on refunds, duplicate charges, and FBA storage discrepancies, and produces an FBA fee-discrepancy case packet per finding. Use when a user asks about Amazon fee errors, overcharged fees, wrong size tier, missing referral refunds, fee audit, or Payments report analysis. Trigger phrases: "fee audit", "overcharged", "wrong fee", "size tier error", "referral fee refund", "payments report", "FBA fee dispute". Works with zero tools. the user pastes Payments report rows.
Plan Amazon FBA inventory end to end. Calculates reorder timing and quantity, safety stock, week-by-week days of cover, builds a 12-week PO schedule, applies ASIN-level restock limits and seasonal multipliers, watches the IPI score levers, and surfaces stranded and aged inventory. Use when a user asks about restocking, when to reorder, how much to order, running out of stock, the IPI score, restock limits, ASIN restock limit, days of cover, 90-day forecast, restock schedule, long-term storage fees, aged inventory, or stranded inventory. Trigger phrases. "inventory management", "when to reorder", "restock", "safety stock", "IPI score", "stockout", "aged inventory", "long-term storage", "reorder schedule", "forecast", "days of cover", "ASIN restock limit". Works with zero tools. the user provides sales rate and lead times.
Analyze true Amazon profitability per ASIN and across a portfolio. Goes beyond per-unit fees to include advertising, returns, storage, promotions, and hidden fees, and finds where the real money is made and lost. Use when a user asks about profitability, true net profit, which products make money, why profit is lower than expected, portfolio profit analysis, or hidden fees eating margin. Trigger phrases: "profit analysis", "true profit", "net margin", "which products are profitable", "where is my money going", "hidden fees". Works with zero tools. the user pastes revenue, fee, and cost figures.
Parses Amazon Search Term Reports (50K+ rows) into 4 actionable buckets: negative exacts, negative phrases, exact-match harvest, and hero scale terms. Produces a Bulk Operations upload-ready file in minutes instead of the eyeball approach that misses 60% of opportunities. Use when a user mentions STR, PPC bulksheet, or search term analysis. Trigger phrases: "search term report", "STR analysis", "PPC bulksheet", "negative keywords bulk upload", "harvest exact match". Works with zero tools.
When the user wants to set up, improve, or audit analytics tracking and measurement. Also use when the user mentions "set up tracking," "GA4," "Google Analytics," "conversion tracking," "event tracking," "UTM parameters," "tag manager," "GTM," "analytics implementation," "tracking plan," "how do I measure this," "track conversions," "attribution," "Mixpanel," "Segment," "are my events firing," or "analytics isn't working." Use this whenever someone asks how to know if something is working or wants to measure marketing results. For A/B test measurement, see ab-testing.
Performs advanced CLI operations, structured data transformation, and Unix forensics with modern Rust-powered tools. Use when searching codebases with ripgrep, transforming JSON or YAML with jq, refactoring across files with sed and fd, debugging APIs with httpie, or navigating directories with zoxide.
API reference for CoinMarketCap DEX endpoints including token lookup, pools, transactions, trending, and security analysis. Use this skill whenever the user mentions DEX API, asks about on-chain token data, wants to look up tokens by contract address, needs security/rug risk checks, or is building DEX integrations. This is the definitive reference for CMC DEX API questions. Trigger: "DEX API", "token by contract address", "CMC security API", "liquidity pool API", "/cmc-api-dex"
Chanmama e-commerce data queries: influencers (find influencers/streamers/KOLs/influencer filtering/follower profiles/sales data/sales rankings/what XX sold), products (bestseller rankings/hot products/how well this product sells/selling influencers/user reviews/product analysis), shops (shop data/shop sales/related products/sell-through analysis), brands (XX brand market performance/market share/competitor comparison/brand sales trends), livestreams (livestream room data/viewer counts/chat analysis/livestream products/livestream sales), videos (viral videos/trending videos/Qianchuan creatives/video sales data/video rankings), and categories (category trends/market analysis/category data/attribute analysis). Use this Skill when the user asks about Douyin data or Douyin e-commerce data.
Pick the right way to represent a dataset so a reader gets the finding in three seconds — a catalog of 20+ chart and diagram forms with when-to-use and failure modes, plus the encoding decisions that make any of them readable (takeaway headline, direct labels, kill the axis, highlight-and-mute, show the caveat). Style-agnostic — use with whatever palette or design system the destination already has. Use when charting survey results, benchmark data, usage metrics, or research findings for a post, brief, deck, or report, and whenever the default bar chart feels like it's burying the point.
Estimates Fabric capacity cost before a migration by profiling Spark, SQL, Power BI, and Real-Time workloads, then recommending SKU size and comparing Reserved, Pay-As-You-Go, and Autoscale billing. Use only for capacity sizing and pricing questions, not for general migration or connectivity work.
Use this skill to migrate course catalog data from external sources (CSV, PDF, website) and bulk-create Learning and LearningCourse records in Education Cloud. Triggers when a user wants to import courses, parse course catalog documents, or set up a course catalog for an academic term. Handles multi-source data ingestion, intelligent interpretation of course structures (lecture+lab linked courses via IsLinkedOnly flag, credit hours, course types), batch creation via sObject REST API, data quality validation, conflict resolution for existing courses, and gap identification. Explains what's missing (descriptions, types) and requests additional sources. Pauses for clarification on multi-component courses and conflicting data. DO NOT TRIGGER for creating individual Learning Course records manually, for creating Learning Programs (different workflow), or for enrollment/registration workflows.
Analyze and explain one or two EVM transactions using live Etherscan data and human-verifiable Etherscan evidence links. Use when a user provides a transaction hash or asks what happened, why a transaction failed, which contracts or internal calls were involved, where assets moved, whether a proxy implementation executed, which call reverted, or why two transactions behaved differently. Reconstruct the supported execution path, decode calls and events, summarize asset and permission changes, showcase the relevant Etherscan transaction, contract, token, label, log, and internal-transaction views, and provide plain-English, developer, support, or security-focused explanations with confidence and limitations. Do not use for broad wallet investigations, multi-hop laundering traces, or full contract audits.
Use when looking for research topics a longitudinal cohort database can answer (NHIS, UK Biobank, an institutional EMR or registry). Profiles the cohort, matches PI expertise, scans literature saturation and returns ranked topic proposals with gap evidence.
Build and deploy Apache Flink user-defined functions (UDFs) in Java for stream processing over Kafka. Use this skill when users want to create scalar UDFs, user-defined table functions (UDTFs), or process table functions (PTFs) in Java, deploy them to Confluent Cloud or local Docker environments, and invoke them from Flink SQL or the Table API. Trigger on: Flink UDF, custom Flink function, process table function, PTF, UDTF, Flink user defined, extend Flink SQL, stateful stream processing with Flink. Do NOT trigger for: Kafka Streams UDFs (use kafka-streams-programming skill), general Flink job development without custom functions, CDC streaming data piplines that include Flink (prefer the confluent-cloud-cdc-tableflow skill), Flink connector setup, or Kafka producer/consumer code.
Use when building database queries with frappe.qb in Frappe v14-v16. Covers the PyPika-based query builder: SELECT, INSERT, UPDATE, DELETE, joins, aggregation, subqueries, cross-DB compatibility (MariaDB/PostgreSQL), and migration from raw SQL. Prevents SQL injection and DB-specific bugs. Keywords: frappe.qb, query builder, DocType, Field, pypika, join, frappe.qb example, how to write query, join tables, SQL replacement, parameterized query, aggregate, ImportMapper, cross-database, parameterized query.
Manage the SAP AI Core Context Registry — data destinations (HDL connections), tabular artifacts (CSV/Parquet/Delta files as queryable tables), and scenario configurations (named artifact bundles). Also interactively build and send tabular prediction requests using the SAP AI Core Tabular Prediction service (sap-rpt-1-small / sap-rpt-1-large). Use this skill whenever the user mentions context registry, data destinations, tabular artifacts, scenario configurations, TCR, HDL connections, or wants to register/list/delete structured data sources in SAP AI Core. Always use this skill for any tabular-orchestration / tcr API work, even if the user just asks "show me my data destinations" or "register this CSV as a tabular artifact". Also use for: SAP AI Core tabular prediction, sap-rpt prediction, "predict a column with sap-rpt", TCR row classification. NOT FOR general orchestration, generic ML prediction, or LLM pipelines — use `genai-hub-orchestration` for those. NOT FOR general classification/prediction requests unrelated to SAP AI Core TCR (e.g. "predict sales next quarter", "classify this text").
Scrape LinkedIn profiles using the user's Chrome profile. Use when asked to find leads, scrape LinkedIn profiles, extract contact data from LinkedIn, or build prospect lists. Triggers include "find founders on LinkedIn", "scrape this LinkedIn profile", "get LinkedIn data for these people", "build a lead list from LinkedIn".
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.
A/B test evaluation, cohort retention analysis, funnel metrics, and experiment-driven product decisions. Use when analyzing experiments, measuring feature adoption, diagnosing conversion drop-offs, or evaluating statistical significance of product changes.
Implements Syncfusion ASP.NET Core Chart (SfChart) for data visualization. Use this when building charts, visualizing time-series or categorical data, or creating dashboards. Covers series configuration (line, bar, pie), axes, tooltips, legends, and customization for ASP.NET Core applications.
Inspect, run, and debug RevOS actions—the operations an integration performs per table row (sync a company to NetSuite, enrich a contact, push a deal to HubSpot)—and their runs through the RevOS MCP server. Use this whenever something failed, did not sync, or came out wrong ("why didn't this deal reach NetSuite?", "the sync is red", "what does this error mean?"), when the user wants to see which actions exist or what an action column did, wants an action run or re-run for some or all rows, or wants one record synced right now from an id or a link ("sync this deal", "push this company now")—even if they never say the word "action". Read it before the first api_read or api_write on the `action-runs` resource. Syncing a source system into RevOS ("sync HubSpot", "is our data up to date?") is the `connections` skill.
Turn Amazon Brand Analytics reports into decisions. Reads Search Query Performance, Search Catalog Performance, Market Basket, Repeat Purchase, and Top Search Terms exports, and converts them into ranked actions. Use when a user pastes or describes Brand Analytics data, asks about Search Query Performance, SQP, market basket analysis, search frequency rank, click share, conversion share, repeat purchase behavior, or wants to know what their Brand Analytics is telling them. Trigger phrases: \"brand analytics\", \"SQP\", \"search query performance\", \"market basket\"、\"click share\"、\"conversion share\"、\"repeat purchase\". Works with zero tools. the user pastes the report data.
Compare Amazon inbound shipment placement options (minimal-split vs Amazon-optimized split, partial-split, optional unified inventory) given SKU dimensions, units, and destination forecast. Returns the lowest landed cost per unit. Use when a user asks about STA (Send to Amazon), inbound placement fees, shipment splits, fulfillment center routing, or inbound shipping optimization. Trigger phrases: \"STA\", \"inbound placement\", \"shipment split\", \"placement fee\", \"fulfillment center routing\". Works with zero tools.
Estimate Amazon monthly sales and revenue from a Best Seller Rank, and size a niche or competitor from observable signals. Explains the BSR-to-sales relationship, adjusts by category and price, and returns a sales range with a confidence note. Use when a user asks how many units a product sells, to estimate sales from BSR, size a market or niche, gauge a competitor's volume, or judge demand. Trigger phrases: "sales estimate", "how many units", "estimate from BSR", "best seller rank", "market size", "competitor sales", "monthly sales". Works with zero tools. the user provides BSR, category, and price.
Look up official company data from European public registries across 11 countries/regions (CZ, SK, PL, DE, UK, NL, RO, HR, SE + EU-level + ESG). Covers company registration, ownership, financial filings, VAT status, ESG data. Use when the user asks to "look up a company", "check registry", "find company info", "look up IČO/KRS/LEI/CRN", "company due diligence", "check VAT status", "find ownership structure", or needs official data from European registries. Reads tracked companies from data/companies.json. Some lookups use Python scripts (stdlib), some fall back to Apify actors for scraping-based registries.
Provides expertise on Chroma vector database integration for semantic search applications. Use when the user asks about vector search, embeddings, Chroma, semantic search, RAG systems, nearest neighbor search, or adding search functionality to their application.
Extract structured data from clinical notes with span-level provenance and null-safety. Use when users say "extract [variables] from this note", "abstract this chart", "pull structured data from these notes", "what does this note say about [field]", or when building a chart-abstraction, registry, or cohort dataset from unstructured clinical text.
Workload-aware architecture design for Apache Doris. MUST USE when designing data architectures, choosing between data models, planning ingestion strategies, sizing clusters, or translating business requirements into Apache Doris system designs. Complements doris-best-practices with decision frameworks and sizing-first workflow. Use when user describes a workload involving: IoT, sensor data, telemetry, real-time analytics, dashboard, log analysis, log search, CDC sync, time-series, device monitoring, point query service, ad-hoc analytics, lakehouse federation, ETL/ELT pipeline, report analytics, clickstream, user behavior, observability, metrics, fleet tracking, or any OLAP workload requiring table design from scratch. Also triggers on prompts like: "design a table for...", "how should I store...", "build an architecture for...", "we have X devices sending data every Y seconds", "recommend a cluster size for...", "what data model should I use for...", "we need to ingest X GB/day", "migrate from MySQL/PostgreSQL to Apache Doris". Also use for legacy analytics/search/serving stack consolidation prompts even when Apache Doris is not named explicitly, including replacing or migrating from Impala, Kudu, Elasticsearch/ES, Greenplum, Presto, HBase, Hive, Hadoop, Redis, or Lambda-style multi-engine data platforms.
DCF (Discounted Cash Flow) valuation for US stocks using SEC EDGAR financials. Use when asked to: estimate intrinsic value or fair value of a US stock; determine if a stock is overvalued or undervalued; run a discounted cash flow analysis; calculate upside/downside from current price. Triggers: "what is AAPL worth", "DCF MSFT", "is NVDA overvalued", "intrinsic value of GOOGL", "fair value Tesla".
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.