Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Fetches cryptocurrency market data, prices, technical analysis, news, and trends using the CoinMarketCap MCP. Use for ANY question involving cryptocurrencies, tokens, or blockchain markets, even if the user doesn't explicitly ask for data. This includes price checks, portfolio questions, market analysis, coin comparisons, holder metrics, technical indicators, and news. Trigger: "bitcoin", "ETH", "crypto", "token price", "market cap", "how is [coin] doing", "/cmc-mcp".
Comprehensive web scraping, crawling, and data extraction toolkit powered by Firecrawl API. Provides scripts for single-page scraping (scrape.py), web search (search.py), URL discovery (map.py), multi-page crawling (crawl.py), structured data extraction (extract.py), and autonomous data gathering (agent.py). Use when you need to: (1) extract content from web pages, (2) search and scrape the web, (3) discover URLs on websites, (4) crawl multiple pages, (5) extract structured data with JSON schemas, or (6) autonomously gather data from anywhere on the web. Requires FIRECRAWL_API_KEY environment variable.
GoldRush Foundational API — REST API for historical and near-real-time blockchain data across 100+ chains. Use this skill whenever the user needs wallet token balances, transaction history, NFT holdings, token prices, token approvals, cross-chain activity, block data, portfolio value tracking, or any on-chain data query via REST. This is the default skill for blockchain data lookups, portfolio dashboards, tax tools, compliance checks, block explorers, and any application that fetches historical or current chain data. If the user needs real-time streaming or WebSocket push data, use goldrush-streaming-api instead. If the user needs pay-per-request access without an API key, use goldrush-x402 instead.
Hardens CockroachDB user privileges by auditing and tightening role-based access control, reducing admin grants, restricting PUBLIC role permissions, and applying least-privilege principles. Use when reducing excessive privileges, cleaning up admin access, or implementing RBAC best practices.
Expert in aggregating, processing, and synthesizing information from multiple sources into coherent insights. Use when building knowledge graphs, ontologies, RAG systems, or extracting insights across documents. Triggers include "knowledge graph", "ontology", "synthesize information", "GraphRAG", "insight extraction", "cross-document analysis".
Manages TLS certificates for CockroachDB clusters including CA certificate configuration, client certificate authentication, certificate rotation, and troubleshooting SSL/TLS connection errors. Use when setting up client certificate auth, resolving SSL connection failures, rotating certificates, or configuring mTLS for CDC changefeeds.
Monitors CockroachDB background job health by identifying failed, paused, and long-running jobs using SHOW JOBS and SHOW AUTOMATIC JOBS. Surfaces schema changes, backups/restores, automatic statistics collection, and SQL stats compaction jobs without DB Console access. Use when investigating schema change delays, failed backups, or automatic job issues.
Convert an Omni Analytics topic into a Databricks Metric View definition in Unity Catalog. Use this skill whenever someone wants to export Omni metrics to Databricks, create a Metric View from an Omni topic, harden BI metrics into Unity Catalog, or bridge Omni's semantic layer with Databricks AI/BI dashboards and Genie spaces.
Tracks quality metrics including defect density, test effectiveness ratio, DORA metrics, and mean time to detection. Use when establishing quality dashboards, defining KPIs, evaluating test suite effectiveness, or reporting quality trends to stakeholders.
Diagnose and optimize existing slow SQL queries using execution plans, indexing strategies, query rewriting, and ORM tuning. Use when the user provides a query, performance symptom, or EXPLAIN plan; use sql-query-generation when creating a new query from requirements.
Analyzes SQL queries for missing indexes, N+1 patterns, suboptimal joins, and full table scans. Interprets EXPLAIN, detects anti-patterns, rewrites queries. Triggers on: "optimize this query", "slow query", "add indexes", "explain plan", "N+1 query", "why is this query slow".
Implement Syncfusion WPF OLAP Client for business intelligence and multidimensional data analysis. Use this when working with OLAP Client controls, cube analysis, SSAS (SQL Server Analysis Services), or multidimensional data. Covers cube database connections (offline cubes, Mondrian, ActivePivot), dimension browsing, OLAP reports, drill-through operations, and KPI analysis.
Implement Syncfusion WPF OLAP Grid for multi-dimensional OLAP data analysis. Use this when working with OLAP grids, multi-dimensional analysis, business intelligence grids, or SSAS data display. Covers OLAP data source connections, drill down/up operations, KPI display, dimensional analysis, and cube data visualization for WPF applications.
Guide for implementing Syncfusion WPF Smart DataGrid (SfSmartDataGrid) with AI-powered natural language commands. Use this when creating AI-assisted data grids with natural language sorting, filtering, grouping, and highlighting in WPF applications. Covers Azure OpenAI integration, data binding, and natural language prompt handling for grid operations.
Яндекс Метрика: traffic breakdown by source and UTM tags, conversion and search engine reports, API segments and counter access. Cache-first approach for context window hygiene. Triggers: яндекс метрика, yandex metrika, metrika analytics, метрика трафик, метрика конверсии, метрика отчёт, создать сегмент, доступ к счётчику, выдать доступ рекламному кабинету.
Generate vector embeddings via 9Router /v1/embeddings using OpenAI / Gemini / Mistral / Voyage / Nvidia / GitHub embedding models for RAG, semantic search, similarity. Use when the user wants embeddings, vectors, RAG, semantic search, or to embed text.
Query FeatureDB read/write usage data from PAI-FeatureStore. Use for analyzing consumption, usage trends, and cost estimation by official pricing. Supports date range queries with breakdowns. Triggers: "FeatureDB read/write volume", "PAI-FeatureStore usage", "FeatureDB usage", "FeatureDB statistics", "Feature View usage", "PAI-FeatureStore billing"
Audits the security posture of a CockroachDB cluster (Cloud or self-hosted) across network, authentication, authorization, encryption, audit logging, and backup dimensions. Use when assessing cluster security readiness, preparing for compliance reviews, or investigating security configuration gaps.
Designs comprehensive backend systems including RESTful APIs, microservices, database architecture, authentication/authorization, caching strategies, message queues, and scalability patterns. Produces API specifications, database schemas, architecture diagrams, and implementation guides. Use when designing backend services, APIs, data models, distributed systems, authentication flows, or when users mention backend architecture, API design, database design, microservices, or server-side development.
Use when planning how many patients or cases a study needs before data collection (power analysis, IRB justification). Walks a decision tree to the right test and returns reproducible R/Python code and IRB-ready justification text. Analyzing collected data is /analyze-stats.
Looker Developer Onboarding: Step 7 (Final Step). Creates a LookML dashboard in the project, imports it as a user-defined dashboard (UDD) in Looker, and iteratively refines it based on user feedback by syncing changes. Only execute this after Step 6 (Model Setup using `creating-lookml-model`).
Provide a lookup index of dbt models (BigQuery tables) to guide query writing against a data warehouse. Use when you need to query, analyze, or look up data in a dbt-powered data warehouse, or when resolving a vague data question into the right BigQuery tables to query.
Execute KQL management commands (table management, ingestion, policies, functions, materialized views) against Fabric Eventhouse and KQL Databases via CLI. Use when the user wants to: 1. Create or alter KQL tables, columns, or functions 2. Ingest data into an Eventhouse (inline, from storage, streaming) 3. Configure retention, caching, or partitioning policies 4. Create or manage materialized views and update policies 5. Manage data mappings for ingestion pipelines 6. Deploy KQL schema via scripts Triggers: "create kql table", "kql ingestion", "ingest into eventhouse", "kql function", "materialized view", "kql retention policy", "eventhouse schema", "kql authoring", "create eventhouse table", "kql mapping"
Inspect the RevOS BigQuery lakehouse: list datasets and tables, introspect table schemas and column types, preview sample rows, assess data layers (bronze/silver/gold), and check data completeness and null rates. Required companion skill for create-dbt-transformations and create-cubes — load before generating dbt models or cube definitions to introspect warehouse columns and types. Use when asked to: explore the lakehouse, list BigQuery tables, inspect a table schema, preview data, check raw source tables, assess data quality, check null rates, understand available data, or perform BigQuery schema introspection.
Query and subscribe to Midnight blockchain data via the Indexer GraphQL API v4. Covers contract state reads, transaction lookups, block queries, real-time subscriptions (contractActions, blocks, unshielded/shielded transactions), state deserialization, the offset/null bug workaround, and TypeScript helper patterns. Use when a user needs to read on-chain state after a transaction, watch contract events in real time, look up blocks or transactions, query unshielded balances, or monitor DUST generation status.
Source the next ML experiment proposal by **reading the audit digest** at `scratch/audit/<stem>/audit.md` (produced by `audit-ml-pipeline` at § 4 record-outcome). For every row in the digest's `## Checks summary` whose `severity` is `issue` or `tip`, follow the row's `documentation_url` to draft a Backlog row whose `Item` is the mitigation the docs recommend. The `## Metrics summary` provides context for the human summary paragraph but does not drive Backlog rows on its own. Returns the enriched Backlog rows + a one-paragraph summary back to `iterate-ml-experiment`, which writes the rows into `JOURNAL.md` and re-presents the sourcing menu so the user can promote a `B<N>` row. Stops at "Backlog enriched, summary returned"; never writes a per-experiment design note, never picks the "winning" finding — the user picks via `B<N>`. TRIGGER when: `iterate-ml-experiment` is picking a sourcing strategy and the user picks `skore` from the menu; the user says "mine the report", "what does skore see?", "fill the backlog from the diagnostic"; the previous experiment has finished and the user wants the report converted into actionable backlog items. SKIP when: the previous experiment hasn't run yet (no audit digest on disk); the user has a concrete modelling idea (use `iterate-from-user`); the task is the *mechanics* of running / opening a report — route to `evaluate-ml-pipeline`; the user wants a narrative read of one specific section of the report (route to `evaluate-ml-pipeline`). HOW TO USE: read the existing `scratch/audit/<stem>/audit.md` digest as text — do NOT re-open the skore Project, do NOT call `report.*` accessors. For each row in the `## Checks summary` section whose severity is `issue` or `tip`, follow the `documentation_url` (via WebFetch) and draft one Backlog row citing `audit:<stem>:checks.<code>`. Dedupe against rows already in `JOURNAL.md` Backlog by source citation. Return the candidate rows + a one-paragraph human summary. The parent skill writes the rows to `JOURNAL.md` and re-shows the sourcing menu.
Source the next ML experiment proposal from the user via one of three entry points selected by `AskUserQuestion`: (a) a scientific article URL the agent must read and synthesize, (b) a resource link or path (GitHub issue / spec file / reference repo), or (c) free-text the user types directly. In every branch, the agent reads the source, synthesizes its understanding of what to implement, and confirms with the user *before* returning the Proposal block. Hand the confirmed Proposal back to `iterate-ml-experiment`, which writes it into `journal/NN_short_name.md` and seeks the user's design-note approval. Stops at "Proposal returned, user-confirmed"; never writes a design note, never authors acceptance criteria. TRIGGER when: `iterate-ml-experiment` is picking a sourcing strategy and the user picks `user` from the menu; the user volunteers a concrete idea ("I want to try X"); the user pastes or links a scientific article, GitHub issue, spec file, or reference repo and asks us to read it. SKIP when: the user wants to mine the previous report (use `iterate-from-skore`); the user is asking for a symbol lookup or pipeline mechanics (use the `python-api` skill); the work is evaluation mechanics on a single report (route to `evaluate-ml-pipeline`). HOW TO USE: open with an `AskUserQuestion` for the entry point — article-link / resource-link / free-text. In each branch: gather the source material with the appropriate tool (`WebFetch`, `gh issue view`, `Read`), synthesize what you understand into the three shaping questions, and **confirm with the user via plain text** (one or two sentences: "you'd like to implement X by changing src/<pkg>/<file>.py — right?") before returning the Proposal. Do not write any design note. Do not author acceptance criteria.
Reviews, audits, and modifies CockroachDB cluster settings. Self-Hosted has full control over all settings and start flags. Advanced/BYOC can modify most SQL-level settings but infrastructure settings are managed by CRL. Standard has limited settings access — session variables are the primary tuning mechanism. Basic has minimal settings — use session variables and Cloud Console. Use when auditing configuration, tuning performance, or troubleshooting settings-related issues.
Detect the divergence where commodity prices rise while holdings of the corresponding physical ETF/trust decline, and cross-validate with multiple indicators to assess whether there are risks of physical supply tightness or delivery pressure.
Design and implement Monte Carlo methods for uncertainty quantification, risk analysis, and probabilistic simulations across scientific and financial domains. Use when "monte carlo, random sampling, uncertainty quantification, risk analysis, stochastic simulation, MCMC, variance reduction, probabilistic, " mentioned.
Search relevance and ranking on OpenSearch/Elasticsearch for a two-sided marketplace — candidate retrieval (hybrid BM25 + kNN, RRF, two-tower EBR), base relevance (BM25F, multi_match, LambdaMART), quality signals (Wilson lower bound, Bayesian average, rank_feature saturation/sigmoid), personalization (listing/user/session embeddings), spatial/temporal decay (gauss/exp), marketplace balance (conversion-weighted ranking, supply fairness, Pareto multi-objective), bias correction (IPS, click models, Thompson sampling), empirical evaluation (judgment sets, NDCG, ablation, A/B sizing, CUPED, regression suites), and diversity (MMR, DPP, max-per-host). Triggers on function_score, rank_feature, script_score, kNN, hybrid query, learning-to-rank, two-sided ranking, exposure fairness, NDCG, A/B testing, judgment set construction, ranking ablation, or "why is my OpenSearch ranking bad". Applies to Elasticsearch too — same APIs.
Use this skill to produce standalone, publication-ready PNG graphics and reproducible matplotlib scripts from tabular data (CSVs or DataFrames). It renders numerical data into formal scientific visualizations—including scatter, line, bar, pie, ring, bubble, tornado, KDE, violin, box, heatmap, histogram, and area charts, plus composite multi-panel figures that combine these types in a single image—for scholarly manuscripts. Only trigger when the final deliverable is an individual image file. Do not use for interactive dashboards or HTML-rendered outputs (Plotly, Streamlit, Quarto, Jupyter notebooks), nor when the request involves building a container document or presentation that includes charts (slide deck, conference poster). It is not for non-data conceptual illustrations like flowcharts, algorithm schematics, or process diagrams. This skill focuses on high-fidelity data rendering into final image files, not presentation design, document layout, or reverse-engineering code from existing screenshots.
Ingest tabular Parquet files into Rerun chunk streams with rerun.experimental.ParquetReader. Read when converting trajectory or sensor tables (LeRobot-style parquet, exported logs) into entities and components — column grouping, timeline/index columns, static columns, and lenses (DeriveLens) that assemble the typed components (Transform3D, Scalars) from the reader's grouped struct/scalar output. Builds on rerun-chunk-processing and rerun-data-model.
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.