Skills
97,222 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Deploy Django on Google App Engine Standard with Cloud SQL PostgreSQL. Covers Unix socket connections, Cloud SQL Auth Proxy for local dev, Gunicorn configuration, and production-ready settings. Use when deploying Django to App Engine, configuring Cloud SQL PostgreSQL, setting up Unix socket connections, or troubleshooting "No such file or directory", "connection refused", or "FATAL: password authentication failed".
Retrieve ESG benchmark comparison metrics by sector using Octagon MCP. Use when comparing ESG performance across industries, analyzing sector-level sustainability benchmarks, identifying ESG leaders and laggards by industry, or referencing frameworks like MSCI, S&P Global, CDP, and CSRD.
Aggregates the top 10 trending topics across the web using hourly data from seven major platforms, including Douyin, Weibo, Bilibili, Kuaishou, Zhihu, Toutiao, and Baidu. Supports browsing trends from the past seven days. Does not support queries for specific topics.
Manage Alibaba Cloud RDS Supabase (RDS AI Service 2025-05-07) via OpenAPI. Use for creating, starting/stopping/restarting instances, resetting passwords, querying endpoints/auth/storage, configuring auth/RAG/SSL/IP whitelist, and listing instance details or conversations.
Retrieve recommended Douyin account growth data. Query categorized daily, weekly, and monthly rankings of Douyin accounts by follower growth, including total followers, follower growth rate, and follower increase. Use when users need to check Douyin follower-growth rankings, analyze growth trends across fields, review follower growth data, or get operational insights.
Foundry IQ knowledge bases. WHEN: make local or Blob documents searchable; create/diagnose KBs; triage unsupported connectors or multi-source KB creation/reconfiguration (even with classic-search terms); connect existing KB to agents (including multi-source KBs); create/reuse a Search service; retrieve from an existing knowledge base with citations; no brand words needed. Read its procedure before query/target questions. NOT: other KB providers, repository-file search, classic Azure AI Search index/query/app work with no Foundry IQ KB or connector request, generic agent creation.
Run a full McKinsey-level market research and strategy analysis using 12 specialized prompts. Use when: market research, competitive analysis, business strategy, TAM analysis; customer personas, pricing strategy, go-to-market plan, financial modeling; risk assessment, SWOT analysis, market entry strategy, comprehensive business analysis; or Arabic requests such as بحث سوق, تحليل استراتيجي, تحليل منافسين, دراسة جدوى, خطة عمل, “حلل لي السوق” for business entry or investment decisions. Do not use when: the user wants a quick opinion on a business idea (just answer directly); product recommendations or shopping (use personal-shopper); content strategy for social media (use viral-equation); simple web search for company info (use web_search directly); comparing products to buy (use personal-shopper); briefly analyzing a single competitor (just answer directly). Edge cases: “حلل لي السوق” for a specific product to buy → personal-shopper, not this skill; for business entry → this skill. “وش أفضل منتج” → personal-shopper. “وش حجم سوق X” → this skill. “قارن لي بين منتجين” → personal-shopper. “قارن لي بين شركتين” as competitors → this skill. “دراسة جدوى مشروع” → this skill. “أبغى أفتح مشروع” → this skill (full analysis). “أبغى أشتري لابتوب” → personal-shopper (purchase, not business). Inputs: business description, industry, target customer, geography, financials (optional). Tools: sessions_spawn (sub-agents), web_search, web_fetch. Output: complete strategy report saved to artifacts/research/{date}-{slug}.html. Success: the user gets 12 consulting-grade analyses synthesized into one actionable report.
Complete pricing for European and American options. 9 methods: Black-Scholes, Binomial CRR, Trinomial, Monte Carlo (antithetic) + Longstaff-Schwartz, Bjerksund-Stensland 2002 / BAW (American closed-form), Heston 1993 (stochastic volatility, smile via Fourier), Bates 1996 (Heston + Merton jumps, crash risk), greeks (BS), implied vol, P(ITM), and P(Profit). Designed for backtesting: every function is flat vectorized Python with numpy (no abstractions), uses math.erfc (not scipy). BS 2.4 us/op, BS2 3.6 us, Heston 400 us, Binomial N=500 5.6 ms. CLI with 15 modes plus validate and bench. O(1) time complexity for all closed-form methods.
Modelar, consultar, migrar e operar PostgreSQL com integridade, índices, concorrência, segurança, performance e recuperação. Use para schemas, SQL, EXPLAIN, locks, isolation levels, migrations, roles, backup ou tuning em Postgres; não use para outro banco relacional sem confirmar a semântica específica, e não use para RLS/Auth/Realtime de Supabase — combine com `specsfy-specialist-supabase` nesse caso.
Expert knowledge for Azure Cosmos DB development, including troubleshooting, best practices, decision-making, architecture and design patterns, limits and quotas, security, configuration, integrations, coding patterns, and deployment. Use for NoSQL/Mongo/Cassandra/Gremlin/PostgreSQL APIs, change feed, vector search, multi-region HA, bulk operations, and other Azure Cosmos DB development tasks. Not for Azure Table Storage (use azure-table-storage), Azure SQL Database (use azure-sql-database), Azure SQL Managed Instance (use azure-sql-managed-instance), or Azure Virtual Machines (use azure-virtual-machines).
sprint.migration module — Version classes (up/down), HelperManager helpers, builders, migrate.php CLI, migrations.*.php configs. Use when writing or applying schema/content migrations, exporting iblocks/HL/options, or checking migration status.
Expert in graph database design and development with deep knowledge of graph modeling, traversals, query optimization, and relationship patterns. Specializes in SurrealDB but applies generic graph database concepts. Use when designing graph schemas, optimizing graph queries, implementing complex relationships, or building graph-based applications
Write and revise queries with the published HelixDB TypeScript SDK (`@helix-db/[email protected]`, Node.js 20+). Use for `readBatch`, `writeBatch`, traversal builders, direct `toQueryRequest`/`toQueryJson` payloads, projections, indexes, vector and BM25 search with traversal-scoped prefiltering, structured remote errors, and `Client.query`. Stored routes, registration, and query bundles are not v3 SDK APIs. When the target is Helix Cloud, always use helix-mcp first.
Post-mortem on what the user has already published - which posts actually worked, why, and what to stop doing. Use when the user pastes their LinkedIn analytics or past posts and asks "what's working", "why did this flop", "read my analytics", "audit my content", or wants to know what to double down on.
Expert-level Redis for caching, pub/sub, data structures, and high-performance applications. Use when the user mentions cache, pub/sub, in-memory stores, key-value stores, or NoSQL, or when the task involves Data Structures, Basic Operations, Advanced Patterns, or Redis Streams.
Delivers the owner's custom recurring report pack on a set cadence — runs the saved report definition from report-builder, wraps it in a business-pulse snapshot so the numbers have context, and hands over one chat summary plus the workbook. Defined once, then delivered on schedule without being asked again. Runs on any single connected data source, or entirely from an uploaded CSV or XLSX. Use it when the owner asks for their report pack, their weekly or monthly numbers, "send me the usual report," "run my report pack," "set this up to come every Monday," or "I want these numbers on a schedule."
TCGA/GDC cancer genomics analysis — cohort construction, clinical metadata retrieval, somatic mutation frequencies, survival analysis, and multi-omics integration. Use for TCGA-BRCA-style cohort studies, mutation prevalence by cancer type, survival-by-mutation analysis, and pan-cancer driver discovery. Always cancer-type-specific (don't use pan-cancer counts without cohort context)
Retrieve comprehensive year-over-year growth in key financial metrics including Revenue, Gross Profit, Operating Income, Net Income, EPS, and Free Cash Flow. Use when analyzing overall company financial performance trends across income statement and cash flow
Answers one question for an SMB owner — is the growth engine actually working? Pulls sales trend by channel, funnel conversion, campaign return, best and worst performing products, and customer sentiment from whatever is connected, then ends with the three growth actions worth taking this week. The growth-side twin of business-pulse, which covers the finance side. Use this whenever the owner asks how marketing or sales is performing, whether a campaign paid off, where leads are coming from, why the pipeline is stalling, which products are moving, or says anything like "is my marketing working," "growth check," "how's the funnel," or "where should I put my money next month." Reach for it even when the owner names a single channel rather than growth overall.
Design and operate an advanced AI agent memory system with the published HelixDB v3 SDKs using hybrid graph plus exact traversal-prefiltered vector and BM25 search. Use for long-term memory, profiles, RAG, recall, extraction, deduplication, consolidation, versioning, forgetting, categorisation, or ingestion. TypeScript-first (`@helix-db/[email protected]`); a Rust v3 variant is in EXAMPLES.rust.md.
Retrieve and analyze Amazon product reviews by ASIN across 15 marketplaces, including the US, with filtering by star rating. Triggers when users mention Amazon reviews, US reviews, Amazon.com reviews, product feedback, buyer complaints, negative or positive reviews, star ratings, review analysis or sentiment, product improvement suggestions, Vine reviews, verified purchase reviews, competitor review research, or customer feedback. Also trigger when a request involves reading, filtering, or analyzing Amazon product reviews, even if the user does not explicitly say “reviews”.
Delivers the weekly growth briefing as one merged read — whether the growth engine is working, what customers are saying in public and private, what competitors actually changed, and the three growth actions worth taking this week. The growth twin of the Monday brief, chaining growth-pulse and review-reputation and adding a web-native competitor scan, merged into a single page rather than three reports. Offers to run on a weekly cadence once, and sets it up on a yes. Use this whenever the owner wants a recurring or one-off growth check, including phrasings like "how's marketing doing," "growth check for the week," "give me my Monday growth brief," "what should I be doing about growth this week," "are we winning or losing out there," or "set up a weekly marketing update." Reach for it when the owner asks how the business is growing rather than how it is paying its bills.
This skill should be used when working with memory-lancedb-pro, a production-grade long-term memory MCP plugin for OpenClaw AI agents. Use when installing, configuring, or using any feature of memory-lancedb-pro including Smart Extraction, hybrid retrieval, memory lifecycle management, multi-scope isolation, self-improvement governance, or any MCP memory tools (memory_recall, memory_store, memory_forget, memory_update, memory_stats, memory_list, self_improvement_log, self_improvement_extract_skill, self_improvement_review)
Define, track, and analyze product metrics with frameworks for goal setting and dashboard design. Use when setting up OKRs, building metrics dashboards, running weekly metrics reviews, identifying trends, or choosing the right metrics for a product area
Debug and inspect PostHog implementations on any website. Use this skill when a user wants to understand how PostHog is implemented on a page, troubleshoot tracking issues, verify configuration, check what events are being sent, or audit a PostHog setup. Works with Chrome DevTools MCP and Playwright MCP to inspect live websites.
Use when writing Spark jobs, debugging performance issues, or configuring cluster settings for Apache Spark applications, distributed data processing pipelines, or big data workloads. Invoke to write DataFrame transformations, optimize Spark SQL queries, implement RDD pipelines, tune shuffle operations, configure executor memory, process .parquet files, handle data partitioning, or build structured streaming analytics.
Write correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, translating between dialects, or building complex analytical queries with CTEs, window functions, or aggregations
Leader view—roll up a team’s pipeline by rep and stage, flag at-risk deals, and surface coaching moments. Use when the user asks “show my team’s pipeline,” “team forecast,” “prep for my team’s pipeline review,” or “where does my team need help.”
Direct REST API access to UniProt. Protein searches, FASTA retrieval, ID mapping, Swiss-Prot/TrEMBL. For Python workflows with multiple databases, prefer bioservices (unified interface to 40+ services). Use this for direct HTTP/REST work or UniProt-specific control
How to read paid media dashboards without fooling yourself. Attribution models, platform reporting quirks, multi-platform reconciliation, ROAS vs LTV horizon traps, statistical noise in performance metrics, incrementality testing, and the failure modes that produce expensive lessons. Triggers on read paid media dashboard, attribution analysis, ROAS vs LTV, multi-platform reconciliation, ad incrementality, geo holdout, conversion lift study, ghost bidding, paid media reporting, board-deck paid media metrics, blended CAC, MMM, MTA, last-click attribution. Also triggers when a marketer is about to scale, kill, or rebudget a campaign based on platform metrics, or when reconciling platform reports against warehouse revenue.
Datos de fondos comunes de inversion argentinos via CAFCI (Camara Argentina de Fondos Comunes de Inversion). Combina catalogo JSON (1152 fondos, 4615 clases, fees, IDs, metadata), snapshot diario XLSX (VCP, patrimonio, market share, variaciones), ficha individual markdown (rendimientos TNA por periodo) y composicion de cartera (top activos). Sin API key.
Eight-layer pre-match football analysis. Takes a natural-language query, finds the match on FlashScore MCP, runs build_match_context.py to get normalized context in JSON, and produces a structured report using probabilistic language. Only for notstarted matches. Does not invent data or replace predictions with heuristic models.
Manage Power BI report workspace items and PBIR definitions in Microsoft Fabric via `az rest` CLI against the Fabric REST API. Use when the user wants to: (1) upload or publish a PBIR/report definition to Fabric, (2) get or download report definitions, (3) update report item definitions or properties, (4) list workspace reports, (5) delete reports. For editing PBIR files, pages, visuals, filters, themes, or formatting, use `powerbi-report-authoring`. For business questions over report data, use `fabriciq`. Triggers: upload PBIR report definition, upload Power BI report, download PBIR definition, publish PBIR definition, publish Power BI report to Fabric, manage Power BI reports, list workspace reports.
Run conversion rate optimization through hypothesis-driven testing including audit, hypothesis generation, test design, statistical analysis, and rollout decisions. Use this skill whenever the user wants to optimize conversion, run A/B tests, audit a funnel, generate test hypotheses, design experiments, or analyze test results. Triggers on conversion optimization, CRO, A/B test, split test, multivariate test, hypothesis, conversion funnel, funnel audit, experiment design, statistical significance, lift, optimization. Also triggers when the user has a conversion problem and isn't sure where to start, or when test results are ambiguous and need interpretation.
Builds phased data and system migrations using feature flags, dual writes, backfills, and validation. Includes rollback plans and risk mitigation. Use for “data migration”, “system migration”, “database migration”, or “platform migration”.
Prepare, audit, or revise Nature-ready Data Availability statements, data repository plans, dataset citations, and FAIR metadata checklists for manuscripts. Use when the user asks about Nature data availability, research data sharing, repository selection, accession numbers, restricted or sensitive data, source data, supplementary datasets, DataCite-style dataset references, FAIR metadata for academic publication, or Chinese-to-English data availability wording for Chinese-speaking authors preparing Nature-family submissions.
Construcción y optimización cuantitativa de portafolios: Markowitz (scipy.optimize + Monte Carlo), Black-Litterman (prior CAPM, views absolutas/relativas, posterior bayesiano), HRP/HERC/NCO (clustering jerárquico, risk parity, NCO con restricciones). Todo flat numpy + scipy, sin Riskfolio-Lib ni PyPortfolioOpt.
The ads consultant that becomes an ads agent. Reads paid-ad performance natively through the TikTok Ads connector, or via a build-connector Zapier connection for other ad platforms, explains in plain English what is working and what is burning money, recommends budget, targeting, and creative changes with the dollar impact of each, drafts new ad copy in the owner's voice plus Canva creative briefs, and — only after an explicit yes — executes the approved changes in the ad account. Works from exported CSV reports when nothing is connected, the normal starting point. Use this whenever the owner talks about ads or ad spend at all, including "are my ads working," "what's my ROI on ads," "I'm spending money on Facebook and I don't know what I'm getting," "should I turn this campaign off," "my cost per lead is going up," "write me a new ad," "who should I be targeting," or "the ad guy quit"
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.