Skills
97,222 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Store key-value data globally with Cloudflare KV's edge network. Use when: caching API responses, storing configuration, managing user preferences, handling TTL expiration, or troubleshooting KV_ERROR, 429 rate limits, eventual consistency, cacheTtl errors, wrangler types issues, or remote binding configuration
Query Developer Experience (DX) data via the DX Data MCP server PostgreSQL database. Use this skill when analyzing developer productivity metrics, team performance, PR/code review metrics, deployment frequency, incident data, AI tool adoption, survey responses, DORA metrics, or any engineering analytics. Triggers on questions about DX scores, team comparisons, cycle times, code quality, developer sentiment, AI coding assistant adoption, sprint velocity, or engineering KPIs
Use when asked to “PMF survey”, “measure product-market fit”, “40% rule”, “Sean Ellis test”, “Rahul Vohra method”, or “how disappointed would you be”. Helps quantify product-market fit and systematically improve it. The PMF Survey framework (created by Sean Ellis, popularized by Rahul Vohra at Superhuman) measures how disappointed users would be without your product and turns that data into a roadmap.
Primary Python toolkit for molecular biology. Preferred for Python-based PubMed/NCBI queries (Bio.Entrez), sequence manipulation, file parsing (FASTA, GenBank, FASTQ, PDB), advanced BLAST workflows, structures, phylogenetics. For quick BLAST, use gget. For direct REST API, use pubmed-database.
Create text-driven visuals in Markdown: charts, diagrams, cards, architecture and page layouts. Charts and analytical views (bar, line, pie, heatmap, correlation, regression); reliability and operations (latency, incident, throughput, cycle time, OKR, standup, on-call); product and finance (funnel, retention, revenue, budget); process and workflow (approval, BPMN); software design and behaviour (class, state machine, sequence, C4); dependencies and impact (dependency graph, ER, causality); system architecture as HTML (layer stack, wings, zones, topology, service catalogue, request paths); infrastructure (cloud, Kubernetes, ETL, network, security, IAM, compliance); governance and people (ArchiMate, org chart, hiring); knowledge and planning (mind map, roadmap, Gantt, migration); documents (comparison, SWOT, memo, policy, catalogue, case study). Use when a document needs a diagram, chart or card. Figures render live. Not for slide decks or math notation. Not recommended: mermaid / canvas / drawio / dot
Use when working with n8n's built-in Data Tables, designing schemas, inserting/updating/upserting rows, deduping, or querying. Triggers on "Data Table", "data table", `n8n-nodes-base.dataTable`, "dedup", "idempotency", "lookup", "persistent state", "store across executions", or any schema design discussion inside n8n.
Write, push, run, publish, and manage Kaggle Benchmark tasks using the kaggle CLI and the kaggle-benchmarks Python SDK. Use when the user wants to create or push a benchmark task (optionally with attached Kaggle datasets), run benchmarks against LLM models, check task/run status, stream or fetch execution logs, download results and source notebooks, publish a task to make it public, or troubleshoot benchmark workflows
Aggregate and analyze customer reviews from G2, Capterra, Trustpilot, App Store, and other platforms. Performs sentiment analysis, identifies pain points, extracts feature feedback, generates marketing claims, and compares competitor reviews. Use when users need review analysis, competitive intelligence, or customer feedback insights.
Integrate digital health data sources (Apple Health, Fitbit, Oura Ring) and connect to WellAlly.tech knowledge base. Import external health device data, standardize to local format, and recommend relevant WellAlly.tech knowledge base articles based on health data. Support generic CSV/JSON import, provide intelligent article recommendations, and help users better manage personal health data.
Clinical variant interpretation, from raw variant calls to ACMG-classified recommendations with structural impact analysis. Use for VUS classification, pathogenicity assessment with cited criteria, structure-based variant impact (AlphaFold/PDB), non-coding/regulatory variant effect prediction with sequence deep-learning models (AlphaGenome, Enformer, Borzoi, ChromBPNet, Evo 2), and producing clinical-grade variant reports for return of results or molecular tumor boards. Use whenever a user asks about a variant's significance, an intronic/promoter/enhancer/UTR non-coding variant's functional impact, or needs ACMG classification — even if they don't say “ACMG”.
Analyzes a multi-step conversion funnel to find where visitors drop off and which steps have the worst leakage. Use this skill when someone describes a journey and asks about conversion rates, drop-off, fallout, or step completion. Trigger for "analyze our checkout funnel," "where are visitors dropping off," "what's our add-to-cart to purchase conversion rate," "funnel analysis," "show me fallout between steps," or "which step loses the most visitors."
Apply factor models to portfolio construction and fund evaluation, from CAPM through the Fama-French 3- and 5-factor models plus momentum. Use when the user asks about 'Fama-French', 'value factor', 'smart beta', 'factor tilt', 'momentum exposure', or the 'factor zoo', wants to run or interpret a factor regression (loadings, alpha after controlling for factors, R-squared, t-stats), decompose a manager's returns into factor exposures versus skill, or asks 'is my fund closet indexing'. Also trigger on SMB, HML, RMW, CMA, UMD, size/value/quality/profitability/low-vol premia, factor ETF or smart-beta product evaluation (factor purity, turnover, capacity, fees), factor cyclicality and the danger of factor timing, factor crowding, long-short academic factors versus long-only implementable tilts, and post-publication factor decay.
Use this skill when the user wants to build AI applications with Weaviate. It contains a high-level index of architectural patterns, 'one-shot' blueprints, and best practices for common use cases. Currently, it includes references for building a Query Agent Chatbot, Data Explorer, Multimodal PDF RAG (Document Search), Basic RAG, Advanced RAG, Basic Agent, Agentic RAG, and optional guidance on how to build a frontend for each of them.
A tool for searching popular Xiaohongshu posts. Search by keyword to retrieve popular content data, rank and recommend posts by data-based scores, and help creators discover trends and get inspiration. Runs only in the main Agent; do not dispatch to subagents. If the API returns insufficient credits (code=3210), it automatically opens Alipay to recharge and then continues the search
Helps developers retrieve Chainlink Data Streams reports over REST and WebSocket, including report schemas. A price-feed consumer that uses AggregatorV3Interface or latestRoundData goes to chainlink-data-feeds-skill. Reports, REST, WebSocket, and report schemas stay here. It also applies to credentials, report decoding, official Go, Rust, and TypeScript SDKs, high-availability streaming, on-chain report verification, and timestamp lookback
Unity Catalog governance, access control, and observability. Use to grant or revoke access (GRANT/REVOKE), reason about the privilege model and ownership, set up row-level security and column masks, create external locations and storage credentials, define catalogs/schemas/tables/volumes, answer "who can read this table", and query system tables (audit, lineage, billing) or work with volume files in /Volumes/.
Resolves a PostHog experiment reference from natural language to a concrete experiment ID by browsing `experiment-list` (not feature-flag tools), with disambiguation when multiple experiments match. Use when the user names or quotes an experiment (“split test demo,” “the File engagement boost experiment,” “onboarding retention test,” “landing page hero experiment,” “pricing experiment”), describes it loosely (“the signup experiment,” “my pricing test,” “the one with the new checkout”), uses a relative reference (“latest,” “most recent,” “the one I created yesterday”), filters by status (running, draft, paused, exposure frozen, stopped, archived), or otherwise refers to an experiment by anything other than its concrete ID.
When you want to model personal financial scenarios — house purchase + rental income (ADU, bedroom rentals, house-hacking), renovation budgets, monthly cash flow, big-purchase decisions, savings/investment what-ifs for a household. Ships with a house scenario template; other scenarios (refi, car, education, retirement, side income) slot in as templates. Outputs markdown scenario comparison tables. Saves scenarios to ~/Documents/personal-cfo/ with an index in ~/.config/makerskills/personal-cfo/archive/. Not for company books or monthly reporting (that's company-cfo). Composes with decide, deep-research (rental comps, rates), business-brainstorm, second-brain. Triggers on "/personal-cfo," "model this scenario," "house math," "rental forecast," "monthly cash flow," "what if I rent out the ADU," "compare these housing scenarios," "should we buy this house," "house-hack math," "renovation budget."
Use when asked to "run an A/B test", "design an experiment", "check statistical significance", "trust our results", "avoid false positives", or "experiment guardrails". Helps design, run, and interpret controlled experiments correctly. Based on Ronny Kohavi's framework from "Trustworthy Online Controlled Experiments".
Use when writing or reviewing n8n expressions (`{{...}}` syntax), `$json` / `$node` references, Luxon date code, or expression errors. Triggers on `{{}}`, `$json`, `$node`, `$input`, `DateTime`, `Luxon`, "expression error", "evaluating", "format date", "transform field", or any node-parameter assignment.
Build and interpret polygenic risk scores (PRS) for complex diseases using GWAS summary statistics. Covers PRS construction (clumping/thresholding, PRS-CS), validation in independent cohorts, ancestry-aware adjustment, and clinical interpretation (population-relative risk, not absolute prediction). Use for PRS-based risk stratification.
End-to-end guidance for writing mathematical modeling competition papers. Covers CUMCM and MCM/ICM, from paper structure planning and chapter writing to model validation, reference formatting, and final format checks. Pairs with math-modeling-solver as “problem solving → writing”; can expand solver-generated draft excerpts directly into full text. This skill must be used whenever the user mentions mathematical modeling paper writing, modeling competitions, papers for CUMCM/MCM, Electrician Cup, Asia-Pacific Cup, Shenzhen Cup, or Huawei Cup, CUMCM, MCM/ICM, paper structure, abstract writing, model validation, sensitivity analysis, LaTeX modeling templates, Word modeling formatting, Memo/Letter writing, model naming, Our Work flowcharts, or needs any part of a modeling paper written, revised, optimized, or checked.
Use when the user reaches for a Code node, mentions writing JavaScript or Python in n8n, or any custom logic comes up in workflow design. Triggers on "Code node", "Code", "JavaScript", "Python", "custom logic", "transform data", "$input", "$json transformation", "loop in code", "write a function", or any time the obvious answer seems to be "just put it in code."
Run Karpathy-style autonomous ML search on a real training repo: choose the right mode (setup, program.md, bounded loop, results interpretation, or constrained-hardware adaptation), preserve the immutable prepare.py / 300-second / val_bpb contract, and route prompt/skill eval work away to LangSmith, Promptfoo, Braintrust, or skill-autoresearch.
Monthly CFO workflow for a company or agency — pull raw data from bank + payment processor + payroll + expense management, categorize and reconcile, compute end-of-month cash via transaction-sum method, update a scenario projector for forward forecasting, write the monthly snapshot report, surface decisions to leadership. Modes — monthly (default; the standing report), weekly (thin cash pulse), scenario (ad-hoc modeling in the projector), pickup (resume where the prior run left off). Anonymized team-scope sibling to personal-cfo (which handles personal household finances). Composes with company-brain (report gets stored + wiki-indexed there), toolify (wire company-specific data sources), loopify (schedule the monthly + weekly runs). Triggers on "/company-cfo," "/cfo," "monthly cash report," "do the CFO snapshot," "CFO monthly," "let's run CFO," "cash projection," "runway forecast," "monthly financials," "cash pulse."
Focuses on retrieving the latest Douyin trending rankings, updated hourly, including trending events, popularity scores, and links. Supports searching historical rankings from the past 7 or 30 days and scheduled subscription pushes.
Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.
Academic paper fraud detector, in tribute to student Geng. Analyzes academic paper PDFs to detect research misconduct such as data fabrication, image reuse or splicing, Western blot manipulation, and statistical anomalies. Use when users provide a paper PDF and ask to "check for plagiarism," "detect fraud," "check for fabrication," "analyze the paper," or "investigate academic fraud."
This skill should be used when the user asks "SQL or NoSQL", "which database", how to design a "data model" or "schema design", picks an "indexing" strategy, needs "sharding" or "partitioning", sets up "replication" (leader-follower / multi-leader), defines a "primary key"/"sort key", asks whether to "denormalize", or weighs "polyglot persistence". Use it whenever a design must decide where records live, how they are keyed and accessed, and how the store scales past one node — even if the user just says "store the data".
Currency handling patterns for the Dinero.js money library. Use when working with multiple currencies, converting between currencies, defining custom currencies, storing monetary values in databases, or integrating with payment services like Stripe or PayPal. Triggers on currency conversion, database schema design for money, or payment API integration with Dinero objects
Investigates a single PostHog error tracking issue end-to-end. Use when the user provides an issue ID or pastes an issue URL (`/error-tracking/<id>`) and wants to understand the error — who it affects, what triggers it, when it started, whether it correlates with a release, browser, OS, or feature flag, and what the next step should be. Pulls aggregated metrics, sample exception events, segment breakdowns, linked replays, and synthesizes a hypothesis-grade summary in one pass
Generates a concise, executive-ready performance summary covering key metrics, trends, and what's driving movement. Use this skill when someone needs to produce a briefing, executive summary, performance narrative, or stakeholder readout — for example, "write an exec summary of last week's performance," "create a performance briefing for our leadership team," "produce a monthly business review summary," "what should I tell executives about our metrics," or "generate a performance narrative." Also trigger for "QBR summary," "weekly business review," or "stakeholder briefing."
This skill should be used when the user asks about a "caching strategy", "cache invalidation", "what to cache", "read-through vs write-through vs write-back", "cache eviction" (LRU/LFU/TTL), "Redis vs Memcached", "stale reads", or hits "thundering herd", "cache stampede", "cache penetration", or "hot key" problems, or is choosing a Redis `maxmemory` eviction policy (allkeys-lru, volatile-lru, noeviction) vs TTL expiry for sessions or other cached state. Use it whenever a design is read-heavy or a datastore is overloaded by reads, even if the user doesn't say "cache".
Covers publication-ready matplotlib figures for academic papers, slides, and reports—bars, trends, scatter, heatmaps, and multi-panel layouts—with this repository’s house style, print/vector export conventions, and parity with figures4papers demos. Use when the user is finalizing or creating such figures in matplotlib. Do not use for interactive dashboards or web viz (Plotly, Altair, Bokeh), exploratory-only plots without a publication target, dominant 3D or geographic mapping, or Illustrator/Figma-first infographic workflows.
Professional finance research toolkit — backtesting (11 engines + benchmark comparison panel), factor analysis, Alpha Zoo (478 pre-built alphas across qlib158/alpha101/gtja191/academic/fundamental), options pricing, 90 finance skills, 30 multi-agent swarm teams, Trade Journal analyzer, and Shadow Account (extract → backtest → render) across 29 market-data sources (tushare, yfinance, okx, binance, akshare, baostock, tencent, mootdx, ccxt, futu, mt5, tickerall, local, eastmoney, sina, stooq, yahoo, pykrx, india_broker, qveris, longbridge, nobitex, wallex, plus optional-key finnhub/alphavantage/tiingo/fmp/gildata)
Metadata management, data analysis, development changes, operations diagnostics, and inspections for Volcengine databases (MySQL, veDB-MySQL, PostgreSQL, SQL Server, MongoDB, Redis) and self-managed public-network databases (MySQL and PostgreSQL families). Covers instance lists, databases within instances, table lists and schemas, data queries, data analysis and visualization reports (including joint analysis across data sources/files), data governance (asset inventories/data profiling/data quality/sensitive data identification), slow query diagnostics, deadlock and lock-wait analysis, transaction and active session troubleshooting, error log queries, tablespace analysis, health inspections, monitoring metric queries, and change request applications. Does not support ByteCloud databases such as ByteRDS / ByteDoc / ByteRedis
Deploy and manage the ELK Stack (Elasticsearch, Logstash, Kibana) for log aggregation and analysis. Configure log pipelines, create visualizations, and implement log-based monitoring. Use when centralizing logs, implementing search functionality, or building log analytics platforms.
Guides experiment state transitions: launching, pausing, resuming, freezing/unfreezing exposure, ending, shipping variants, archiving, resetting, duplicating, copying to another project, and migrating a legacy experiment to the new experiments engine. Covers preconditions, implications for variant assignment and analysis, and the decision framework for when to use each action. TRIGGER when: user asks to launch, pause, resume, end, ship, archive, reset, duplicate, or copy an experiment to another project, to freeze/unfreeze exposure (stop enrolling new users while metrics keep flowing, or reopen enrollment), or to migrate, convert, move or upgrade a legacy experiment to the new engine or the new metric format. DO NOT TRIGGER when: user is creating an experiment (use creating-experiments), configuring rollout (use configuring-experiment-rollout), or setting up metrics (use configuring-experiment-analytics).
Produces a compact KPI digest showing how key metrics changed over a period and what's driving the movement. Use this skill when someone asks for a performance summary, a weekly recap, a morning briefing, a KPI update, or any variation of "how did we do this week/month." Also trigger for "give me a performance overview," "what moved in the last 7 days," "pull our AA KPI report," or "summarize our metrics."
Web page and website scraping with Firecrawl API. Use this skill when scraping web articles, blog posts, documentation pages, paywalled content, or JavaScript-heavy sites. Triggers on requests to scrape websites, extract article content, convert pages to markdown, or handle anti-bot protection.
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.