Skills
96,770 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Perform RFM (Recency, Frequency, Monetary) customer segmentation from transaction data. Use this skill when the user needs to segment customers by purchase behavior, identify high-value buyers, design retention campaigns, or prioritize marketing spend by customer value — even if they say 'who are our best customers', 'which customers are at risk of churning', or 'how do we target our marketing'.
Use when the user wants an autonomous ML research loop that explores the space broadly rather than hill-climbing one approach. A temperature scheduler replaces the usual hypothesis step: it forces several wild, diverse swings (full rewrites, different architectures/regimes) early, then enters an adaptive phase that picks swing / merge / exploit per iteration — with a hard stagnation guard that bans further small-step exploits once they run too long, forcing a pivot back to a swing or merge. Tracks an approaches.md registry and a move_type per iteration; analyses every run before the next move. One change per iteration; loops forever until interrupted. Not for the standard analysis-first ml-autoresearch (which lets analysis alone choose each change), one-off training runs, or sweeps.
Analyze one GitHub user's code contribution activity in a specified UTC time window and generate a Markdown report focused on practical contributions (authored merged PRs, PRs merged by the target user, and authored commits) across accessible public and private repositories. Use when users ask to evaluate real engineering contribution over a period and want evidence links.
Tonghuashun financial data queries covering stocks, funds, macroeconomics, industry economics, news announcements, bonds, Hong Kong and US stocks, and index sectors, plus intelligent stock picking, fund selection, macro and industry economic indicator queries, and financial announcement and news search.
Text network analysis and knowledge graphs via the InfraNodus MCP server: analyze text structure, find content gaps, generate research questions and ideas, compare texts, optimize content for SEO/GEO, analyze Google search results, GraphRAG retrieval, structured memory. Accepts text, URLs (YouTube transcription included), and saved graphs. Also builds knowledge graphs of code repos and Obsidian vaults: invoked in a project folder ("graph this repo", "analyze this vault", "/infranodus") it mines docs, PDF text, docstrings, WHY/NOTE comments, and commit/PR/issue history into saved graphs with a report, and keeps them current by replacing the statements of changed files in place. When infranodus/manifest.json exists in the project root, answer questions about themes, decisions, rationale, or gaps by querying the existing graphs FIRST, before reading files. For an LLM-authored knowledge base built from sources (wiki pages, curated ontologies), prefer the llm-wiki skill.
Use compatible KOLSprite MCP tools for a straightforward current TikTok creator, product, video, or shop lookup. Use for searches, filters, profiles, comparisons, and returned statistics. Do not use for multi-step opportunity evaluation, cold-start planning, content strategy, creator-program design, competitor diagnosis, or growth strategy.
Build a tiered target-account list by analyzing Epiphan's best customers (pulled live from HubSpot + customer-orders MCP), deriving an ICP across firmographics/tech/growth, and scoring 100+ net-new lookalike companies 0-100. Gates every surfaced company through Golden Rules + qualify_lead so existing customers/channel/AE-owned accounts are excluded. Use when: "find companies like [customers]", "build a lookalike list", "who else looks like [account]", "lookalike target account list", "companies similar to our best customers", "expand into [vertical]", expanding into a new vertical. Not for timing signals (intent-signal-aggregator-skill) or single-contact lookups (contact-hunter-skill).
Crypto market-structure research agent — 24+ indicators across derivatives, options (gamma wall, skew), on-chain (MVRV, smart money signals, DEX hot tokens), and macro sentiment. Powered by OKX CeFi CLI + OnchainOS + direct HTTP for options chain. Use this skill whenever the user asks about: derivatives data, gamma wall, options skew, funding rates, open interest, put/call ratio, MVRV, cost basis, realized price, exchange flows, CEX inflows/outflows, liquidation pressure, whale tracking, smart money flows, fear/greed index, BTC dominance, stablecoin flows, taker volume, basis/backwardation, or any request like "what does the market structure look like", "give me a macro overview", "how are derivatives positioned", "is the market overleveraged", "should I be bullish or bearish based on data", "are whales accumulating or distributing", "show me exchange flows". Also trigger when users mention specific tokens and want deeper analysis beyond simple price action — e.g., "what's going on with ETH right now", "is BTC about to move", "analyze SOL market conditions".
Use this skill to classify tabular data end-to-end in MATLAB — load a dataset, prepare and clean it, select promising classifiers, train them, and compare accuracies with cross-validation, holdout, or hyperparameter optimization plus statistical tests. TRIGGER when: user asks to classify tabular data, pick classifiers for a dataset, compare classifier accuracy, run cross-validation or a holdout evaluation, or find the best model with statistical uncertainty. DO NOT TRIGGER when: user has non-tabular inputs (images, sequences, time series), wants a regression model, is training a specific neural network architecture (use matlab-train-network), or wants cost-sensitive learning or an arbitrary class-prior vector (this skill only supports the built-in uniform-prior toggle for imbalanced data).
Build automated pipelines for medical imaging data: format conversion (DICOM to NIfTI, PNG, HDF5), batch processing, ML dataset preparation, research data export, and imaging ETL workflows. Use when converting DICOM to other formats, preparing imaging datasets for machine learning, building research data pipelines, batch processing medical images, extracting imaging features, or automating imaging workflows.
Integrating TanStack DB with meta-frameworks (TanStack Start, Next.js, Remix, Nuxt, SvelteKit). Client-side only: SSR is NOT supported — routes must disable SSR. Preloading eager collections in route loaders with collection.preload(). On-demand Query Collections require preloading the live query because source collection preload is a no-op. Multiple collection preloading with Promise.all. Framework-specific loader APIs.
Appends structured decision entries to the yahoo-mlb decision log (tracker/decisions-log.md) on behalf of any agent in the MLB team. Validates entries against the authoritative schema, serializes concurrent writes from parallel agents, and runs the Monday calibration pass to fill in outcomes and update the variant scoreboard. Use when any MLB agent needs to record a decision, when the coach requests "log decision", "append to decision log", "record variant outcome", or runs the "calibration pass".
Parses Yahoo Fantasy Baseball league state (roster, standings, current matchup, FAAB remaining, free agents) from authenticated Yahoo team pages via Claude-in-Chrome browser automation, then grounds it against league-config.md and team-profile.md to emit a normalized league-state bundle every other agent can consume without re-scraping. Use when the coach or any downstream agent needs to read Yahoo roster, refresh team profile, pull league state, get current matchup, check FAAB remaining, list free agents, or when user mentions "what's on my roster", "who am I playing this week", "how much FAAB do I have left", or "refresh my team".
Python moexalgo library workflows for futures-only FUTOI DataFrames, .env session.TOKEN setup, Market("FO").futoi, Ticker(...).futoi, futures open interest, FIZ/YUR segmentation, pos and pos_long or pos_short fields, participant counts, library parameter caveats, and aggregation caveats.
Guide users through configuring key MongoDB MCP server options. Use this skill when a user has the MongoDB MCP server installed but hasn’t configured the required environment variables, or when they ask about connecting to MongoDB/Atlas and don’t have the credentials set up
Guides MongoDB users through implementing and optimizing Atlas Search (full-text), Vector Search (semantic), and Hybrid Search solutions. Use this skill when users need to build search functionality for text-based queries (autocomplete, fuzzy matching, faceted search), semantic similarity (embeddings, RAG applications), or combined approaches. Also use when users need text containment, substring matching ('contains', 'includes', 'appears in'), case-insensitive or multi-field text search, or filtering across many fields with variable combinations. Provides workflows for selecting the right search type, creating indexes, constructing queries, and optimizing performance using the MongoDB MCP server.
Handle files and binary data in n8n correctly. Use when working with files, images, PDFs, attachments, uploads or downloads, base64, vision/multimodal input, or when an AI agent needs a file as tool input or output — and whenever the user mentions $binary, binaryPropertyName, "read the PDF", "attach the file", "send the image", Merge losing binary, or a CDN for chat images. Covers the $binary vs $json split, reading/writing binary, keeping binary alive across transforms with Merge, the agent-tool binary boundary, and the CDN/URL requirement for chat surfaces.
OpenTelemetry Transformation Language (OTTL) for OTel Collector pipelines. Use when writing or debugging OTTL statements in the transform processor, filter processor, or routing connector in any collector configuration — context selection, path expression mistakes (`attributes` vs `resource.attributes`), `error_mode`, cardinality reduction with `keep_keys`, JSON body handling with `ParseJSON`, PII redaction with `SHA256` / `replace_pattern`, and span naming. Not for receiver/exporter connectivity, DNS, pipeline wiring, or telemetry that never reaches the processor — those are infra problems, not OTTL problems.
Reconstruct an open-source project's growth channels, key content, and stage-by-stage attribution from GitHub history and public web evidence. Use this skill when asked to research GitHub star growth, identify launch and amplification channels, explain viral milestones, find representative posts or videos, compare Product Hunt/Show HN/Reddit/KOL impact, or produce an evidence-backed OSS growth attribution report.
Ingest PDF/Markdown/TXT files into joelclaw's docs memory pipeline with Inngest durability, durable NAS artifacts, and OTEL verification. Use when adding docs, running batch reindex, reconciling coverage, or recovering stuck runs. Triggers on: 'ingest pdf', 'ingest markdown', 'docs add', 'pdf-brain ingest', 'backfill books', 'docs reconcile', 'reindex docs', 'batch reindex'.
When the user wants to predict future cash consumption and “Runway” using time-series AI models. Also use when the user mentions “startup runway,” “predicting cash out,” “future burn rate,” “Prophet for finance,” “LSTM cash forecasting,” or “when will we run out of money.”
Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.
PyGraphistry visualization: bindings, color/size/icon encodings, layout controls, static export, and privacy-safe link sharing. Use when asked to "color nodes by type", "set point size", "add icons to nodes", "layout my graph", "export graph as PNG", "share a graphistry link privately", or "url_params". Also triggers on "encode_point_color", "bind(point_label=...)", "settings(url_params=...)", "badge", "static graph image", or "iframe embed". Proactively suggest when the user has a working plot but wants to customize its appearance or control link sharing and privacy.
Control PyMOL molecular visualization through Claude Code. Use when asked to "visualize protein", "render structure", "show cartoon", "color by chain", "ray trace", "set up pymol", "install pymol", or work with molecular graphics. Handles setup, visualization commands, and publication-quality figure generation.
HogQL query examples and reference material for PostHog data. Read when writing SQL queries to find patterns for analytics (trends, funnels, retention, lifecycle, paths, stickiness, web analytics, error tracking, logs, sessions, LLM traces) and system data (insights, dashboards, cohorts, feature flags, experiments, surveys, hog flows, data warehouse). Includes HogQL syntax differences, system model schemas, and available functions.
Use when planning or reviewing production database migrations, adding columns, indexes, constraints, backfills, renames, table rewrites, or concurrent operations. Covers phased rollouts, lock behavior, rollback strategy, strong_migrations compliance, and deployment ordering for schema changes.
All-in-one Redis expertise covering data modeling and key naming, client connections (pooling, pipelining, client-side caching, timeouts), clustering and replication, Redis Search (FT.CREATE, vector and hybrid search, RAG pipelines), observability and incident triage, production security hardening, semantic caching for LLM responses via LangCache, and agent memory via Redis Iris (session events, long-term memory, background promotion). Use whenever designing, writing, reviewing, or debugging anything that touches Redis: choosing a data structure, naming keys, configuring a client, sharding across a cluster, building or tuning a search index, monitoring or diagnosing performance, hardening a deployment, caching LLM completions, or wiring persistent memory for an AI agent, merged into one skill so the right domain loads automatically without picking between separate ones.
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.