Skills
97,222 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Design, plan, and analyze A/B tests with statistical rigor. Use when the user asks about A/B testing, split testing, experiment design, statistical significance, sample size calculation, test duration, multivariate testing, or conversion experiments. Trigger phrases include “A/B test,” “split test,” “experiment,” “statistical significance,” “sample size,” “test duration,” “which version wins,” “conversion experiment,” “hypothesis test,” “variant testing”
When the user wants to build or improve a sales bot's ability to rate conversation quality. Also use when the user mentions "conversation scoring," "quality scoring," "exchange rating," "conversation metrics," or "performance scoring."
Analytics tracking, interpretation, funnel analysis, product metrics, and ROI measurement. Use when setting up GA4/GTM tracking, interpreting analytics data, analyzing conversion funnels, calculating ROI, or measuring product engagement. Triggers on “analytics,” “GA4,” “Google Analytics,” “conversion tracking,” “event tracking,” “UTM parameters,” “tag manager,” “GTM,” “tracking plan,” “funnel analysis,” “conversion rates,” “user flow,” “cohort analysis,” “retention,” “product metrics,” “North Star metric,” “ROI,” “break-even,” “payback period,” “investment analysis,” “validate my funnel,” “why isn't my funnel converting,” or “executive financial report.” For A/B test setup, see ab-test-setup
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.
Use SurrealKit, SurrealDB's schema-management and migration CLI, to scaffold projects from templates, sync schema in development, plan and execute production rollouts (with rollback), statically check SurrealQL without a database, generate JSON/TypeScript types, and write declarative TOML test suites for schemas, permissions, and API endpoints. Use this skill whenever users set up, migrate, type, check, or test a SurrealDB schema with SurrealKit.
Drug safety and adverse event analysis — FAERS spontaneous-report mining, FDA black-box warnings, signal detection (PRR, ROR, IC), risk factors by demographic/comorbidity, label change tracking, and SIDER curated label-derived side-effect frequencies. Use for post-market safety surveillance, AE signal investigation, drug-AE association strength scoring, "what side effects does drug X have" questions, and pharmacovigilance reports.
Core ML, Create ML, Vision framework, Natural Language framework, on-device ML integration. Use when user wants image classification, text analysis, object detection, sound classification, model optimization, or custom model integration. Covers Core ML vs Foundation Models decision.
Provision, connect, and operate Sealos Cloud databases through sealos-cli for local development, Devbox development, and app setup. Use when the user needs a cloud database for a project, asks to create or connect PostgreSQL/MySQL/MongoDB/Redis or another Sealos database, wants DATABASE_URL or similar env vars wired into a dev environment, needs database connection details, backups, logs, public access, or wants to replace local Docker Compose databases with a managed Sealos database.
A self-maintained competitor-monitoring machine for YouTube: the read surfaces that matter for market and competitor research plus a local databank of channel histories, snapshots, comments, and packaging assets - market data hours old, not weeks. Trigger phrases: `monitor my youtube competitors`, `which competitor videos are gaining views right now`, `find fresh breakout videos in a niche`, `youtube packaging and thumbnail analysis data`, `mine youtube comments for audience signal`, `new niche research workspace`, `switch youtube api key`, `backfill youtube channel history`, `get the transcript of this youtube video`, `use youtube`, `run youtube`.
Use the `surreal` command-line binary to run a SurrealDB server (in-memory, RocksDB, SurrealKV, or TiKV), open an interactive or piped SQL REPL, import and export databases, check server readiness, upgrade the binary, repair storage, and manage SurrealML models. Use this skill whenever users run, connect to, back up, restore, or operate SurrealDB from the terminal. Triggers: surreal start, surreal sql, surreal import, surreal export, surreal isready, surreal upgrade, surreal fix, surreal ml, SurrealDB CLI, surreal server.
Fetches real, citable Bilibili (B站) video data — stats, metadata, tags, and full danmaku text — via login-free API calls, never hand-typed or estimated. Use when citing view/like/favorite counts, analyzing why a video performed, or archiving a Bilibili source into a knowledge base. Accepts BVID, av numbers, b23.tv links, or full URLs; subtitles and 收藏夹 (favorites) enumeration need the user's Bilibili login.
Analyze Claude Code and Claude Desktop Code token usage, cost, quota burn, model mix, cache read/write, and 5-hour block consumption using ccusage evidence. Use when the user asks why Claude quota was exhausted, whether a model such as fable/opus/sonnet is unusually expensive, how many tokens were spent today or historically, or needs a human-friendly explanation of local Claude Code CLI/Desktop usage.
Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs
Use this skill when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO, and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, and model persistence. Use for cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without a local GPU setup.
Comprehensive market analyst skill that orchestrates all Octagon stock performance and market data skills. Use when conducting stock analysis, creating market reports, evaluating valuations, comparing sectors, or performing technical and sentiment analysis
Retrieve chemical compound data from PubChem and ChEMBL with disambiguation, cross-referencing, and stereochemistry handling. Use for resolving compound names to SMILES/InChI/CID/ChEMBL IDs (including OPSIN deterministic IUPAC-name-to-structure parsing), fetching molecular properties, distinguishing isomers/stereo forms, and cross-validating identity across databases. Always use English compound names; flags ambiguous queries (e.g., Vitamin D has multiple forms).
Use when the user asks for a publication-quality scientific figure or table, wants help choosing the right chart for results, needs a paper-ready pubfig or pubtab workflow, wants a figure + companion table for a results section, wants an Excel sheet turned into publication-ready LaTeX, or wants an existing scientific figure/table reviewed and upgraded.
Use the Bailian CLI (`bl`) to complete the full “data → fine-tuning → export → deployment → invocation” workflow, or skip training and deploy a base model directly. Supports fine-tuning text models (SFT/DPO/CPT), audio TTS models (CosyVoice), image generation models (Wan2.7), and video generation models (Wan i2v/kf2v). Covers dataset validation/upload, creating fine-tuning jobs, waiting for training, exporting the best checkpoint, creating inference deployments, waiting for readiness, and providing invocation examples. Activate this skill when the user mentions “training a model,” “fine-tuning,” “fine-tune,” “finetune,” “deploying a model,” “launching a model,” “running/invoking a fine-tuned model,” “training an inference model,” “continued pretraining,” “LoRA/SFT/DPO training,” “speech synthesis model,” “TTS fine-tuning,” “CosyVoice,” “voice cloning,” “image generation fine-tuning,” “text-to-image,” “image-to-image,” “Wan2.7,” “image model training,” “video generation fine-tuning,” “image-to-video,” “first-frame-to-video,” “first-and-last-frame,” “kf2v,” “i2v,” “Wan video,” or “video effects training” on Bailian / DashScope / Alibaba Cloud Model Studio. Use it even if the user does not explicitly say “use bl,” as long as they intend to train or deploy on Bailian; do not assemble commands yourself.
Chief Data Officer advisory for startups: AI training data rights and consent provenance, data product strategy (warehouse vs lakehouse vs mesh, build-vs-buy), B2B customer-data-as-asset valuation and M&A readiness, data team org evolution. Use when deciding whether to train models on customer data, choosing data architecture, valuing data for fundraising or M&A, sequencing data hires, or when user mentions CDO, chief data officer, data strategy, data mesh, lakehouse, training data, data product, data monetization, or customer data asset. NOT a tactical data engineering skill — strategic decisions only.
Crypto trading analysis advisor. Triggers when users ask whether a token is worth trading, if a position can be taken, or for trend analysis. Provides high-value data sources and professional trading principles. Trigger phrases include "should I trade", "can I long/short", "is it a good setup", "trade recommendation", "can I enter here", "what's the play" etc.
Diagnose why a product metric changed (dropped, spiked, or plateaued) by orchestrating breakdowns, actors, paths, lifecycle, retention, and annotations queries. Use when the user reports an anomaly, asks "why did X change?", or needs root-cause analysis for a trend, funnel, retention, stickiness, or lifecycle metric.
Query 1688 best-selling product rankings for sourcing discovery and wholesale product research. Trigger this skill when users mention 1688 product rankings, 1688 best sellers, wholesale hit products, domestic sourcing rankings, one-piece dropshipping product selection, 1688 trending products, popular items for bulk purchasing, supplier product rankings, 1688 billboard, 1688 bestsellers, sourcing rankings, wholesale hot products, trending product rankings, supplier rankings, or 1688 trends. Trigger it even if the user does not explicitly mention “1688 rankings,” as long as their request involves finding popular wholesale products or sourcing opportunities on 1688.
Query and analyze scholarly literature using the OpenAlex database. This skill should be used when searching for academic papers, analyzing research trends, finding works by authors or institutions, tracking citations, discovering open access publications, or conducting bibliometric analysis across 240M+ scholarly works. Use for literature searches, research output analysis, citation analysis, and academic database queries.
When the user wants to build or improve a sales bot's ability to gauge prospect tone—frustrated, curious, warm, skeptical—and adjust responses accordingly. Also use when the user mentions “reading tone,” “emotional detection,” “prospect mood,” “sentiment scoring,” or “tone analysis.”
Cricket data via ESPN public endpoints and Cricsheet open data—live-ish series scoreboards, standings, match summaries, and news (ESPN), plus historical ball-by-ball data, player stats, and player registry (Cricsheet, ODC-BY 1.0). Zero config, no API keys. Use when the user asks about cricket scores, IPL/BBL/PSL/international series, points tables, match details, cricket news, ball-by-ball history, or player career stats. Don't use for other sports—use football-data (soccer), nfl-data (NFL), nba-data (NBA), wnba-data (WNBA), nhl-data (NHL), mlb-data (MLB), tennis-data (tennis), golf-data (golf), cfb-data (college football), cbb-data (college basketball), or fastf1 (F1). For betting odds, use polymarket or kalshi. For general news, use sports-news. Don't use for ICC rankings—no free source exists (v1 limitation).
Transform raw data from CSVs, Google Sheets, or databases into executive-ready reports with visualizations, key metrics, trend analysis, and actionable recommendations. Creates data-driven narratives for leadership. Use when users need to turn spreadsheets into executive summaries or board reports.
MPSTATS daily time-series performance for a single SKU on the Russian Ozon marketplace. Returns date-level metrics for an Ozon product, including sales, price, stock, and rating, with optional search rank/visibility data to validate growth trends, seasonality, and anomalies. Trigger this skill when users mention Ozon trends, sales trends, price movements, daily data, stock trends, search rank, product history, MPSTATS trend, Ozon daily performance, Ozon time series, Ozon search visibility, or Russian marketplace product history. Trigger it even if “MPSTATS” is not explicitly mentioned, as long as the user wants daily or period-based trends for an Ozon product.
Redis vector search guidance covering HNSW vs FLAT algorithm choice, vector index configuration (dims, distance metric, datatype), filtered hybrid search combining vector similarity with TAG or NUMERIC filters, and the RAG retrieval pattern with RedisVL. Use when defining a VECTOR field in FT.CREATE, integrating embeddings (OpenAI, Cohere, sentence-transformers), tuning HNSW parameters (M, EF_CONSTRUCTION, EF_RUNTIME), building a retrieval-augmented generation pipeline, or filtering vector results by attribute
Build production-ready web scrapers for any website using Bright Data infrastructure. Guides you through site analysis, API selection, selector extraction, pagination handling, and complete scraper implementation. Use this skill whenever the user wants to build a scraper, create a crawler, extract data from a website, scrape product pages, handle pagination, build a data pipeline from a web source, or automate data collection from any site — even if they don't explicitly say 'scraper'. Triggers on phrases like 'build a scraper for', 'scrape data from', 'extract products from', 'crawl pages on', 'get data from [website]', or 'I need to pull data from'.
Rules and guidelines for working with Spring Data JDBC in the project. ALWAYS use this skill when adding, removing, or modifying Spring Data JDBC entities (@Table from org.springframework.data.relational.core.mapping), aggregates, AggregateReference links, embedded objects, @MappedCollection associations, or Spring Data JDBC repositories (CrudRepository / ListCrudRepository). Trigger on any request that touches @Table, @Column, @MappedCollection, @Embedded annotations from spring-data-relational, AggregateReference fields, @PersistenceCreator constructors, or @Query methods on JDBC repositories. Do NOT use for JPA (jakarta.persistence) entities — use the spring-data-jpa skill instead.
A one-stop skill for retrieving Amazon Ads reports, covering all report types for Sponsored Products (SP) / Sponsored Brands (SB) / Sponsored Display (SD). The script automatically creates, waits for, downloads, and extracts reports, returning readable structured data directly. The single source of truth for actually available report types and each type’s column list/groupBy/filters is `references/report-types/{adProduct-dir}/{reportTypeId}.md`. Trigger this skill when users mention pulling or downloading Amazon Ads reports, or retrieving any reports for SP/SB/SD campaigns, keywords, search terms, advertised products, purchased products, ad groups, traffic anomalies, Prompt expansion, etc. This skill depends on linkfox-amazon-ads-auth. Sponsored Television (ST) / Amazon DSP are not yet covered.
Drill down into product listings by seller ID on MPSTATS for the Russian Ozon marketplace. Returns comprehensive metrics for all SKUs under that seller, including sales volume, revenue, price, rating, inventory, turnover, and lost sales, with multidimensional numeric filtering, sorting, and currency conversion. Use for store structure analysis, breakdowns of a seller’s bestsellers, and competitor store benchmarking. Trigger this skill when the user mentions Ozon seller products, Ozon store analysis, Ozon seller drill-down, Ozon seller SKUs, Ozon store bestsellers, Ozon competitor stores, MPSTATS seller, Ozon seller drill-down, Ozon shop audit, Russian marketplace seller SKUs, or Ozon store structure. Even if the user does not explicitly say “MPSTATS,” trigger this skill whenever they intend to view sales performance for all products in a store by Ozon seller ID.
Execute read-only SQL queries against multiple MySQL databases. Use when: (1) querying MySQL databases, (2) exploring database schemas/tables, (3) running SELECT queries for data analysis, (4) checking database contents. Supports multiple database connections with descriptions for intelligent auto-selection. Blocks all write operations (INSERT, UPDATE, DELETE, DROP, etc.) for safety.
When the user wants to build or improve a sales bot's ability to track conversion rates, drop-off points, and response patterns. Also use when the user mentions "bot analytics," "conversation metrics," "tracking performance," "measuring bot effectiveness," or "conversion tracking."
Implement, review, or improve CloudKit and iCloud sync in iOS/macOS apps. Use when working with CKContainer, CKRecord, CKQuery, CKSubscription, CKSyncEngine, CKShare, NSUbiquitousKeyValueStore, or iCloud Drive file coordination; when syncing SwiftData models via ModelConfiguration with cloudKitDatabase; when handling CKError codes for conflict resolution, network failures, or quota limits; or when checking iCloud account status before performing sync operations.
Designs relational and document database schemas from requirements or an existing app, including tables and collections, keys, relationships, constraints, indexes driven by real query patterns, ERDs, and reversible migration scripts. PostgreSQL (including Supabase) is the default; MySQL, SQLite, and MongoDB are also supported. Use when the user wants to model data for a new app or feature, review or fix an existing schema, choose keys or indexes, plan multi-tenancy, normalize or denormalize, or write CREATE TABLE statements or migrations—even if they just describe their app and ask “how should I store this”. For moving data between database providers, use database-migrator.
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.