Skills
97,222 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Use this skill when analyzing Wispr Flow voice dictation history for self-reflection, work patterns, mental health insights, or productivity analytics, and when managing the Wispr Flow dictionary (adding terms, fixing mishears, exporting/importing, suggesting improvements). Triggered by requests like “/wispr-analytics”, “analyze my dictations”, “what did I dictate today”, “wispr reflection”, “add to wispr dictionary”, “improve dictation”, “wispr suggest”, “export wispr dictionary”, or any request to review voice dictation patterns or manage dictation quality
Expert knowledge for Azure Cache for Redis development including troubleshooting, best practices, decision making, architecture & design patterns, security, configuration, and deployment. Use when configuring geo-replication, persistence, VNet/private endpoints, Entra/RBAC auth, or ARM/Bicep deployments, and other Azure Cache for Redis related development tasks. Not for Azure Managed Redis (use azure-managed-redis), Azure Cosmos DB (use azure-cosmos-db), Azure Table Storage (use azure-table-storage).
Converts documents into clean, chunked datasets suitable for embeddings and vector search. Produces chunked JSONL files with metadata, deduplication logic, and quality checks. Use when preparing “training data,” “vector datasets,” “document processing,” or “embedding data.”
Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel proteins; generating protein embeddings; performing inverse folding; or conducting protein engineering tasks. Supports both local model usage and cloud-based Forge API for scalable inference.
Use this skill when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.
Lovrabet runtime CLI — an AI operations suite for business scenarios. Use the lovrabet command to manage app catalogs, business roles and permissions, user-level external accounts, Service Tree business commands, API documentation discovery, dataset queries, Instant API data operations, Custom SQL/Backend Function, Personal Backend Function, file uploads, OCR text and structured-field extraction for ticket-related business documents, approval tasks and processing, scheduled tasks, Skill, knowledge bases, and runtime app-config key status checks. Triggers: 云图, lovrabet, lovrabet-cli, business roles, role members, page permissions, menu permissions, dataset permissions, user-account bind, provider external account binding, DingTalk userId binding, service tree, business service tree, api-doc, dataset, data filter, file upload, file query-url, ocr recognize, OCR recognition, ticket text extraction, ticket field extraction, invoice recognition, receipt recognition, credential recognition, attachment upload, personal-bff, approval, approvals, pending tasks, approve, reject, transfer, schedule, scheduled tasks, cron, kb, skill, sql exec, bff exec, app-config, accessKey, compress, jq.
Cross-tool golden rules for an autonomous Scrapfly web agent connected to the Scrapfly MCP server (web_scrape, screenshot, check_if_blocked, the Cloud Browser lifecycle, snapshot/click/fill/type/press/scroll/select/drag, and WebMCP). Use when an LLM agent has access to the Scrapfly MCP toolset and needs to choose between stateless scraping and a stateful Cloud Browser session, decide when to call browser_unblock, or coordinate multi-tool flows
Analyze paid advertising performance across channels and recommend budget reallocation to maximize ROAS and minimize CAC. Use when: planning quarterly ad budget allocation, diagnosing underperforming ad channels, deciding whether to scale spend on a channel, calculating marginal ROI across Google Ads, Meta, LinkedIn, or TikTok, rebalancing media mix after performance shifts, or setting up a test-and-scale framework for new channels.
Backtrader is an open-source quantitative backtesting framework, implemented entirely in Python. It supports multiple data sources, strategies, and timeframes, as well as live trading. Use when users need to develop quantitative strategies, analyze backtests, write trading logic, optimize backtesting parameters, or mention backtrader or quantitative backtesting frameworks. If users only need data retrieval and do not need backtesting, direct them to data Skills such as baostock/akshare/tushare.
Web data extraction and discovery using the Bright Data JavaScript/TypeScript SDK (`@brightdata/sdk`). Use when the user is working in Node.js/TypeScript and asks to "scrape", "get data from", "extract", "search for", or "find" information from websites. Also use when the user mentions specific platforms like Amazon, LinkedIn, Instagram, Facebook, TikTok, YouTube, Reddit, Pinterest, ChatGPT, Perplexity, or DigiKey, or asks for "bulk data", "historical data", or "dataset" from JS. Covers scraping, SERP search, AI discovery, datasets, browser automation, and Scraper Studio. For Python, use brightdata-sdk; for the terminal CLI, use brightdata-cli.
CCXT cryptocurrency exchange library for Go developers. Covers both REST API (standard) and WebSocket API (real-time). Helps install CCXT, connect to exchanges, fetch market data, place orders, stream live tickers/orderbooks, handle authentication, and manage errors in Go projects. Use when working with crypto exchanges in Go applications, microservices, or trading systems
Query patent bibliographic information from the Eureka patent database, including titles, abstracts, applicants, inventors, classification numbers, priority claims, and cited references. Trigger this skill when the user mentions patent bibliography, basic patent information, patent titles and abstracts, patent applicants or inventors, patent classifications, IPC or CPC classifications, patent agents, patent examiners, priority claims, cited references, related documents, estimated expiry, or Eureka patent data. Also trigger it when the user needs basic bibliographic information about a patent, such as its title, abstract, applicant, or classification number, even if they do not explicitly mention “Eureka” or “bibliography”.
Use the Scrapfly CLI (`scrapfly`) to scrape web pages, capture screenshots, extract structured data with AI, crawl entire sites, and drive a cloud browser over CDP from a shell. JSON envelope on stdout for every command. Use when the user asks to "scrape a URL", "screenshot a page", "extract data from HTML", "crawl a site", "drive a browser with Playwright/CDP", "bypass anti-bot", or any task involving the `scrapfly` command.
Finds Fabric items across every workspace with the Catalog Search API when the workspace is unknown, locating an item by display name, inventorying one item type tenant-wide, and returning the workspace and item IDs needed for later calls. Dataflow Gen1 and Gen2 items are not supported. Use only to locate items; once found, query them with the owning item skill, such as sqldw-cli, spark-cli, eventhouse-cli, or fabriciq.
Perform technical analysis on stock candlestick data, calculate indicators such as MA/MACD/RSI, and assess trends and buy/sell signals. Use when: (1) “Analyze the technicals for Moutai”; (2) “See if this stock is worth buying”; (3) “Technical analysis 600519”; (4) assessing stock trends or buy/sell points. First retrieve data using data-collect.
Use this skill whenever the user wants to run a parameter sweep over a Proteina-Complexa design pipeline — cartesian-product hyperparameter scans, Pareto search over generation/reward/evaluation knobs, or any "compare configurations" workflow. Trigger phrases include "sweep beam width", "sweep nsteps", "hyperparameter sweep", "parameter scan", "scan beam_width and temperature", "compare configurations", "find the best generation params", "what's the optimal nsteps", "Pareto search for binder quality vs wall-clock", "complexa sweep", "tune Complexa", "ablate the reward weights", "configs/sweeps", "--sweeper", "run beam_width.yaml". This is the only skill that owns sweeper YAML authoring, cartesian-product expansion, and per-config result ranking.
Expert-level Databricks platform, Apache Spark, Delta Lake, MLflow, notebooks, and cluster management. Use when the user mentions Apache Spark, Delta Lake, MLflow, lakehouse architecture, or PySpark, or when the task involves Cluster Configuration and Management, Delta Lake Architecture, PySpark Data Processing, or MLflow Integration
Finetune a pretrained KERMT encoder on a labeled CSV. The skill validates the input checkpoint (must be a pretrain ckpt — grover_base / cmim / hybrid), validates the labeled CSV, prepares the data (clean + features + optional split), then launches main.py finetune inside the kermt container (detached for hours-scale runs). Hyperparameters come from agent/config/defaults_finetune.json with per-flag CLI override.
Check progress for a detached KERMT run (pretrain, finetune, or any kermt_run_detached invocation). Reads run.json, queries docker for container state, tails the pretrain/finetune log, and parses progress lines (epoch, step, val loss).
Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell analysis.
A ByteHouse cluster diagnostic and health-check tool for checking cluster health, diagnosing issues and anomalies, viewing node status, and analyzing performance metrics. Use this Skill when users need to check ByteHouse cluster health, diagnose cluster issues and anomalies, view node status, or analyze performance metrics.
Use this skill when the user wants to search the DataHub catalog, discover entities, answer ad hoc questions about their data, find datasets, or browse by platform or domain. Triggers on: "search DataHub", "find datasets", "who owns X", "what tables contain PII", "what columns does X have", or any request to search, discover, browse, or answer one-off questions about DataHub metadata. For lineage questions ("what feeds into X"), use `/datahub-lineage`. For systematic audits ("how complete is our metadata"), use `/datahub-audit`.
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.
A high-level interactive graphing library for Python. Ideal for web-based visualizations, 3D plots, and complex interactive dashboards. Built on plotly.js, it allows users to zoom, pan, and hover over data points in a browser-based environment. Use for interactive charts, web applications, Jupyter notebooks, 3D data visualization, geographic maps, financial charts, animations, time-series analysis, and building production-ready dashboards with Dash.
Implements Syncfusion® .NET MAUI Maps (SfMaps) control. Use when implementing maps, visualizing geographic data, or displaying shape layers and tile layers (OpenStreetMap, Azure maps, Google Maps, Bing Maps). Covers markers, bubbles, legends, data labels, tooltips, sublayers, zoom and pan features, or spatial data visualization in .NET MAUI applications.
Search and analyze Tencent Cloud CLS (Cloud Log Service) logs. Use whenever the user asks to: search logs, debug API errors, trace requests by trace ID, find 5xx errors, run CQL/SQL analytics over log topics, extract structured fields. Backed by the official tencentcloud-sdk-python CLS client
End-to-end user research assistant, qualitative and quantitative. Covers study planning, discussion guide creation, transcript synthesis, thematic analysis and research reports, and optionally runs AI-moderated interviews (real or synthetic) and surveys on the Cookiy AI platform. Published by Cookiy.
Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that don't fit in memory.
Use when you need framework-agnostic SQL guidance — schema naming, relational table design, query readability, indexes, transactions, database security, migrations, testing, and monitoring — without choosing Spring Boot, Quarkus, or Micronaut. This should trigger for requests such as Review SQL schema or migrations; Improve SQL query performance and readability; Design relational tables and indexes; Review database transaction, security, or monitoring practices. Part of Plinth Toolkit
Eva Review 2.5.0 is an entry point for post-publication reviews across platforms. Use it to review published short videos, Xiaohongshu posts, WeChat Official Account content, and more. It reads backend screenshots, Excel, CSV, Markdown tables, or multiple historical records to decide what to test next. After the user's first authorization, it continuously maintains the current project's account review log. When users explicitly want to look back at a set of published content, it can provide an analysis of content combinations for that period as needed. For published customer-acquisition videos with real user feedback, it can validate the original trust hypothesis, but does not build a lead or sales system. Triggers: /eva-review, Eva Review, review this published content, see where this post may have stalled after publishing, fill in the previous results, look back at recent content, what have we mainly been talking about lately, analysis of content combinations for a period, summarize recent content patterns, what topics are more likely to perform well for me. Pre-publication review, writing new content directly, and platform speculation without a specific published item are outside this Skill.
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.