Skills
96,981 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Implements Syncfusion .NET MAUI Cartesian Chart (SfCartesianChart) for data visualization with 20+ chart types including line, area, column, bar, financial, statistical, and stacked charts. Use when implementing cartesian charts, line charts, area charts, or financial charts (candle, OHLC). Covers chart axes, legends, tooltips, trackball, zooming and panning, and chart annotations.
Implements Syncfusion .NET MAUI Circular Charts (SfCircularChart) including pie, doughnut, and radial bar chart types. Use when implementing circular data visualization, pie charts, doughnut charts, or radial bar charts. Ideal for percentage breakdowns, category comparisons, part-to-whole relationships, or displaying proportional data in circular format.
Implements and customize Syncfusion .NET MAUI Funnel Charts (SfFunnelChart). Use when implementing funnel charts, sales funnel visualization, conversion funnel analysis, process stage visualization, or marketing funnel tracking. Covers funnel chart implementation, customization, data labels, legends, tooltips, segment spacing, and orientation.
Implements Syncfusion .NET MAUI Polar Charts (SfPolarChart) for visualizing data in polar coordinates. Use when working with polar charts, radar charts, spider charts, web charts, or circular data visualization. Ideal for displaying data in terms of values and angles, creating line or area series in polar layouts, or comparing multiple data series radially.
Use this skill ALWAYS when the user needs to implement Syncfusion MAUI Spark Charts. Triggers on spark chart, sparkline, micro-chart, trend visualization, data visualization in small spaces, chart types (line, area, column, win/loss), markers, range bands, axis configuration, data point styling. Also use immediately for chart customization, performance optimization, marker configuration, data binding patterns, accessibility needs.
Implements Syncfusion .NET MAUI Sunburst Chart (SfSunburstChart) for hierarchical data visualization using a radial, multi-level circular layout. Use this for sunburst charts, radial hierarchical charts, multi-level pie charts. Ideal for visualizing organizational structures, file systems, or nested categorical data.
Works with the OpenMeter REST API for usage metering, billing, and entitlements. Covers CloudEvents ingestion, meters, features, plans, customers, subscriptions, entitlements, notifications, billing profiles, invoices, apps, addons, grants, and the Stripe marketplace. Use when integrating OpenMeter, debugging metering, building catalog sync scripts, or when the user mentions OpenMeter API.
Design safe transaction boundaries, rollback behavior, idempotency, locking, and consistency strategies for Kotlin + Spring business workflows. Use when a feature writes to the database, spans multiple repositories, publishes messages, calls external systems, suffers from partial commits or duplicate processing, or needs precise `@Transactional`, propagation, or isolation guidance.
Provides guidance for mechanistic interpretability research using TransformerLens to inspect and manipulate transformer internals via HookPoints and activation caching. Use when reverse-engineering model algorithms, studying attention patterns, or performing activation patching experiments.
This skill applies Edward Tufte's data visualization principles from "The Visual Display of Quantitative Information" to create high-impact slides. Use when designing presentations, creating charts/graphs, reviewing slides for clarity, or transforming data into visual displays. Triggers on phrases like "make a slide", "create presentation", "design chart", "visualize data", "review my slides", or "make this more impactful".
Scrapes X/Twitter data without API access using Puppeteer stealth and browser console scripts. Extracts profiles, followers, following lists, tweets, search results, hashtags, threads, media, bookmarks, notifications, DMs, likes, and viral tweets. Exports to JSON/CSV. Use when collecting, exporting, or analyzing Twitter data.
UiPath Insights historical monitoring via `uip insights`: job metrics, failure analysis, and process performance; queue totals, SLA risk, timelines, and failure drill-down; machine availability, fault ranking, and runtime minutes; filter discovery of scope; alert definition, history, delivery, and entitlement reads; Insights user, role, and group reads; Maestro process dashboard reads and writes; export destination checks; Looker dashboard lists and embed URLs. For live Orchestrator records or job start/stop/logs→uipath-platform, root-cause analysis→uipath-troubleshoot, workflow authoring→uipath-rpa, org identity→uipath-admin.
Track physical units and propagate measurement uncertainty in scientific calculations using pint and uncertainties. Use for unit conversion and dimensional checking, GUM uncertainty budgets, Type A and Type B evaluation, coverage factors and expanded uncertainty, Monte Carlo propagation, significant-figure and plus-minus reporting, error propagation through curve fits, CODATA constants, auditing Python code for stripped units or broken uncertainty propagation, and order-of-magnitude plausibility checks using dimensionless groups (Reynolds, Peclet, Damkohler, Knudsen, Biot, Womersley), characteristic scales such as diffusion time or Debye length, and observed magnitude ranges. Trigger on "is this number physically reasonable", "sanity check these units", "what regime is this flow in", or a result that looks off by orders of magnitude.
Provides guidance on fine-tuning large language models with Unsloth, a library for faster, lower-memory training using LoRA and QLoRA, based on its official documentation (references/llms-txt.md). Use when setting up LoRA or QLoRA fine-tuning of an LLM with Unsloth, reducing GPU memory use during training, looking up Unsloth features or APIs, debugging Unsloth training code, or learning Unsloth best practices. Not for general model training with plain Hugging Face Transformers or PEFT without Unsloth.
Managing Walrus blob lifecycles: epochs, lifetimes, extending blobs, deleting blobs, burning blob objects, sharing blobs, setting blob attributes, and handling large uploads. Use when the user needs to extend a blob's lifetime, delete or burn blobs, create shared blobs, set/get/remove blob attributes, plan large data uploads (>1 GiB), estimate storage costs, manage concurrent upload memory, or understand epoch-based expiration. For basic store/read, see `walrus-cli`. For quilts, see `walrus-quilts`.
This skill helps users automatically extract complete Markdown content from any website via the BrowserAct Web Search Scraper API. The Agent should proactively apply this skill when users express needs like extract complete markdown from a specific website, scrape the content of an article link, get the text from a target url, convert a webpage to markdown format, fetch the main content of a blog post, extract data from a given web page, parse the html of a website into markdown, download the readable text from a news article, obtain the content of a tutorial page, extract all the markdown text from any http or https url, scrape documentation from a web link, or grab the text of a single webpage.
Two services in one skill: (1) Web3 on-chain data via Chainbase CLI — use when the user asks about blockchain data, token holders, wallet addresses, token prices, ENS domains, transactions, DeFi portfolios, or any on-chain analytics. Triggers: "top holders of", "who holds", "wallet address", "token price", "token transfers", "ENS domain", "on-chain data", "blockchain query", "SQL query on-chain", across Ethereum, BSC, Polygon, Arbitrum, Optimism, Base, Avalanche, zkSync, and other EVM chains. (2) Crypto social intelligence via Tops (chainbase tops) — use when the user asks about trending crypto narratives, social mentions, Twitter/X crypto discussions, narrative discovery, topic heat, or KOL/community signals. Triggers: "trending topics", "crypto narrative", "what's trending in crypto", "social mentions", "who is talking about", "crypto twitter", "KOL signals", "narrative search", "topic posts".
Logs and tracks machine learning experiments with Weights & Biases (W&B, wandb): metrics, hyperparameters, checkpoints, sweeps, artifacts with lineage, model registry, custom charts and shareable reports. Includes integrations for PyTorch, HuggingFace Transformers, PyTorch Lightning and Keras/TensorFlow. Use when logging training runs and comparing them across configurations, running automated hyperparameter sweeps, versioning datasets and models as artifacts, registering model versions, or sharing results with a team workspace. Use when setting up wandb.init for offline or unstable-connection training. Not for general data versioning outside ML runs.
Use when someone asks how their ad campaigns, channels, ad sets, ads, countries or keywords are performing in Adapty Attribution — ROAS, payback, CPI, cost per trial, installs, trial-to-paid, refunds, predicted revenue — which channel or campaign to scale or cut, where to move budget, why installs dropped, why Meta, TikTok or Google report different numbers than Adapty, or how to set up Adapty Attribution with Meta, TikTok, Google Ads, custom tracking links or a web funnel.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Use when working on an AI GO Custom App (ai-go.app): developing the frontend (React + TypeScript) or a Server-Side Action (Python), deployment and verification, planning a new App from scratch (requirements inventory, deciding between the Custom App / Hosted App product lines), planning user authorization architecture (roles, app role allowlists, batch-inviting internal and external people), planning data architecture (self-built tables in the data center / Data Reference), hooking up Webhooks or setting App schedules, and moving/migrating an existing system or an entire project (frontend + backend + DB; Supabase / Google Sheet / MySQL, etc.) into AI GO.
This skill should be used when the user asks about "market research", "market analysis", "PESTEL analysis", "competitive landscape", "trend report", "industry analysis", "market sizing", "data interpretation", "competitive analysis", "act as a market researcher", "market research mode", "market trends", "SWOT analysis", "market opportunity", or needs expertise in analyzing markets, interpreting data, and producing strategic market intelligence reports. Part of the AlterLab FC Skills collection (Public Relations & Advertising department).
This skill should be used when the user asks about "infographic", "data visualization", "information design", "data viz", "chart design", "visual storytelling with data", "information graphics", "statistical graphics", "diagram design", "act as an infographic designer", "infographic mode", "data-ink ratio", "chart selection", "dashboard design", "visual data", "Tufte", "ISOTYPE", "icon array", "pie chart", "bar chart", "flow diagram", "process visualization", "data story", or needs expertise in transforming complex data into clear, compelling visual narratives through information design. Part of the AlterLab FC Skills collection (VCD department).
Analyze current TikTok offsite heat, recent-month momentum, and commercial signals for one or more Amazon ASINs with compatible KOLSprite MCP tools. Use when an Amazon seller provides ASINs or Listings and asks whether they have TikTok signals, how the latest 30 days compare with the prior 30 days, or which ASIN has the stronger TikTok opportunity. Do not use for generic TikTok research, exact Amazon sales attribution, or future-sales prediction.
Plans and evaluates analytical method validation, verification, and transfer using Python scripts (plan_validation, check_response, check_accuracy_precision, check_detection_limits, check_bioanalytical_run, compare_methods) under ICH Q2(R2)/Q14, ICH M10, USP 1220/1225/1226, CLSI EP, or ISO/IEC 17025. Covers protocols, linearity, accuracy and precision, DL/QL, M10 run checks, TOST equivalence, Deming and Passing-Bablok. Use when designing a validation protocol with pre-stated acceptance criteria. Use when evaluating linearity, precision, or detection limit data. Use when checking an ICH M10 bioanalytical run or ISR. Use when comparing or transferring a method between labs. Does not decide a method is validated; USP, CLSI, ISO text is not supplied.
Scans transactions for fraud and anomaly signals — duplicate charges within 48 hours, transactions more than 3 standard deviations above a merchant's historical average, first-ever transaction with a new merchant above a high-dollar threshold, and unusual geography or time. Produces severity-tagged alerts with the transaction id, evidence, and a recommended action (call bank, freeze card, dispute, monitor). Use for vigilance scans on every drop, after any large unexplained outflow, or when user mentions fraud check, suspicious charge, anomaly detection, or duplicate charge.
Implements automated data deletion workflows for GDPR Article 17 right to erasure and retention period expiry. Covers cascading deletion across dependent systems, dependency handling for referential integrity, confirmation logging, and audit trail generation. Activate for automated deletion, erasure automation, data purge, retention expiry queries.
Generates Records of Processing Activities automatically from IT system inventories including Active Directory, cloud service catalogs, API gateway logs, and database schemas. Covers automated field population, data flow discovery, and system-to-RoPA mapping. Activate for automated RoPA, system inventory, data discovery, auto-population, IT-driven records.
This skill should be used when users need to query AWS cost and usage details for a specific date. It supports querying costs at service level (e.g., EC2, S3, RDS) and drilling down to usage type level (e.g., instance types, storage classes, data transfer). Triggers on requests mentioning AWS costs, billing, spending, cost breakdown, or fee analysis.
Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use biopython.
Explains how to use Salesforce BLIP-2 (Q-Former bridging a frozen image encoder and an LLM such as OPT or FlanT5) through HuggingFace Transformers and LAVIS for image captioning, visual question answering, image-text matching, and feature extraction. Use when generating captions for images, building a VQA system, doing zero-shot image-text understanding without task-specific training, matching or retrieving images against text, or fitting a BLIP-2 model into limited GPU memory with INT8/INT4 quantization. Prefer LLaVA or InstructBLIP for instruction-following multimodal chat, and CLIP for plain image-text similarity.
Marketing analytics and measurement infrastructure. Triggers for 'GA4', 'GTM', 'analytics', 'dashboard', 'attribution', 'measurement', 'tracking setup', 'UTM', 'experiment infrastructure' — not CRO hypotheses themselves.
Query the CZ CELLxGENE Census programmatically for versioned public single-cell and spatial transcriptomics data. Use when you need population-scale cell metadata, gene expression slices, Census summary counts, source H5AD URIs/downloads, embeddings, spatial Census data, or reference atlas comparisons across organisms, tissues, diseases, assays, and cell types. For analyzing your own local single-cell data use scanpy, anndata, or scvi-tools.
Use Chrome DevTools MCP to retrieve and analyze web page data. Use this skill when users need to scrape web content, analyze page structure, extract data, take screenshots, monitor network requests, or perform web automation tasks.
Use when the user has a results draft or a set of data-backed claims and wants each one adversarially verified against the underlying dataset before publishing — a pre-publication red-team of the findings. Extracts the discrete checkable claims from the draft, reproduces each claim's number against the data, stress-tests it against the threats most likely to kill it (outliers, confounds, Simpson's reversals, tiny subgroups, alternative specifications), and marks it verified, fragile, or refuted; fragile and refuted claims are revised — hedged, scoped, or retracted — until every claim is verified or appropriately qualified. The result is a draft where every surviving claim has been reproduced and survived a stress test. Not for open-ended discovery of new findings over a dataset (that is a data-analysis task), and not for diagnosing a single known anomaly or pipeline failure — this is a gate over an existing draft.
Generate TypeScript/JavaScript code that reads/decodes AND writes/encodes ClickHouse RowBinary streams for the ClickHouse HTTP server. Use this skill whenever a user wants to parse or produce `RowBinary`, `RowBinaryWithNames`, or `RowBinaryWithNamesAndTypes`. Node.js only, doesn't cover browsers.
Use CloudBase document database Web SDK only for confirmed NoSQL collection work. Query, create, update, and delete document data; if the task mentions PostgreSQL / CloudBase PG / app.rdb(), route to postgresql-development instead.
TRIGGER THIS when researching market salaries, building compensation strategies, creating salary bands, analyzing pay equity, conducting salary reviews, preparing compensation proposals, or ensuring competitive compensation. Analyzes market data, builds equity-focused salary structures, identifies pay gaps, and creates data-driven compensation recommendations.
Advisory-only Kafka tuning for Confluent Cloud or Confluent Platform by priority: latency, throughput, availability, or durability. Use when asked to size a planned topic or tune an existing cluster, topic, producer, or consumer for a quantified target such as lower p99 latency or no lost acknowledged writes. Inspects configuration read-only and recommends changes, trade-offs, and validation; the administrator applies them. Do NOT trigger for: WarpStream or Apache Kafka OSS-only clusters; Flink SQL or UDF authoring, tuning, or debugging (use confluent-cloud-flink-sql or flink-udf); Kafka Connect tuning; new Java/Python client implementation (use developing-kafka-java-client or developing-kafka-python-client); Kafka Streams/KStream/KTable development (use kafka-streams-programming); or CDC, Tableflow, or Iceberg pipelines (use confluent-cloud-cdc-tableflow).
Guide for Convex backend development fundamentals including function types (queries, mutations, actions), layered architecture, HTTP actions, and the core mental model. Use when building Convex backends, creating queries/mutations/actions, implementing HTTP webhooks, or understanding Convex's reactive data model. Activates for Convex project setup, function definition, API design, or backend architecture tasks.
Design and audit product cost structure, gross margin, COGS, cloud/AI inference cost, payment fees, app-store commissions, marketplace payouts, refunds, support cost, usage-based margin, CAC/LTV, payback, cohort profitability, free-tier economics, and pricing sustainability. Use when deciding whether SaaS, app, game, API, AI, marketplace, or subscription monetization can profitably scale.
Use when building a catalog backend module that ingests entities from an external source (e.g. Azure DevOps, internal API, JSON file). Covers both the normal EntityProvider and the IncrementalEntityProvider for large-volume sources, plus module wiring via catalogProcessingExtensionPoint or incrementalIngestionProvidersExtensionPoint. Triggers on phrases like "catalog provider", "ingest entities from X", "import entities from <source>", or "incremental ingestion".
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.