Skills
96,981 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Implements Syncfusion .NET MAUI Pyramid Chart (SfPyramidChart) for visually representing hierarchical, proportional, and parts-to-whole data using pyramid-shaped segments. Use this for pyramid charts, hierarchical data visualization, proportional data display, or segment-based charts. This skill covers installation, data binding, legends, tooltips, data labels, appearance customization, and gradients.
Tencent Map location services, supporting POI search, route planning, travel planning, nearby search, trajectory data visualization and map data visualization. ⚠️ Mandatory behavior: after this Skill loads, the first action must be to check whether a formal Key exists (the environment variable TMAP_WEBSERVICE_KEY or one already provided by the user in the conversation). If a formal Key exists, continue handling the user's request directly. If no formal Key exists, immediately show the user the Key selection prompt (formal Key registration guidance + preset trial Key option), then stop and wait for the user's choice; no analysis, file reading, plan drafting or code writing may happen before the user chooses.
Calculate, understand, and improve the unit economics of a solopreneur business. Use when figuring out if the business is actually profitable per customer, when CAC or LTV numbers are needed, when evaluating whether a pricing or acquisition strategy is sustainable, or when making data-driven decisions about marketing spend and pricing. Covers CAC, LTV, payback period, contribution margin, and the feedback loops between them. Trigger on "unit economics", "CAC", "customer acquisition cost", "LTV", "lifetime value", "payback period", "is my business profitable", "contribution margin", "am I making money per customer", "should I spend more on marketing".
Use when investigating or improving WordPress performance (backend-only agent): profiling and measurement (WP-CLI profile/doctor, Server-Timing, Query Monitor via REST headers), database/query optimization, autoloaded options, object caching, cron, HTTP API calls, and safe verification.
Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.
Assesses AI bias risks for GDPR Art. 9 special category data and AI Act Art. 10 data governance. Covers fairness metrics, bias detection methods, mitigation strategies, and documentation requirements for protected characteristics. Keywords: AI bias, special category, fairness metrics, discrimination, Art. 9, Art. 10.
Measure how a brand appears in AI answers by combining Semrush AI-channel traffic estimates with controlled prompt observations, keeping measured findings separate from diagnostic hypotheses. Use when the user asks about AI visibility, whether ChatGPT or Perplexity mention their brand, AI share of voice, or how much traffic arrives from AI assistants. AI-channel traffic reports require a Trends subscription.
Build employee turnover prediction models to identify flight risk and retention drivers. Use this skill when the user needs to predict which employees are likely to leave, identify retention risk factors, or prioritize HR interventions — even if they say 'attrition prediction', 'who is going to quit', or 'employee retention model'.
Direct ALGOPACK SuperCandles API workflows for tradestats, orderstats, obstats, 5-minute trade/order/order-book metrics, field meanings, EQ/FO/FX endpoints, curl, JSON/CSV handoff, simple HTML output, ISS columns/data normalization, and start pagination
Analyze user/customer feedback and produce a User Feedback Analysis Pack (source inventory, normalized feedback table, taxonomy/codebook, themes + evidence, recommendations, and feedback loop). Use for voice of customer, feature request analysis, support ticket synthesis, churn reason synthesis, and survey open-ends.
Use when the user has a known, already-observed anomaly in their data — a metric spike or drop, an outlier, an unexpected number — and wants its root cause diagnosed, not guessed. Forms a slate of candidate causes, tests each against the data, and eliminates the ones the data refutes, narrowing the live candidates until exactly one survives refutation and passes a positive confirming test. The result is an investigation log with the confirmed root cause and the evidence that ruled out the alternatives. Not for open-ended discovery over a dataset with no specific anomaly in hand (that is data-analysis), and not for checking an external claim against sources (that is claim-verify) — this is reactive diagnosis of one anomaly you already know about.
Build a local-business lead list without scraping Google Maps. Routes "find dentists / plumbers / salons / restaurants in <city> with emails", "business email finder for <category> in <city>", "web-design prospects with no website or a weak one", "enrich my list of business websites with verified emails", "leads within 5 km of an address" and "alert me when new businesses appear" to an OpenStreetMap-based Actor that crawls each business's own website and returns one 73-column row per business with an MX-verified email, phone, social profiles, website platform and a 0-100 lead score. No Google Maps, no API key, no proxy, no login; every filter runs before billing. Use when the user wants local B2B leads for cold email or calls, a Google Maps alternative that avoids Google's terms, a bring-your-own-website-list enrichment pass, or a scheduled new-business watch for one category and city.
Synthesize new waveforms from scratch for Simulink inports using createInputDataset. Use ONLY when the user asks to generate, create, or synthesize signals (step, ramp, sine, chirp, pulse, noise) to populate a Dataset for External Inputs or Signal Editor. Covers timeseries and timetable formats with correct data type, interpolation, units, and dimensions. Also covers function-call and trigger inport timing setup. Do NOT use when the user wants to load, import, or read existing data from files (MAT, CSV, spreadsheet) — even if that data will be used as model input. Do NOT use for running simulations or plotting outputs.
Implements automated PII discovery and classification using tools like Microsoft Purview, BigID, OneTrust DataDiscovery, and AWS Macie. Covers scanning schedules, accuracy tuning, false positive management, and integration patterns. Keywords: data discovery, PII scanning, Purview, BigID, Macie, OneTrust, automated classification, data cataloging.
Comprehensive retrospective analysis of 2025 for BidDeed.AI, Everest Capital USA, Life OS, and Shapira family achievements. Use when Ariel requests year-end review, 2025 analysis, annual retrospective, or wants to understand what was accomplished across all domains (business, family, technical) throughout 2025. Generates detailed analysis with metrics, visualizations, and insights across GitHub repos, Supabase data, Life OS tracking, and conversation history.
Covers Cloudflare Hyperdrive, which connects Cloudflare Workers to existing PostgreSQL and MySQL databases with global connection pooling and query caching: creating configs with wrangler hyperdrive create, wrangler.jsonc bindings, the required nodejs_compat flag, driver setup for node-postgres (pg), postgres.js and mysql2, Drizzle ORM and Prisma integration, local development, and caching and TLS options. Use when giving Workers access to RDS, Aurora, Neon or Supabase databases, setting up connection pooling or a hybrid architecture, or debugging errors such as Failed to acquire a connection from the pool, TLS not supported by the database, connection refused, missing nodejs_compat, or Code generation from strings disallowed.
Access the CoinPaprika API to query cryptocurrency market data including coin prices, tickers, exchanges, historical OHLCV data, and global market statistics. Use this skill when making HTTP requests to api.coinpaprika.com or api-pro.coinpaprika.com for crypto market information.
Integrate or upgrade the Contentsquare React Native SDK -- Session Replay, screen tracking, transactions, analytics, masking, privacy opt-in/out. Always starts with a mandatory Contentsquare project healthcheck (ask for the data source IDs or resolve the Android and iOS bundle ids, call the healthcheck endpoint, report project health) before any other work. Use when working with Contentsquare, CSQ, StartConfig, AnalyticsOptions, CSQMask, CSQWebView, @contentsquare/react-native-bridge, or migrating from the legacy `Contentsquare` API to `CSQ`.
Manufacturing cost accounting, analysis, and reduction for automotive operations. Covers overhead/burden rate calculation, variance analysis, make vs buy decisions, value engineering, and cycle time to cost conversion. USE WHEN user says 'cost analysis', 'overhead rate', 'burden rate', 'variance analysis', 'make vs buy', 'value engineering', 'cost reduction', 'should cost', 'labour rate', or needs manufacturing cost guidance.
Fetches structured public web data through Crawlora's hosted REST API, including broad search, bounded web crawls, and durable cross-corpus research jobs. Calls require a user-provided API key and successful requests may consume credits. Use for public prices, listings, reviews, transcripts, SERPs, trends, financials, and places.
Build a customer health scorecard for a specific account. Use when asked to score account health, assess renewal risk, build a health dashboard, or evaluate an account's likelihood to renew or expand. Produces a structured health scorecard with a RAG status, dimension scores, key risks, and recommended actions.
Use when the user wants an iterative, self-checking exploratory analysis of a dataset — surfacing findings that are each verified by re-running the computation, not asserted. Proposes one specific hypothesis at a time, writes and runs analysis code to test it, and records the finding only if the numbers support it at a meaningful effect size; loops until no new verified finding appears or the budget is hit. The result is a findings report where every claim is backed by a reproducible number. Not for diagnosing a single known anomaly or pipeline failure, and not for verifying an external claim against sources (that is a claim-verification task) — this is open-ended discovery over a bound dataset.
Turn raw business data (CSV, SQLite, spreadsheets, pasted tables) into clear analytical summaries, trend analysis, and actionable reports for small business operators, analysts, and decision-makers. Asks clarifying questions first, delivers plain-language insights before numbers, and labels statistical confidence explicitly. Does not provide financial advice or present projections as fact.
Design and audit data quality observability for analytics events, product metrics, warehouses, imports, pipelines, dashboards, AI datasets, billing data, and operational reports. Use when teams need freshness, completeness, accuracy, schema drift detection, lineage, backfill safety, alerting, incident ownership, and metric trust.
Data visualization with chart selection, color theory, and annotation best practices. Covers chart types (bar, line, scatter, heatmap), axes rules, and storytelling with data. Use for: charts, graphs, dashboards, reports, presentations, infographics, data stories. Triggers: data visualization, chart, graph, data chart, bar chart, line chart, scatter plot, data viz, visualization, dashboard chart, infographic data, data presentation, chart design, plot, heatmap, pie chart alternative
Ground text-to-SQL work in DataHub catalog evidence. Use when a user asks to write, draft, debug, or execute SQL; answer a data question that requires SQL; calculate a metric; query named tables; or investigate SQL results with DataHub MCP tools available. Always begin with find_sql_context, even when the user already supplied tables or dataset URNs, and call inspect_tables_for_sql on the chosen tables before writing SQL.
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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.