Skills
96,981 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Implement Syncfusion React Accumulation Charts including Pie, Doughnut, Funnel, and Pyramid chart types. Use this skill when users need circular data visualization, percentage-based charts, or proportional data displays. Covers data labels, legends, tooltips, grouping, smart labels, drill-down charts, explode effects, theming, accessibility, export/print capabilities, and dynamic data updates.
Evaluate investment performance on a risk-adjusted basis using industry-standard ratios and capture analysis. Use when the user asks about Sharpe ratio, Sortino ratio, Information Ratio, Treynor ratio, Calmar ratio, Omega ratio, or upside/downside capture. Also trigger when users mention 'risk-adjusted returns', 'return per unit of risk', 'M-squared', 'is this fund worth the volatility', 'how to compare two managers', 'capture ratio', or ask which investment performed better after accounting for risk.
Use this skill when users ask how to build or customize Syncfusion PivotView pivot tables in React. Trigger for React pivot grid/OLAP, aggregation, data binding (JSON/remote), drill-down/drill-through, grouping, filtering, conditional formatting, exports (Excel/PDF/CSV), or pivot charts. React-only, not Angular/Vue/Blazor.
Implement Syncfusion React Maps component for geographical data visualization. Use this when displaying interactive maps, location-based information, or regional data analysis. This skill covers map providers (Bing, OpenStreetMap), GeoJSON rendering, markers and bubbles, zooming/panning, data labels, legends, and choropleth visualizations in React applications.
Comprehensive guide to implementing Syncfusion React Query Builder component. Use this skill when building advanced filter interfaces, creating complex data query systems, or implementing dynamic rule-based filtering. This skill covers query builder configuration, SQL query conversion, drag-and-drop filtering, customization, and state management.
Compute and compare investment return metrics including TWR, MWR (dollar-weighted IRR on portfolio cash flows), CAGR, and annualized returns. Use when the user asks about portfolio performance calculation, comparing manager returns, linking sub-period returns, understanding why different return methods give different numbers, converting returns across time periods, or computing the IRR of an investor's own contributions and withdrawals. Also trigger when users mention "how much did I make", "annual return", "compound growth", "dollar-weighted vs time-weighted", "what was my rate of return", "geometric vs arithmetic mean", "log returns", or ask about the effect of cash flows on reported returns. For project or loan IRR, NPV, and generic "solve for the rate" problems, use time-value-of-money instead.
Migrates SQLite and SQL-style Capacitor storage (@capacitor-community/sqlite, cordova-sqlite-storage, @ionic/storage with a SQLite driver, TypeORM/Drizzle/Kysely capacitor drivers, and other bridge-based SQL plugins) to @capgo/capacitor-fast-sql. Use when replacing createConnection/open/query/run/executeSet calls, slow large result sets over the Capacitor bridge, adding SQLCipher encryption, keeping transactions and BLOBs, moving key-value data to KeyValueStore, preserving existing database files during the switch, or fixing Fast SQL setup errors (ATS NSAllowsLocalNetworking, Android cleartext localhost, "Encryption is not available in this build", web OPFS COOP/COEP headers). Do not use for non-SQL storage such as Preferences (capacitor-offline-first), Ionic Secure Storage or Identity Vault (ionic-enterprise-sdk-migration), generic app upgrades (capacitor-app-upgrades), or code that already wraps Fast SQL.
Direct REST API access to PubMed. Advanced Boolean/MeSH queries, E-utilities API, batch processing, citation management. For Python workflows, prefer biopython (Bio.Entrez). Use this for direct HTTP/REST work or custom API implementations.
Programmatic social media and marketing research using free tools: pytrends (Google Trends), yars (Reddit without API keys), and Perplexity MCP (Twitter/TikTok/Web). Use when finding trending topics in a niche, tracking keyword velocity and volume, monitoring Reddit discussions, discovering what's going viral, or researching content opportunities before writing. Zero-cost research stack with built-in rate limiting. Complements content-marketing-social-listening skill with executable code.
Use this skill when the user wants to analyze an existing pipe for improvement opportunities — automation gaps, manual bottlenecks, missing AI agents, field conditions, or adjacent processes. Acts as a process analyst: investigates, diagnoses, and improves the pipe in progressive rounds — each round delivers visible results.
Create diverse synthetic test inputs for LLM pipeline evaluation using dimension-based tuple generation. Use when bootstrapping an eval dataset, when real user data is sparse, or when stress-testing specific failure hypotheses. Do not use when you already have 100+ representative real traces (use stratified sampling instead), or when the task is collecting production logs.
Diagnose, troubleshoot, and advise on any Qdrant deployment by loading the latest official Qdrant skills live from skills.qdrant.tech. Use this whenever someone raises a Qdrant problem or question — slow or degraded search, high or growing memory / OOM crashes, optimizer stuck or slow, indexing slowness, scaling and sharding decisions (node count, QPS, latency, multitenancy, vertical vs horizontal), poor or irrelevant search results, hybrid search and reranking, embedding-model migration, version upgrades and compatibility, monitoring and observability (Prometheus, Grafana, health checks, /metrics, /telemetry), deployment choices (local, Docker, self-hosted, Qdrant Cloud, embedded), or client-SDK questions (Python, TypeScript, Rust, Go, .NET, Java). Trigger especially when the context is clearly a Qdrant cluster, collection, or vector-search deployment. Always prefer this skill over answering from memory: it pulls current, authoritative guidance and only the relevant context.
Estimate potential future losses using VaR, Expected Shortfall, Monte Carlo simulation, and stress testing. Use when the user asks about Value-at-Risk, CVaR, Expected Shortfall, scenario analysis, stress testing, or factor-based risk decomposition. Also trigger when users mention 'how much could I lose', 'worst-case scenario', 'tail risk', 'risk budget', 'component VaR', 'marginal VaR', '99% confidence loss', 'Monte Carlo simulation', or ask how to project portfolio risk forward.
Generate clear, accurate performance reports for investment portfolios with benchmarks, attribution, and risk dashboards. Use when the user asks about portfolio performance reports, return summaries, benchmark comparison, risk dashboards, goal progress tracking, or GIPS-compliant reporting. Also trigger when users mention 'quarterly report', 'how did my portfolio do', 'time-weighted vs money-weighted return', 'annualized returns', 'net-of-fee performance', 'rolling Sharpe', or ask how to present investment results to clients.
A/B testing and experimentation for social media content — evidence over opinion, at honest organic-scale rigor. Use when someone wants to "A/B test" or "split test" content, "test which version/hook/time/caption/thumbnail works," "set up an experiment," "what should I test," or to settle a content debate with evidence instead of opinion. Designs disciplined organic tests: one variable, controlled context, a decision rule set BEFORE publishing, enough duration and repetitions to separate signal from noise. Uses the TEST framework. Reads brand-profile + goals-and-kpis first. It DESIGNS the test and drafts variants; WoopSocial schedules them as controlled sequential posts (exception: YouTube's native Test & Compare); the result is read from native analytics via analytics-and-reporting. Organic can't reach true statistical significance; nothing is fabricated or p-hacked. Distinct from analytics-and-reporting (measures) and goals-and-kpis (sets targets).
College Football (CFB) data via ESPN public endpoints and the NCAA's official endpoints — scores, standings, rosters, schedules, game summaries, play-by-play, rankings, injuries, futures, team/player stats, and news for FBS, plus official FCS scoreboards, NCAA game detail with drive context, and the schools index. Zero config, no API keys. Use when: user asks about college football scores, standings, rankings, team rosters, schedules, game results, play-by-play, injuries, betting futures, team/player statistics, CFB news, or FCS games. Don't use when: user asks about NFL (use nfl-data), college basketball (use cbb-data), or non-sports topics.
Use this skill when building, deploying, evaluating, debugging, or measuring latency for the DeepStream SOP Inference Microservice — a GPU-accelerated FastAPI service that detects whether operators perform assembly-line steps in order via event boundary detection (GEBD) plus VLM classification. Trigger even if the user does not name it: verify operator step sequence, detect missing or out-of-order SOP steps, score factory/work-cell video for procedure compliance, run VLM-based SOP checking on industrial cameras, or call /v1/chat/completions with a file, RTSP, or Basler camera. Also trigger for its internals: SOPVideoProcessor, DeepStream GEBD model (e.g. DDM) via Triton CAPI, nvds_custom_postprocess, Cosmos Reason 1/2 vLLM, SSE streaming, Kafka NvProto/JSON output, Basler/Pylon camera + emulation, Docker compose, chunk-level latency. Do NOT trigger for generic DeepStream pipelines, object detection/tracking, NIM imports, or video summarization.
Use when the user mentions Firetiger or wants to work with their observability data — setting up Firetiger, instrumenting an app with OpenTelemetry, querying traces/logs/metrics with SQL, investigating an incident, monitoring a PR or deployment, or creating a monitoring agent. Always use this skill when the user says "Firetiger", even for simple asks — it routes to the specialized skill that carries the critical gotchas (Basic-auth ingest, DuckDB SQL over per-service tables, the @firetiger comment flow) that prevent common mistakes.
Use this skill whenever the user is working with Drizzle ORM on PostgreSQL. This covers any mention of Drizzle, drizzle-orm, drizzle-kit, drizzle-zod, `pgTable`, `defineRelations`, `relations()`, `db.select()`, `db.query`, or insert/update/delete against Postgres. Typical intents to trigger on: - Designing or debugging Postgres table schemas, columns, identity PKs, enums, JSONB, arrays - Modeling one-to-many or many-to-many relations and join tables in Drizzle - Writing, optimizing, or preparing Drizzle queries (placeholders, `.prepare()`) - Generating Zod validators from Drizzle tables - Setting up TypeScript + Postgres projects (Neon, Supabase, postgres.js, node-postgres) with Drizzle - Migrating from Prisma, TypeORM, or Sequelize **to Drizzle** - Resolving Drizzle type errors; choosing between v1 RC and 0.45.x APIs Do NOT trigger for Drizzle with MySQL/SQLite, raw SQL without an ORM, or other ORMs when Drizzle is not the target.
SAP Datasphere development skill with 3 specialized agents, 5 slash commands, and validation hooks. Use when building data warehouses on SAP BTP, creating analytic models, configuring data flows and replication flows, setting up connections, managing spaces and users, implementing data access controls, using the datasphere CLI, or inspecting authenticated Datasphere browser UI state with Microsoft Edge CDP. Covers Data Builder, Business Builder, analytic models, 40+ connection types, real-time replication, task chains, content transport, and data marketplace.
NHL data via ESPN public endpoints and the official NHL API — scores, standings, rosters, schedules, game summaries, injuries, futures, leaders, and news, plus an analytics backend: play-by-play with on-ice coordinates, cross-league career rows, skater/goalie leaders, and history to 1917. Zero config, no API keys. Use when: user asks about NHL scores, standings, team rosters, schedules, game stats, box scores, play-by-play, injuries, transactions, betting futures, team/player statistics, NHL news, shot locations, career stats, skater/goalie leaders, or historical NHL seasons. Don't use when: user asks about other hockey leagues (AHL, KHL, college hockey). For other sports use: nfl-data (NFL), nba-data (NBA), wnba-data (WNBA), mlb-data (MLB), football-data (soccer), tennis-data (tennis), golf-data (golf), cricket-data (cricket), cfb-data (college football), cbb-data (college basketball), fastf1 (F1). For betting odds use polymarket or kalshi. For news use sports-news.
Analyze US Treasury securities and interest rate risk: bond pricing, yield curve construction, duration, convexity, TIPS, and forward/spot rate analysis. Use when the user asks about Treasury bonds, yield curve construction, interest rate risk, duration, convexity, TIPS, or breakeven inflation rates. Also trigger when users mention 'T-bills', 'T-notes', 'bond pricing', 'yield to maturity', 'inverted yield curve', 'forward rates', 'spot rates', 'DV01', 'real yields', or ask how bonds react to interest rate changes.
Read funnels, retention curves, and event data as a designer — separating a design problem from a tracking artefact. Use when handed product data you did not design and asked why people drop off. For choosing what to measure, use `metrics-definition` (ux-strategy); for running a controlled test, use `a-b-test-design` (prototyping-testing).
Crypto market data from AiCoin Open API v3 — 200+ exchanges, real time. Use whenever the user asks about crypto prices, K-lines, funding rates, open interest, long/short ratios, whale/big orders, liquidation maps, order-book depth, news and newsflashes, Twitter/X posts, trending coins, airdrops and airdrop research, project analysis, exchange listings, crypto stocks, treasury and Grayscale holdings (BTC/ETH), fear & greed indices, market signals, or AI coin picks. Triggers: 'BTC price', '查行情', '看价格', '大饼多少钱', 'K线', '资金费率', '多空比', '持仓量', '鲸鱼单', '大单', '清算', 'liquidation map', '深度', '快讯', '推特', '热门币', 'trending', '空投', '空投研报', '项目分析', '上了哪些交易所', 'ETF', '监管', '灰度', '国库', '上市公司持币', '恐慌贪婪指数'. ALWAYS run the script for real data — NEVER invent prices or use web_search/web_fetch/browser for crypto data. Use aicoin-hyperliquid for HL whale/trader analytics, aicoin-trading for CEX orders, aicoin-freqtrade for bots, aicoin-onchain for DEX swaps.
Design and operate reconciliation processes across portfolio management, custodian, and clearing systems. Use when building a daily position, cash, or transaction reconciliation process, investigating discrepancies between internal records and custodian records, diagnosing recurring break patterns from corporate actions or pricing differences, setting tolerance thresholds for position, cash, or market value matching, implementing three-way reconciliation, designing break investigation workflows with aging and escalation, normalizing multi-custodian feeds from Schwab, Fidelity, or Pershing, reconciling cost basis or accrued income, or preparing for examinations on books and records accuracy.
Implements Syncfusion DataManager for local/remote binding, CRUD, querying, caching, and middleware. Supports JsonAdaptor, ODataAdaptor, ODataV4Adaptor, UrlAdaptor, WebApiAdaptor, WebMethodAdaptor, RemoteSaveAdaptor, GraphQLAdaptor, CustomDataAdaptor, and CustomAdaptor. Covers Query class, filtering, sorting, paging, grouping, persistence, offline mode, caching, and error handling.
Diagnose and repair a broken Notte Function. Use when a deployed or scheduled Function has started failing or returning empty/wrong results — for example, “my function fn_... returns empty now,” “repair my scraper function,” “the Indeed function broke after the site changed,” “fix this failed function run,” or “diagnose why my scheduled function stopped working.” The user supplies the broken Function; this skill finds the root cause, re-explores the changed surface, verifies a fix in isolation, and promotes it behind a confirmation gate. Pairs with notte-functions-build, which builds Functions
First-run setup for [email protected] — installs ONNX/Brain/SONA add-ons, registers the MCP server, and verifies the install via `doctor`
Build an evidence-based competitive pricing strategy for ecommerce products. Use when a seller asks how to position a price against competitors, set regular and promotional prices, protect contribution margin, design bundles or price tiers, respond to competitor moves, or coordinate prices across Amazon, Shopify, TikTok Shop, Walmart, eBay, or other channels. Do not use for automated repricing implementation or claims of a mathematically proven optimal price without sufficient data.
Guide the design and management of trading venue connectivity and market data infrastructure. Owns the FIX session layer (logon, heartbeats, sequence number gaps, resend and gap recovery, disconnects). Use when building or troubleshooting FIX sessions for order routing or drop copy, integrating exchange protocols like OUCH, ITCH, PITCH, or Pillar, designing market data feed architecture (consolidated SIP vs direct feeds), handling trading halts or circuit breakers or LULD bands, planning co-location or failover and disaster recovery for exchange connectivity, implementing Rule 15c3-5 market access controls, or mapping symbology across venues and data sources. For order state machines and execution-report handling see order-lifecycle.
Reconcile what the numbers say with what users say, and design the study that settles it rather than restates it. Use when behavioural data and research findings point different ways. For reading the data on its own, use `behavioural-analytics`; for synthesising interviews on their own, use `affinity-diagram`.
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.