Skills
96,981 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Xiaohongshu personal note data review — based on the note data from the user's own creator backend (Creator Center / Pugongying), it does funnel diagnosis, bottleneck attribution and cross-note pattern discovery. Use when the user says "note data review", "why did this one get no traffic", "take a look at this one's data for me", "Xiaohongshu data analysis", "high impressions but no clicks", "follower growth is bad", "which type of content should I double down on", "I exported my backend data, help me take a look", or directly pastes backend data screenshots/CSV/Excel/a few numbers. It only analyzes the performance of notes on the user's own account; it does not scrape platform data or do engagement farming.
Explains, optimises, writes, and documents SQL queries. Use when asked to explain a SQL query, optimise slow SQL, translate SQL to plain English for non-technical stakeholders, write a query from a natural language description, or produce query documentation. Produces plain-English explanations, annotated optimised queries, or a data dictionary covering output shape, assumptions, and known limitations. Works across PostgreSQL, MySQL, BigQuery, Snowflake, and standard SQL.
Use when the user needs to generate or validate Protheus data dictionary configuration scripts -- covering SX2 (table headers), SX3 (fields), SIX (indexes), SXG (field groups), SXA (form folders/tabs), SX1 (report questions), SX5 (generic tables), SXB (F3 lookup queries), and SX7 (triggers). Also triggers on Portuguese phrasing like "criar campo customizado", "cadastrar tabela customizada", "dicionario de dados", "criar indice SIX", "gatilho de campo", "tabela generica SX5", "consulta F3", or field-prefix names like ZA1/ZZ1.
Guide for implementing Syncfusion 3D Chart in ASP.NET Core. Always use this skill when user needs 3D chart functionality, data visualization, series configuration, axes setup, or interactive chart features like tooltips and selection. Covers setup, data binding, chart types, styling, and user interactions.
Guide for implementing Syncfusion Bullet Chart in ASP.NET Core applications. Use this skill when building comparative performance visualizations, KPI dashboards, progress tracking, goal comparison charts, or any scenario requiring actual vs. target value display. Bullet charts effectively compare actual performance against target benchmarks in a compact format—ideal for dashboards, executive summaries, and performance monitoring systems. Trigger when user mentions bullet chart, performance comparison, target visualization, KPI dashboard, progress bars with targets.
Implement interactive hierarchical TreeMap visualizations in ASP.NET Core using Syncfusion. Create tree maps for hierarchical data with multiple layouts, color mapping, drill-down capabilities, and interactive features. Use this skill whenever the user needs to visualize hierarchical data structures, display nested categories with relative sizes, implement interactive drill-down navigation, or enable data exploration through tree maps.
Implements Syncfusion Flutter Maps (SfMaps) for interactive geographical data visualization in Flutter apps. Use when working with shape layers, tile layers, choropleth maps, or map markers and overlays. This skill covers GeoJSON rendering, OpenStreetMap/Bing Maps integration, bubbles, legends, tooltips, zoom and pan, and spatial data visualization.
Implements Syncfusion Flutter Pyramid Charts (SfPyramidChart, PyramidSeries) for proportional and hierarchical data visualization. Use when working with pyramid charts, funnel-style visualizations, or segment-based data comparisons in Flutter. This skill covers series configuration, segment exploding, gap ratio, PyramidMode, data labels, legends, and tooltip customization.
Implements Syncfusion Flutter Spark Charts (SfSparkLineChart, SfSparkAreaChart, SfSparkBarChart, SfSparkWinLossChart) for compact, lightweight data visualization. Use when working with micro charts, sparklines, KPI indicators, or inline trend charts in Flutter dashboards. This skill covers chart configuration, data binding, markers, tooltips, and trackball for all four spark chart types.
Implements Syncfusion Flutter TreeMap (SfTreemap) for visualizing hierarchical and proportional data in Flutter apps. Use when working with treemap layouts, nested rectangles, drill-down navigation, or hierarchical data visualization. This skill covers layouts, labels, tooltips, legends, selection, color mapping, and drilldown navigation.
Tavily AI search platform with 5 modes: Search (web/news/finance), Extract (URL content), Crawl (website crawling), Map (sitemap discovery), and Research (deep research with citations). Use for: web search with LLM answers, content extraction, site crawling, deep research.
Trade quality-control scorecard and postmortem analysis. Deterministic, zero-token, zero external dependencies. Computes a 3-axis weekly grade (Result 40%, Growth 30%, Process 30%) from equity curves and trade logs. Includes 16 risk-adjusted performance metrics (Sharpe, Sortino, Calmar, MAR, Ulcer Index, Profit Factor, Expectancy), idle-cash penalty detection, and 8-tag deterministic trade postmortems (thesis verification, regime violation, sizing errors, premature entries, perfect exits). Pure Python stdlib — works with any broker, any ledger format via pluggable data adapter. Use when: building a trading quality-control system, setting up anti-stall guardrails for a trading pod, implementing a weekly trader scorecard, running post-trade analysis, detecting "standing still" (idle cash accumulation) in a systematic trading operation, or replacing self-reported P&L with broker-verified fill reconciliation. DO NOT USE FOR: live order execution, broker integration, real-time risk management, or portfolio optimization.
The Earnings Analyst. Reads a company's reported numbers straight from SEC EDGAR (XBRL company facts from its 10-Qs and 10-Ks, plus the latest earnings 8-K and its press release) and sums up what changed: revenue, gross, operating and net margins, net income and diluted EPS, quarter by quarter, each against the same quarter a year earlier, with the trailing twelve months. Writes a metrics file the screener reads. Use when the user says “how did Apple's earnings look”, “analyze the earnings”, “what changed this quarter”, “is revenue growing”, “check the 10-Q”, “compare margins”, or “run the analyst on my holdings”
Validate wallet balances in TRES Finance against DeBank and generate a discrepancy report. Use this skill whenever the user asks to validate, verify, cross-check, or audit their TRES balances against on-chain data or DeBank. Also trigger when the user asks if their balances are correct or wants to see discrepancies. Only EVM-compatible wallets are supported.
Aggregate customer feedback from multiple sources — support tickets, NPS comments, Slack messages, G2 reviews, call transcripts, survey responses — into a unified VoC report with theme clustering, sentiment analysis, trend detection, and actionable recommendations for product, marketing, and CS teams. Chains review-site-scraper for public review data.
X (Twitter) data extraction and scraping. Use when asked to scrape tweets, extract followers, search Twitter/X users, download media from tweets, monitor X accounts, or analyze Twitter engagement. Triggers on twitter, x.com, tweet, follower, following, retweet, quote tweet, scrape, OSINT.
Use when the user needs to analyze WeRead reading history, generate reading reports, visualize reading statistics, notes, bookshelf, word clouds, heatmaps, radar charts, or export a fully formatted HTML report. Also applies to creating an example reading report for AI founders. Not for general book recommendations, or raw WeRead API queries that do not produce a report.
Build credit scoring models to predict default probability from borrower characteristics. Use this skill when the user needs to assess creditworthiness, build a credit scorecard, or evaluate lending risk — even if they say 'predict default risk', 'credit scoring', or 'loan approval model'.
Analyze what an audience looks like -- demographic breakdowns, top field values, coverage rates, and distributions. Use when the user wants to understand audience composition, view segment demographics, or analyze field coverage for a segment.
OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.
Connect Workers to PostgreSQL/MySQL with Hyperdrive's global pooling and caching. Use when: connecting to existing databases, setting up connection pools, using node-postgres/mysql2, integrating Drizzle/Prisma, or troubleshooting pool acquisition failures, TLS errors, nodejs_compat missing, or eval() disallowed.
CloudNative-PG (CNPG) PostgreSQL database management for the Kubernetes homelab. Covers shared platform cluster, dedicated per-app clusters, credential provisioning, cross-namespace replication via kubernetes-replicator, and monitoring. Use when: (1) Adding a new database for an application, (2) Creating a dedicated CNPG cluster, (3) Setting up database credentials and cross-namespace replication, (4) Debugging database connectivity or CNPG cluster health, (5) Adding PostgreSQL extensions for specialized workloads. Triggers: "database", "postgresql", "postgres", "cnpg", "cloudnative-pg", "pooler", "pgbouncer", "database credentials", "db password", "managed roles", "Database CRD", "database cluster", "shared database", "dedicated database", "cnpg cluster"
Define, compare, and audit constrained optimization decisions with explicit variables, units, hard and soft constraints, feasibility, Pareto tradeoffs, and honest solver evidence. Do not use for statistical modeling or ML fitting, which belong to data-scientist or ml-engineering, or for operating a named solver.
Creates and launches PostHog surveys through MCP, including NPS/CSAT popovers, hosted feedback forms, and headless surveys. Guides survey type selection, audience targeting, draft review, and launch readiness. Use when asked to create a survey or form, or before calling survey-create. For investigating an existing survey's delivery or responses, use debugging-surveys instead.
[Deprecated] Optional advanced tool for complex data modeling. For simple MySQL table creation, use relational-database-tool directly; for PostgreSQL / CloudBase PG schema work, use postgresql-development. New environments should use PostgreSQL DDL via queryPgDatabase/managePgDatabase—see postgresql-development skill instead
Data validation patterns including schema validation, input sanitization, output encoding, and type coercion. Use when implementing validate, validation, schema, form validation, API validation, JSON Schema, Zod, Pydantic, Joi, Yup, sanitize, sanitization, XSS prevention, injection prevention, escape, encode, whitelist, constraint checking, invariant validation, data pipeline validation, ML feature validation, or custom validators.
Document or design a database schema with entity relationships, table definitions, constraints, indexes, and access patterns. Use when asked to design a database, document an existing schema, model entities and relationships, define table structures, plan an index strategy, or produce a data model for review. Produces a structured schema document covering an ER diagram, table DDL definitions, index strategy, access pattern analysis, normalization decisions, and migration notes.
Databricks AI Runtime, the `databricks air` CLI commands for submitting and managing GPU training workloads on Databricks serverless compute. Use for: writing and submitting `databricks air` workload YAML, passing hyperparameters and secrets, checking run status, listing/cancelling runs, streaming a run's logs and watching its progress, custom Docker image setup, and environment configuration
Fetches the DORA metrics Deployment Frequency and Lead Time for Changes, per project and per repo, from the provider's API (GitHub, GitLab, Bitbucket, or Azure DevOps — not local git). Use this skill whenever the user asks to run or update the DORA metrics, measure the deployment frequency or lead time of a project (e.g. "Example Project"), generate the biweekly metrics report, or asks how many deploys a project made or how long a change takes to reach production — regardless of which of the four providers the project's repos live on. Also trigger on phrases like: "run the DORA metrics", "metrics for Example Project", "deployment frequency for [project]", "lead time for [project]", "biweekly metrics report", "how many deploys did we do this sprint", "add a GitLab/Bitbucket/Azure DevOps project to the DORA metrics"
Ecommerce business review for D2C stores from order transaction CSVs. Runs the bundled Python engine (KPI trees, ~30 pass/watch/fail health checks, 30d/90d/365d windows), then interprets the results: either a full narrative business review written to REVIEW.md, or an inline answer to a focused question.
Web scraping, crawling, searching, and browser automation via the Firecrawl CLI (firecrawl). Use when scraping URLs to markdown/HTML, crawling entire websites, discovering all URLs on a site, running web searches with optional scraping, launching cloud browser sessions for automation, or using the AI agent for natural-language web queries. Requires firecrawl-cli installed (npm install -g firecrawl-cli) and authentication (firecrawl login or FIRECRAWL_API_KEY env var). Triggers on: scrape URL, crawl site, map website URLs, firecrawl search, browser automation with firecrawl, web agent query.
This skill should be used when the user invokes "/fundamental-report" followed by a stock ticker. Generates a comprehensive fundamental analysis research note at summary depth covering all 14 analysis areas with key metrics, signal rating, cross-validation, and investment thesis arguments.
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.