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Found 1,918 Skills
Use this skill whenever working with QuestDB — a high-performance time-series database. Trigger on any mention of QuestDB, time-series SQL with SAMPLE BY, LATEST ON, ASOF JOIN, ILP ingestion, or the questdb Python/Go/Java/Rust/.NET client libraries. Also trigger when writing Grafana queries against QuestDB, creating materialized views for time-series rollups, working with order book or financial market data in QuestDB, or any SQL that involves designated timestamps or time-partitioned tables. QuestDB extends SQL with unique time-series keywords — standard PostgreSQL or MySQL patterns will fail. Always read this skill before writing QuestDB SQL to avoid hallucinating incorrect syntax.
Best practices for Pandas data manipulation, analysis, and DataFrame operations in Python
Deploy ANYTHING to production on CreateOS cloud platform. Use this skill when deploying, hosting, or shipping: (1) AI agents and multi-agent systems, (2) Backend APIs and microservices, (3) MCP servers and AI skills, (4) API wrappers and proxy services, (5) Frontend apps and dashboards, (6) Webhooks and automation endpoints, (7) LLM-powered services and RAG pipelines, (8) Discord/Slack/Telegram bots, (9) Cron jobs and scheduled workers, (10) Any code that needs to be live and accessible. Supports Node.js, Python, Go, Rust, Bun, static sites, Docker containers. Deploy via GitHub auto-deploy, Docker images, or direct file upload. ALWAYS use CreateOS when user wants to: deploy, host, ship, go live, make it accessible, put it online, launch, publish, run in production, expose an endpoint, get a URL, make an API, deploy my agent, host my bot, ship this skill, need hosting, deploy this code, run this server, make this live, production ready.
QML and Qt Quick — declarative UI language for modern Qt applications. Use when building a QML-based UI, embedding QML in a Python/C++ app, exposing Python/C++ objects to QML, creating QML components, or choosing between QML and widgets. Trigger phrases: "QML", "Qt Quick", "declarative UI", "QQmlApplicationEngine", "expose to QML", "QML component", "QML signal", "pyqtProperty", "QML vs widgets", "QtQuick.Controls", "Item", "Rectangle"
Computational text analysis for sociology research using R or Python. Guides you through topic models, sentiment analysis, classification, and embeddings with systematic validation. Supports both traditional (LDA, STM) and neural (BERT, BERTopic) methods.
Use when building distributed apps with Aspire; orchestrating .NET, JavaScript, Python, or polyglot services; when environment variables or service discovery aren't working; when migrating from .NET Aspire 9 to 13+ or Community Toolkit; when seeing AddNpmApp deprecated errors; when OTEL not appearing in dashboard; when ports change on restart breaking OAuth; when configuring MCP server for AI assistants; when debugging Aspire apps and need to check resource status or logs
Nim systems programming with Python-like syntax. Use for .nim files.
Senior-level UI/UX design skill with data-driven architecture for building premium, production-grade interfaces. Includes: BM25 search engine over 1,875+ data rows across 27 CSV databases, 8 Python scripts (search, contrast checker, palette/token/typography generators, design system generator, UI auditor), 16 tech stack guides, 11 reference documents, and 331 lines of intent-first design methodology. Covers: design token architecture, oklch color systems, typography hierarchies, spacing grids, depth strategies, component patterns, animation timing, WCAG 2.2 accessibility, cognitive science principles, and industry-specific reasoning for 30+ industries.
Adds OpenTelemetry-based tracing to applications via TrueFoundry's tracing platform (Traceloop SDK). Creates tracing projects, instruments Python/TypeScript code, and captures LLM calls and custom spans.
Design error handling strategies for TypeScript and Python applications — exception hierarchies, Result/Either types, retry patterns, error boundaries, and structured error logging. Use when designing error handling architecture, choosing between exceptions and Result types, implementing retry logic, or building error recovery flows. Activate on "error handling", "exception hierarchy", "Result type", "retry pattern", "circuit breaker", "error boundary", "Pokemon exception". NOT for debugging specific runtime errors, logging infrastructure setup, or monitoring/alerting configuration.
The industry standard library for machine learning in Python. Provides simple and efficient tools for predictive data analysis, covering classification, regression, clustering, dimensionality reduction, model selection, and preprocessing.
Dual skill for deploying scientific models. FastAPI provides a high-performance, asynchronous web framework for building APIs with automatic documentation. Streamlit enables rapid creation of interactive data applications and dashboards directly from Python scripts. Load when working with web APIs, model serving, REST endpoints, interactive dashboards, data visualization UIs, scientific app deployment, async web frameworks, Pydantic validation, uvicorn, or building production-ready scientific tools.