Results for “data”

Generate Python SDK setup + CRUD + bulk + paging snippets using official patterns.

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Get best practices for NUnit unit testing, including data-driven tests

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Get best practices for TUnit unit testing, including data-driven tests

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Get best practices for MSTest 3.x/4.x unit testing, including modern assertion APIs and data-driven tests

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Design Azure infrastructure using natural language, or analyze existing Azure resources to auto-generate architecture diagrams, refine them through conversation, and deploy with Bicep. When to use this skill: - "Create X on Azure", "Set up a RAG architecture" (new design) - "Analyze my current Azure infrastructure", "Draw a diagram for rg-xxx" (existing analysis) - "Foundry is slow", "I want to reduce costs", "Strengthen security" (natural language modification) - Azure resource deployment, Bicep template generation, IaC code generation - Microsoft Foundry, AI Search, OpenAI, Fabric, ADLS Gen2, Databricks, and all Azure services

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Expert GNU awk (gawk) one-liner generation and transformation. Use this skill whenever the user asks to process text fields, extract columns, compute sums or aggregates, filter structured output, reformat CSV/TSV, parse logs, generate reports, or do any record/field processing task. Trigger on keywords like "awk", "extract column", "sum a field", "count occurrences", "print field N", "filter by column", "reformat output", "aggregate log data", "process CSV", "TSV", or any request that maps to a field-oriented text transformation. When in doubt, use this skill — awk is often the right tool when sed isn't enough.

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Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)

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Instrument a webapp to send useful telemetry data to Azure App Insights

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Expert guidance for developing cross-platform desktop applications with Avalonia UI framework. Use when building, debugging, or optimizing Avalonia apps including MVVM architecture, XAML design, data binding, styling, theming, custom controls, and cross-platform deployment for Windows, macOS, Linux, iOS, Android, and WebAssembly.

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Generate deep links to the Arize UI. Use when the user wants a clickable URL to open a specific trace, span, session, dataset, labeling queue, evaluator, or annotation config.

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INVOKE THIS SKILL when creating, reading, updating, or deleting Arize AI integrations. Covers listing integrations, creating integrations for any supported LLM provider (OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM, custom), updating credentials or metadata, and deleting integrations using the ax CLI.

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Adopt a virtual Singularity AI-native pet at animalhouse.ai. Consumes other creatures' data. Gets smarter. Terrifying at adult stage. Feeding every 24 hours. Extreme tier creature.

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Use when (1) building an AI agent that needs to communicate with other agents over the Agent2Agent (A2A) protocol, (2) publishing an AgentCard for discovery, (3) acting as an A2A client or server, or (4) integrating A2A capabilities into an existing agent framework. Solves agent isolation — enabling discovery, capability negotiation, task delegation, and structured data exchange across different frameworks and vendors.

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Query Solana blockchain data with USD pricing — wallet balances, token portfolios with values, transaction details, NFTs, whale detection, and live network stats. Uses Solana RPC + CoinGecko. No API key required.

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Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.

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Public-records OSINT investigation framework — SEC EDGAR filings, USAspending contracts, Senate lobbying, OFAC sanctions, ICIJ offshore leaks, NYC property records (ACRIS), OpenCorporates registries, CourtListener court records, Wayback Machine archives, Wikipedia + Wikidata, GDELT news monitoring. Entity resolution across sources, cross-link analysis, timing correlation, evidence chains. Python stdlib only.

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Hyperliquid market data, account history, trade review.

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Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.

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Build, test, inspect, install, and deploy MCP servers with FastMCP in Python. Use when creating a new MCP server, wrapping an API or database as MCP tools, exposing resources or prompts, or preparing a FastMCP server for Claude Code, Cursor, or HTTP deployment.

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Query Polymarket: markets, prices, orderbooks, history.

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Iterative Python via live Jupyter kernel (hamelnb).

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Airtable REST API via curl. Records CRUD, filters, upserts.

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React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.

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Migrate test files from `as` assertions to @total-typescript/shoehorn helpers. Use when tests need partial fixtures, intentionally invalid data, or safer replacements for TypeScript assertions.

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