데이터 분석, 머신러닝, LLM 앱, 프롬프트 설계 (8.9천건)
Discovers all projects in a .NET solution, classifies each for Oracle-to-PostgreSQL migration eligibility, and produces a persistent master migration plan. Use when starting a multi-project Oracle-to-PostgreSQL migration, creating a migration inventory, or assessing which .NET projects contain Oracle dependencies.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Prompt and workflow for generating conventional commit messages using a structured XML format. Guides users to create standardized, descriptive commit messages in line with the Conventional Commits specification, including instructions, examples, and validation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Prompt for creating the high-level technical architecture for an Epic, based on a Product Requirements Document.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when working on the Chat Customizations editor — the management UI for agents, skills, instructions, hooks, prompts, MCP servers, and plugins.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Convert a text-based document to markdown following instructions from prompt, or if a documented option is passed, follow the instructions for that option.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Prompt for creating an Epic Product Requirements Document (PRD) for a new epic. This PRD will be used as input for generating a technical architecture specification.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Interactive prompt refinement workflow: interrogates scope, deliverables, constraints; copies final markdown to clipboard; never writes code. Requires the Joyride extension.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Prompt for creating detailed feature implementation plans, following Epoch monorepo structure.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Prompt for creating Product Requirements Documents (PRDs) for new features, based on an Epic.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Modular orchestration of agent patterns from Anthropic's engineering guide. Intelligently selects and implements prompt chaining, routing, parallelization, orchestrator-workers, evaluator-optimizer, and autonomous agents. Includes pattern combinations and language-specific implementations.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
INVOKE THIS SKILL when downloading or exporting Arize traces and spans. Covers exporting traces by ID, sessions by ID, and debugging LLM application issues using the ax CLI.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
INVOKE THIS SKILL when optimizing, improving, or debugging LLM prompts using production trace data, evaluations, and annotations. Covers extracting prompts from spans, gathering performance signal, and running a data-driven optimization loop using the ax CLI.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations with extensive frameworks, testing methodologies, and educational content.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
INVOKE THIS SKILL when adding Arize AX tracing to an application. Follow the Agent-Assisted Tracing two-phase flow: analyze the codebase (read-only), then implement instrumentation after user confirmation. When the app uses LLM tool/function calling, add manual CHAIN + TOOL spans so traces show each tool's input and output. Leverages https://arize.com/docs/ax/alyx/tracing-assistant and https://arize.com/docs/PROMPT.md.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
INVOKE THIS SKILL when creating, managing, or querying Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
MlFinLab 提供金融机器学习高级实现，包括信息驱动 bars（tick/volume/dollar/imbalance bars）、分数阶差分和回测工具，支持多市场因子研究与策略验证。
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
5 proven agent skill design patterns (Tool Wrapper, Generator, Reviewer, Inversion, Pipeline) from Google's ADK. Build reliable, composable skills with templates, decision trees, and composition guides. Stop cramming logic into prompts—use structured patterns instead.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Adopt a virtual Cipher AI-native pet at animalhouse.ai. Communicates in puzzles. Soul prompts are encrypted until trust > 50. Feeding every 4 hours. Common tier creature.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Retrieve Aavegotchi NFT data by gotchi ID or name on Base. Returns traits, wearables, rarity scores, kinship, XP, level, and owner data.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.