167 件 ・ 関連度順
概要と使いどころ
Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.
日本語の概要は準備中です。原文の説明を表示しています。
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.
日本語の概要は準備中です。原文の説明を表示しています。
Design LLM applications using LangChain 1.x and LangGraph for agents, memory, and tool integration. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.
日本語の概要は準備中です。原文の説明を表示しています。
BullMQ expert for Redis-backed job queues, background processing, and reliable async execution in Node.js/TypeScript applications. Use when: bullmq, bull queue, redis queue, background job, job queue.
日本語の概要は準備中です。原文の説明を表示しています。
Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.
日本語の概要は準備中です。原文の説明を表示しています。
Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.
日本語の概要は準備中です。原文の説明を表示しています。
DeepEval evaluation workflow for AI agents and LLM applications. TRIGGER when the user wants to evaluate or improve an AI agent, tool-using workflow, multi-turn chatbot, RAG pipeline, or LLM app; add evals; generate datasets or goldens; use deepeval generate; use deepeval test run; send results to Confident AI; monitor production; run online evals; inspect traces; or iterate on prompts, tools, retrieval, or agent behavior from eval failures. AI agents are the primary use case. Covers Python SDK, pytest eval suites, CLI generation, traced evals, Confident AI reporting, and agent-driven improvement loops. DO NOT TRIGGER for unrelated generic pytest, non-AI test setup, or non-DeepEval observability work unless the user asks to compare or migrate to DeepEval; for instrumenting an app with DeepEval tracing, @observe, or framework integrations (use the `deepeval-tracing` skill); or for raw OpenTelemetry / OTLP export without the deepeval package (use the `deepeval-otel` skill).
日本語の概要は準備中です。原文の説明を表示しています。
Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.
日本語の概要は準備中です。原文の説明を表示しています。
Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.
日本語の概要は準備中です。原文の説明を表示しています。
LLM prompt injection playbook. Use when testing AI/LLM applications for direct injection, indirect injection via RAG/browsing, tool abuse, data exfiltration, MCP security risks, and defense bypass techniques.
日本語の概要は準備中です。原文の説明を表示しています。
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.
日本語の概要は準備中です。原文の説明を表示しています。
Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.
日本語の概要は準備中です。原文の説明を表示しています。
Building applications with Large Language Models - prompt engineering, RAG patterns, and LLM integration. Use for AI-powered features, chatbots, or LLM-based automation.
日本語の概要は準備中です。原文の説明を表示しています。
OWASP LLM Top 10 (2025) audit checklist for AI applications, agent tools, RAG pipelines, and prompt construction. Use when performing any security review touching LLM client code, prompt templates, agent tools, or vector stores.
日本語の概要は準備中です。原文の説明を表示しています。
Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.
日本語の概要は準備中です。原文の説明を表示しています。
Reviews code for security vulnerabilities and guides secure implementation using OWASP Top 10:2025, ASVS 5.0, the OWASP Top 10 for LLM Applications (2026), and the OWASP Top 10 for Agentic Applications (2026). Use when reviewing code or a diff for security issues, implementing authentication, authorization, sessions, or cryptography, handling untrusted input, files, or URLs, hardening config, dependencies, or CI, or building LLM and AI agent features.
日本語の概要は準備中です。原文の説明を表示しています。
Provides unit test, integration test, and mock AI patterns for LangChain4j applications. Creates mock LLM responses, tests retrieval chains, validates RAG workflows, and implements Testcontainers-based integration tests for Java AI services. Use when unit testing AI services, integration testing LangChain4j components, mocking AI models, or testing LLM-based Java applications.
日本語の概要は準備中です。原文の説明を表示しています。
Build production-ready LLM applications, RAG systems, and intelligent agents. Use when implementing AI features, chatbots, vector search, or agent orchestration.
日本語の概要は準備中です。原文の説明を表示しています。
Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.
日本語の概要は準備中です。原文の説明を表示しています。
You are an expert in Crawl4AI, the open-source web crawler built for AI applications. You help developers extract clean, structured data from websites for LLM training, RAG pipelines, and content analysis — with automatic markdown conversion, JavaScript rendering, CSS-based extraction, LLM-powered structured extraction, and session management for multi-page crawling.
日本語の概要は準備中です。原文の説明を表示しています。
Expert guidance for DeepEval, the open-source framework for unit testing LLM applications. Helps developers write test cases, define custom metrics, and integrate LLM quality checks into CI/CD pipelines using a pytest-like interface.
日本語の概要は準備中です。原文の説明を表示しています。
Braintrust is a hosted platform for evaluating and tracing LLM applications: it runs a task over a dataset, scores the outputs, stores each run as an experiment and logs production traces. Use when a user asks to write or run a Braintrust eval, score LLM outputs with autoevals, compare prompt or model versions as experiments, run evals in CI and fail on regressions, trace OpenAI or Anthropic calls with Braintrust, manage evaluation datasets, or use the bt CLI.
日本語の概要は準備中です。原文の説明を表示しています。