556 件 ・ 関連度順
概要と使いどころ
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
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。
Generate, derive, optimize, and diagnose structured AI image prompts for luminous cinematic Eastern xianxia environments under one inherited master canon with measurable divine scale, fixed five-layer space, 40–60% content-bearing breathing air, monumental Eastern architecture, railing-free edges, and coherent immortals and costume. Cover one-location scenic landmarks, immense inhabited celestial realms, cloud-borne palace cities, high-key Eastern sky-megastructure imagery, and the explicit `仙界大境原典` creative route. Use for Chinese fantasy scenery, places where immortals live, heavenly capitals, colossal celestial gates, architectural fragments above cloud seas, measurable divine scale, unusual framing, color and lighting direction, style-preserving variants, prompt rewrites, diagnosis, or direct image generation. Support 16:9, 21:9, 4:3, 3:2, 4:5, and 9:16; default to 16:9 for one prompt and never infer a nine-image series from a vertical ratio.
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。
Iteratively refine a product spec by debating with multiple LLMs (GPT, Gemini, Grok, etc.) until all models agree. Use when user wants to write or refine a specification document using adversarial development.
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。
Comprehensive guide for writing modern Neo4j Cypher queries. Essential for text2cypher MCP tools and LLMs generating Cypher queries. Covers removed/deprecated syntax, modern replacements, CALL subqueries for reads, COLLECT patterns, sorting best practices, set-based writes, and Quantified Path Patterns (QPP) for efficient graph traversal.
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。
Guide the creation of high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when the user wants to build an MCP server to integrate an external API or service, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
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。
Scroll-native one-page landing that overwhelms — concept, composition, asset placement, and scroll choreography derived from DESIGN.md and the measured landing-craft codex. Trigger: '랜딩 페이지', '원페이지', 'landing page', 'one-pager', 'hero to footer', '스크롤 랜딩', 'affinity.studio 같은'.
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。
Scroll-native one-page landing that overwhelms — concept, composition, asset placement, and scroll choreography derived from DESIGN.md and the measured landing-craft codex. Trigger: '랜딩 페이지', '원페이지', 'landing page', 'one-pager', 'hero to footer', '스크롤 랜딩', 'affinity.studio 같은'.
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。
Evaluate LLM systems using automated metrics, LLM-as-judge, and benchmarks. Use when testing prompt quality, validating RAG pipelines, measuring safety (hallucinations, bias), or comparing models for production deployment.
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。
Code-only motion design pipeline (no After Effects) for Raphaël / Howseen: launch films, showreels, product promos, LinkedIn/X videos, meme clips. Use when asked to make a motion design video, a promo/launch film, a showreel, "remake this video", a video from a prompt (e.g. "make a 15s motion graphics video…"), to change a video's music/SFX, or to prepare memes for posts. Covers the brief → beat map → stills → seek(t) HTML engine → Playwright render → ffmpeg → music/SFX → QA flow, plus asset sourcing (Mixkit, Pexels, Unsplash, svgl, 21st.dev) and every gotcha hit so far.
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。
大模型安全测评全流程管理工具。支持创建/更新模型和智能体测评资产、查询可用资源、发起合规测评任务、发起安全测评任务、分析测评结果等全流程操作。
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。
Multi-LLM collaborative brainstorming and planning. Use when user explicitly requests consultation with multiple AI models (ChatGPT, Gemini, other LLMs) before presenting an implementation plan, or asks to "consult the council", "ask other models", or "get perspectives from other AIs". Queries external LLM APIs, synthesizes their perspectives, and presents an adapted implementation plan.
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。
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
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。
Use when reviewing, designing, or modifying Java enterprise systems that use AI, LLMs, AI agents, RAG, tool calling, workflow automation, or model-based decision support and need EU AI Act regulatory awareness. This should trigger for requests such as Review a Java AI system for EU AI Act controls; Design governance for an AI agent with enterprise tools; Add human oversight and auditability to LLM workflows; Assess RAG or model-driven decision support before production release. Part of Plinth Toolkit
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。
Use when reviewing, designing, or modifying Java enterprise systems that use GenAI, LLMs, AI-assisted coding, RAG, AI agents, generated code, generated dependencies, prompt workflows, external model providers, or AI-enabled business logic and need ISO/IEC 42001 AI management system-aware engineering guidance. Part of Plinth Toolkit
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。
Lifecycle SOP for **per-model prompt artifacts** — the compiled prompts, instructions, few-shot demos, edit-format pins, and embedding-bound indices that change behavior when the underlying LM, dataset, or framework version changes. Activate when adopting compiled prompts (DSPy, GEPA, BootstrapFewShot output), when supporting multiple LMs in production, when a provider deprecates a model snapshot, or when a framework deprecates a config surface (LlamaIndex `ServiceContext` → `Settings`, Aider edit-format defaults). Do NOT activate for one-off raw prompt edits or for truly model-agnostic system prompts that have been swap-tested. Search keywords: prompt portability, model swap, recompile prompt, model deprecation, prompt per model, prompt breaks on new model, version compiled prompts.
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。
Decision SOP for serving LLMs with vLLM. Covers PagedAttention mental model, quantization/parallelism/batching tradeoffs, OOM triage, and when NOT to use vLLM. Activates when a coder-agent is choosing or tuning an inference engine, debugging vLLM throughput/latency/OOM, or comparing vLLM against TGI/SGLang/TensorRT-LLM/llama.cpp.
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 prompt optimization for LLMs and AI systems. Use when building AI features, improving agent performance, crafting system prompts, or optimizing LLM interactions. Masters prompt patterns and 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。
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
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。
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
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。
Choose optimal external AI models for code analysis, bug investigation, and architectural decisions. Use when consulting multiple LLMs via claudish, comparing model perspectives, or investigating complex Go/LSP/transpiler issues. Provides empirically validated model rankings (91/100 for MiniMax M2, 83/100 for Grok Code Fast) and proven consultation strategies based on real-world testing.
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。
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
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。
Score a website's AI answer-engine visibility 0–100 against the open AIV rubric, and, with the user's own API keys, check and track through the OpenAI, Perplexity, Gemini and Anthropic APIs whether AI engines actually cite it. Use when the user wants to know whether AI engines can find, parse, trust and cite their site, or whether they do. Triggers on: 'does ChatGPT cite us', 'AI citation rate', 'share of voice in AI answers', 'track AI visibility weekly', 'AIV', 'AI visibility', 'GEO audit', 'generative engine optimization', 'AEO', 'llms.txt', 'will AI cite my site', 'AI search ranking', 'get cited by ChatGPT'.
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。
Use when the user explicitly asks for the Inngest REST API v2, raw HTTP, OpenAPI, API docs, API authentication, or an endpoint that the Inngest CLI does not expose. Covers api-docs.inngest.com, llms.txt, the OpenAPI v2 spec, Bearer authentication with API keys or signing keys, production and local base URLs, raw curl/fetch requests, request-shape discovery, pagination, secret redaction, and when to prefer the `inngest-api-cli` skill instead.
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。
HuggingFace Transformers with biomedical LMs (BioBERT, PubMedBERT, BioGPT, BioMedLM) for scientific NLP: NER (genes, diseases, chemicals), relation extraction, QA, text classification, abstract summarization. Covers loading, biomedical tokenization, inference pipelines, fine-tuning. Alternatives: spaCy en_core_sci_lg (rule-based NER), Stanza (biomedical models), NLTK.
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。