「selection」の検索結果

Expert en optimisation des coûts d'agents IA (sélection modèle, caching, compression prompts, budgeting tokens)

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。

Master local LLM inference, model selection, VRAM optimization, and local deployment using Ollama, llama.cpp, vLLM, and LM Studio. Expert in quantization formats (GGUF, EXL2) and local AI privacy.

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。

RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.

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 database architect specializing in data layer design from scratch, technology selection, schema modeling, and scalable database architectures.

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。

Database design principles and decision-making. Schema design, indexing strategy, ORM selection, serverless databases.

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 typefaces based on voice and context, not trends. Use when selecting fonts for a project, evaluating type choices, or understanding what a typeface communicates before anyone reads a word.

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。

How to use windows-computer-use-mcp effectively — tool selection, focus rules, safety, and mission design.

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。

RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.

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。

Python development principles and decision-making. Framework selection, async patterns, type hints, project structure. Teaches thinking, not copying.

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。

Master local LLM inference, model selection, VRAM optimization, and local deployment using Ollama, llama.cpp, vLLM, and LM Studio. Expert in quantization formats (GGUF, EXL2) and local AI privacy.

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 guide for using n8n-mcp MCP tools effectively. Use when searching for nodes, validating configurations, accessing templates, managing workflows, or using any n8n-mcp tool. Provides tool selection guidance, parameter formats, and common patterns.

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 choosing an animation engine, defining motion tokens, adding scroll effects or micro-interactions, or auditing motion for accessibility and performance. Covers engine selection (GSAP, Motion.dev, Anime.js, React-Spring, Three.js, CSS Native), DTCG motion tokens, and the anti-slop/a11y quality gate.

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。

Database design principles and decision-making. Schema design, indexing strategy, ORM selection, serverless databases.

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 integrating Google Gemini API into projects. Covers model selection, multimodal inputs, streaming, function calling, and production best practices.

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。

API design principles and decision-making. REST vs GraphQL vs tRPC selection, response formats, versioning, pagination.

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 Agent engineering: pattern selection (12-mode matrix), data simulation, convergence iteration. Covers intent routing, function calling, ReAct, MCP, prompt chaining, guardrails, evaluation.

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 animation values that feel native on Apple platforms instead of guessing springs. Use when: picking a spring/duration/easing for any transition; an animation "feels off", sluggish, floaty, wobbly, or cheap; tuning a sheet, drawer, panel, selection lens, button press, or drag release; wiring gesture-driven motion; or someone asks "what spring should I use", "why does this feel wrong", "make it feel native/premium", "match Apple's feel". Also fires on hand-picked magic numbers in .spring(), on animations that jump when interrupted, and on motion that only starts after a gesture ends.

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, review, or improve data visualizations using Swift Charts. Use when building bar, line, area, point, pie, or donut charts; when adding chart selection, scrolling, or annotations; when plotting functions with vectorized BarPlot, LinePlot, AreaPlot, or PointPlot; when customizing axes, scales, legends, or foregroundStyle grouping; or when creating specialized visualizations like heat maps, Gantt charts, stacked/grouped bars, sparklines, or threshold lines.

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。

Display and manipulate PDF documents using PDFKit. Use when embedding PDFView to show PDF files, creating or modifying PDFDocument instances, adding annotations (highlights, notes, signatures), extracting text with PDFSelection, navigating pages, generating thumbnails, filling PDF forms, or wrapping PDFView in SwiftUI.

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。

Translate Figma designs into production-ready SwiftUI code with 1:1 visual fidelity using the Figma MCP workflow. Trigger when the user provides Figma URLs or node IDs and wants iOS/SwiftUI implementation, asks to implement a design or component from Figma for an iOS app, or references Figma selections in the context of an Xcode/SwiftUI project. Also trigger when user asks to inspect Figma designs for iOS planning, fetch design tokens for SwiftUI, or convert Figma assets for Xcode. Requires a working Figma MCP server connection. Do NOT trigger for web/React implementations.

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 designing, running, or judging an experiment on a Syntropic137 workflow - comparing prompt variants, fan-out vs single agent, adversarial cross-model review, model selection (can Haiku do this job), phase splits, or tool grants. Trigger phrases include "run a workflow experiment", "A/B the workflow", "does fan-out help", "which model for this phase", "is this prompt better", "measure review quality", "why did the workflow do that". Do NOT use for a one-off workflow run that is just doing work, for authoring a workflow that is not being compared against anything, or for debugging a single failed execution (that is ordinary debugging).

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 hyperparameter selection — learning rate formulas, LoRA rank, batch size, group size, schedules, and model-specific tuning. Use when the user asks about learning rate, batch size, hyperparameter tuning, or how to configure training parameters.

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。

Web browser game development principles. Framework selection, WebGPU, optimization, PWA.

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。

Game art principles. Visual style selection, asset pipeline, animation workflow.

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。