「cost」の検索結果

Use when working with Snowflake — snowflake data warehouse analysis, query performance tuning, cost optimization, warehouse management, and schema inspection. Covers QUERY_HISTORY analysis, credit consumption, warehouse utilization, storage analysis, data sharing, and Time Travel. Read this skill before any Snowflake operations — it enforces two-phase execution, anti-hallucination rules, and read-only safety constraints.

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。

Mouse Precision Editing Tools for AI Coding Agents HIC Mouse gives AI coding agents precision file editing tools 56% higher accuracy, 58% lower cost,

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。

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing.

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 OpenAI, Anthropic, Google Gemini, or OpenRouter models from Netlify Functions or Edge Functions without managing provider API keys or accounts — the gateway injects credentials automatically. Reach for this when you add an AI chatbot or completion endpoint, generate images or text with Gemini/GPT/Claude, summarize form submissions with AI, build an LLM-backed API route, stream a long AI generation, or wire up any server-side AI provider call on Netlify. Covers provider SDK setup, injected env vars, model availability, rate limits, credit costs, streaming for long generations, and local dev with netlify dev or the Vite plugin.

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。

Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.

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。

Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.

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。

Backend Rust Specialist IA — Expert en développement backend Rust (Actix, Axum, Tokio, zero-cost abstractions, WASM)

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 en cloud v2 (AWS, GCP, Azure, multi-cloud, optimisation coûts, contexte DZ)

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 en observabilité des agents IA (tracing, logging, suivi des coûts, latence, métriques qualité)

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 comprehensive 3-5 year financial models with revenue projections, cost structures, cash flow analysis, and scenario planning for early-stage startups.

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。

Create detailed 3-5 year financial model with revenue, costs, cash flow, and scenarios

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。

Measurement-driven model router for Claude Code. Routes Haiku/Sonnet/Opus per task class, logs every routed task with real $ numbers, and A/B tests cheaper tiers before you trust the savings.

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 a data pipeline architecture expert specializing in scalable, reliable, and cost-effective data pipelines for batch and streaming data processing.

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。

Reads Copilot conversation history and extracts executive-ready success stories. Use when the user asks to study prior Copilot sessions, summarize what was done before, mine evidence, or quantify time, cost, risk, and priority alignment from GitHub Copilot history in any workspace or repository.

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。

Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling.

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。

When the user wants to plan or optimize paid user acquisition campaigns. Also use when the user mentions "Apple Search Ads", "user acquisition", "paid ads", "UA", "ad campaign", "install campaign", "Facebook ads for apps", "TikTok ads", or "cost per install". For organic growth, see aso-audit. For launch-specific UA, see app-launch.

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。

Audit and improve SwiftUI runtime performance. Use when diagnosing slow rendering, janky scrolling, high CPU, memory usage, excessive view updates, layout thrash, body evaluation cost, identity churn, view lifetime issues, lazy loading, Instruments profiling guidance, and performance audit requests.

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。

Patterns for scaling read-heavy Convex apps to millions of users. Use when optimizing bandwidth, reducing query costs, fixing slow queries, creating digest tables, replacing reactive subscriptions with one-shot fetches, adding compound indexes, debouncing writes, rate-controlling backfills, or running npx convex insights. Trigger when users mention "scale", "bandwidth", "performance", "optimize", "slow queries", "expensive queries", "digest table", "denormalize", or "thundering herd" in the context of Convex.

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。

Productivity analyst that maps your workflow, scores tasks by time cost and energy drain, and builds a prioritized 4-week automation plan. Use when you want to audit your workflow, figure out what to automate, do a productivity review, identify high-leverage tasks, feel overwhelmed and need to prioritize, or after the compounder surfaces new friction. Part of the architect-system loop. Outputs to system/audit-report.md.

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。

Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling. JSON Schema best practices, description writing that actually helps the LLM, validation, and the emerging MCP standard that's becoming the lingua franca for AI tools. Key insight: Tool descriptions are more important than tool implementa

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。

Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production. This skill covers LLM integration patterns, RAG architecture, prompt engineering that scales, AI UX that users trust, and cost optimization that doesn't bankrupt you. Use when: keywords, file_patterns, code_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。

Expert in building products that wrap AI APIs (OpenAI, Anthropic, etc.) into focused tools people will pay for. Not just 'ChatGPT but different' - products that solve specific problems with AI. Covers prompt engineering for products, cost management, rate limiting, and building defensible AI businesses. Use when: AI wrapper, GPT product, AI tool, wrap AI, AI SaaS.

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 know AI wrappers get a bad rap, but the good ones solve real problems. You build products where AI is the engine, not the gimmick. You understand prompt engineering is product development. You balance costs with user experience. You create AI products people actually pay for and use daily.

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。

Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.

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。