「cost」の検索結果

Use when a UE5 task involves Niagara or Chaos destruction: a Niagara fireball, projectile trail, impact explosion, muzzle flash, spell, smoke, sparks, debris, Niagara fluids or flipbooks; "Niagara is too expensive", stat NiagaraOverview, Effect Types, pooling, Data Channels, lightweight emitters, VFX scalability; judging an effect's readability, value, color, timing, white blowout or strobing; or a wall that shatters, geometry collection fracture, Dataflow, damage thresholds, floating chunks, debris cost, Chaos Visual Debugger.

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 a UE5 task involves characters in motion: "import FBX from Maya" with a skeleton, "retarget animations", IK Rig, IK Retargeter, feet sinking or floating after retarget, mocap cleanup, Motion Matching, Pose Search, Choosers, GASP, state machine vs motion matching, root motion, offset root bone, foot IK, Control Rig, Animation Blueprint cost, "animation is expensive", budget allocator, URO, MetaHuman LODs, ML Deformer, cloth.

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 building a 2D game in Unity 6.3 URP: pixel-art or HD sprite import and PPU, sprite atlases, Tilemaps, Rule Tiles and reskins, the 2D Renderer and 2D lights with normal maps, 2D animation, a platformer or top-down controller ("make a platformer controller that feels good", coyote time, jump buffer, one-way platforms, spikes), Pixel Perfect Camera, a follow or room camera, hit stop and screen shake; or when pixel art looks blurry or shimmers, tiles show seams, 2D lights do nothing or cost too much, the jump feels floaty, or the camera loses the player when falling.

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 generated assets must keep a consistent style, character, or product look and references stop scaling, or when a user asks to train a custom model through the Scenario MCP, fine-tune a LoRA, clone a voice, curate a training dataset, choose a base model, set epochs and sample prompts, estimate training cost, diagnose a trained model that lost its identity or has one epoch, or generate with a trained model. Keywords: custom model, LoRA, dataset curation, epoch previews.

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。

Develop a business case for an initiative using costs, benefits, risks and evidence. Use when a sponsor needs budget or timeline justification.

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。

Compare feasible approaches to a customer problem and recommend a path with costs, constraints and evidence. Use for an architecture or delivery decision, not implementation.

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。

Refactor a too-long SKILL.md by progressive disclosure: measure token cost, classify every section KEEP/CUT/MOVE, shorten the body into references/ and scripts/, verify nothing was lost. Don't use for authoring new skills, eval retrofits, or prose.

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。

Writes idiomatic Rust code with memory safety, zero-cost abstractions, ownership patterns, lifetime management, trait hierarchies, and async applications with tokio.

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。

Designs cloud architectures, creates migration plans, generates cost optimization recommendations, and produces disaster recovery strategies across AWS, Azure, and GCP.

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。

Deploy applications to AWS. Triggers on phrases like: deploy to AWS, host on AWS, run this on AWS, AWS architecture, estimate AWS cost, generate infrastructure. Analyzes any codebase and deploys to optimal AWS services.

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。

Analyze a software codebase for algorithmic complexity and performance hotspots, then propose or implement safe optimizations without breaking behavior. Use when Codex is asked to scan many files, find inefficient loops, nested iteration, repeated scans, costly rendering/recomputation, N+1 queries, avoidable O(n^2) or O(n) operations, or reduce complexity such as O(n^2) to O(n log n) / O(n), while preserving tests, APIs, outputs, and maintainability.

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。

Clinical study operations — protocol structure, endpoint selection, eligibility design, sample-size and power planning, site feasibility, and documentation readiness. Use when planning, auditing, or costing a study.

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。

Operations management across process optimization, efficiency, and continuous improvement. Use when designing workflows, building capacity plans, evaluating vendors, running Lean Six Sigma DMAIC projects, or optimizing cost-per-unit.

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 and compare technology stacks with TCO analysis, security assessment, and ecosystem health scoring. Use when comparing frameworks, calculating total cost of ownership, assessing migration paths, or analyzing ecosystem viability.

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。

Scan codebases for technical debt with AST parsing, prioritize by impact, and generate trend dashboards. Use when tracking tech debt, prioritizing refactoring, calculating cost-of- delay, planning sprint debt, or reporting debt to execs.

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。

This skill should be used when the user asks to "optimize Snowflake queries", "analyze Snowflake SQL performance", "size Snowflake warehouses", "review Snowflake data models", or "troubleshoot Snowflake cost issues".

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。

ML engineering skill for productionizing models, building MLOps pipelines, and integrating LLMs. Covers model deployment, feature stores, drift monitoring, RAG systems, and cost 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。

Design, review, and validate Azure cloud architectures. Use when choosing Azure compute, storage, networking, or identity services, or applying the Azure Well-Architected Framework to a workload.

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 and optimize GitHub Actions CI/CD workflows. Use when designing workflows, planning multi-environment deployments, optimizing pipeline cost and runtime, or implementing blue- green, canary, or rolling deployment strategies.

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。

Test and evaluation harness for AI agents — scenario suites, deterministic replay, regression diffing, cost and latency budgets. Use when agent quality is vibe-checked, before shipping a prompt or model change, or when evals drift.

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。

Form optimization for lead capture, contact, demo request, application, and checkout forms. Covers field-cost analysis, multi-step form design, validation UX, mobile optimization, and A/B testing 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。

Playbook for designing SaaS and startup products that convert, retain, and monetize — landing pages & CRO, checkout & forms, onboarding/activation, churn reduction, pricing psychology, dashboards, behavioral-science tactics, feature discipline, positioning/ICP, go-to-market, and AI-era differentiation. Use when advising on product design, UX, conversion, landing pages, checkout, onboarding, trials, pricing, retention/churn, growth, or positioning for a startup or SaaS. Distilled from product designer Richard (@richardrx). Not for use on gambling, betting, or casino products.

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 AWS solution architecture for startups focusing on serverless, scalable, and cost-effective cloud infrastructure with modern DevOps practices and infrastructure-as-code

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 RL reward signals using the OpenJudge framework. Covers choosing between pointwise and pairwise reward strategies based on RL algorithm, task type, and cost; aggregating multi-dimensional pointwise scores into a scalar reward; pairwise tournament reward for GRPO on subjective tasks (net win rate across group rollouts); generating preference pairs for DPO/RLAIF; and normalizing scores for training stability. Use when building reward models, scoring rollouts for GRPO/REINFORCE, generating preference data for DPO, or doing Best-of-N selection.

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。