「zat」の検索結果

Codified expertise for production scheduling, job sequencing, line balancing, changeover optimization, and bottleneck resolution in discrete and batch manufacturing. Informed by production schedulers with 15+ years experience. Includes TOC/drum-buffer-rope, SMED, OEE analysis, disruption response frameworks, and ERP/MES interaction patterns. Use when scheduling production, resolving bottlenecks, optimizing changeovers, responding to disruptions, or balancing manufacturing 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。

Codified expertise for returns authorization, receipt and inspection, disposition decisions, refund processing, fraud detection, and warranty claims management. Informed by returns operations managers with 15+ years experience. Includes grading frameworks, disposition economics, fraud pattern recognition, and vendor recovery processes. Use when handling product returns, reverse logistics, refund decisions, return fraud detection, or warranty claims.

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 raw prompts, identify intent and gaps, match ECC components (skills/commands/agents/hooks), and output a ready-to-paste optimized prompt. Advisory role only — never executes the task itself. TRIGGER when: user says "optimize prompt", "improve my prompt", "how to write a prompt for", "help me prompt", "rewrite this prompt", or explicitly asks to enhance prompt quality. Also triggers on Chinese equivalents: "优化prompt", "改进prompt", "怎么写prompt", "帮我优化这个指令". DO NOT TRIGGER when: user wants the task executed directly, or says "just do it" / "直接做". DO NOT TRIGGER when user says "优化代码", "优化性能", "optimize performance", "optimize this code" — those are refactoring/performance tasks, not prompt 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。

Codified expertise for demand forecasting, safety stock optimization, replenishment planning, and promotional lift estimation at multi-location retailers. Informed by demand planners with 15+ years experience managing hundreds of SKUs. Includes forecasting method selection, ABC/XYZ analysis, seasonal transition management, and vendor negotiation frameworks. Use when forecasting demand, setting safety stock, planning replenishment, managing promotions, or optimizing inventory levels.

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 testing strategies using pytest, TDD methodology, fixtures, mocking, parametrization, and coverage requirements.

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 architecture patterns, API design, database optimization, and server-side best practices for Node.js, Express, and Next.js API routes.

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。

Deployment workflows, CI/CD pipeline patterns, Docker containerization, health checks, rollback strategies, and production readiness checklists for web 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。

Cost optimization patterns for LLM API usage — model routing by task complexity, budget tracking, retry logic, and prompt caching.

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。

ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.

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。

Django security best practices, authentication, authorization, CSRF protection, SQL injection prevention, XSS prevention, and secure deployment configurations.

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。

Codified expertise for electricity and gas procurement, tariff optimization, demand charge management, renewable PPA evaluation, and multi-facility energy cost management. Informed by energy procurement managers with 15+ years experience at large commercial and industrial consumers. Includes market structure analysis, hedging strategies, load profiling, and sustainability reporting frameworks. Use when procuring energy, optimizing tariffs, managing demand charges, evaluating PPAs, or developing energy 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。

Trigger when: (1) User wants to create an educational/explainer video, (2) User has a vague concept they want visualized, (3) User mentions "3b1b style" or "explain like 3Blue1Brown", (4) User wants to plan a Manim video or animation sequence, (5) User asks to "compose" or "plan" a math/science visualization. Transforms vague video ideas into detailed scene-by-scene plans (scenes.md). Conducts research, asks clarifying questions about audience/scope/focus, and outputs comprehensive scene specifications ready for implementation with ManimCE or ManimGL. Use this BEFORE writing any Manim code. This skill plans the video; use manimce-best-practices or manimgl-best-practices for 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。

Optimize Core Web Vitals (LCP, INP, CLS) for better page experience and search ranking. Use when asked to "improve Core Web Vitals", "fix LCP", "reduce CLS", "optimize INP", "page experience optimization", or "fix layout shifts".

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。

Choosing the right concurrency abstraction in .NET - from async/await for I/O to Channels for producer/consumer to Akka.NET for stateful entity management. Avoid locks and manual synchronization unless absolutely necessary.

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。

AI-powered issue operations via gh-models. TRIGGERS - issue summarization, auto-labeling, issue 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。

Design, review, and improve agent workflows & agent using SSOT, SRP, Fail Fast principles. Supports Prompt Chaining, Parallelization, Orchestrator-Workers 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。

Guide creation of focused single-purpose agents following the One Agent One Prompt One Purpose principle. Use when designing new agents, refactoring general agents into specialists, or optimizing agent context for a single task.

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 advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search 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。

Expert C++ developer specializing in modern C++20/23, systems programming, and high-performance computing. Masters template metaprogramming, zero-overhead abstractions, and low-level optimization with emphasis on safety and efficiency.

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 LLM architect specializing in large language model architecture, deployment, and optimization. Masters LLM system design, fine-tuning strategies, and production serving with focus on building scalable, efficient, and safe 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。

Expert context manager specializing in information storage, retrieval, and synchronization across multi-agent systems. Masters state management, version control, and data lifecycle with focus on ensuring consistency, accessibility, and performance at scale.

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。

Document management, LLM pipeline, anonymization, Q&A generation, versioning

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。

Quantitative strategy frameworks: pairs trading/cointegration, volatility regime strategies, seasonality/calendar effects, multi-factor models (IC/IR), factor research and screening, correlation analysis, statistical methods (ADF/GARCH), strategy optimization, execution modeling, hedging, and ML-based prediction (sklearn). Also provides CLI access to run indicator scripts against K-line data. Triggers: "量化", "因子", "配对交易", "协整", "波动率策略", "季节性", "多因子", "IC", "机器学习", "对冲", "量化策略", "協整", "波動率策略", "季節性", "多因子", "對沖", "quant", "pairs trading", "cointegration", "volatility strategy", "seasonality", "multi-factor", "factor model", "IC IR", "machine learning", "hedging", "walk-forward", "配對交易", "機器學習", "因子選股"

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。

Update IWE with change explanations. Agent calls update.sh, parses CHANGELOG, explains what changed, helps adapt updates to user customizations.

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。