「optimization」の検索結果

Build robust backend systems with modern technologies (Node.js, Python, Go, Rust), frameworks (NestJS, FastAPI, Django), databases (PostgreSQL, MongoDB, Redis), APIs (REST, GraphQL, gRPC), authentication (OAuth 2.1, JWT), testing strategies, security best practices (OWASP Top 10), performance optimization, scalability patterns (microservices, caching, sharding), DevOps practices (Docker, Kubernetes, CI/CD), and monitoring. Use when designing APIs, implementing authentication, optimizing database queries, setting up CI/CD pipelines, handling security vulnerabilities, building microservices, or developing production-ready backend systems.

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。

Assume the role of Galyarder CMO (Chief Marketing Officer). Growth champion. Lead conversion rate optimization (CRO), analytics architecture, growth engineering, and data-driven market positioning.

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。

Assume the role of Galyarder CFO-COO (Chief Financial and Operating Officer). Stability guardian. Enforce FinOps optimization, legal compliance, GDPR/privacy auditing, and risk parity.

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。

Conversion Rate Optimization specialist. Optimizes onboarding, paywalls, and signup flows.

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。

Creates demand generation campaigns, optimizes paid ad spend across LinkedIn, Google, and Meta, develops SEO strategies, and structures partnership programs for Series A+ startups scaling internationally. Use when planning marketing strategy, growth marketing, advertising campaigns, PPC optimization, lead generation, pipeline generation, or startup marketing budgets. Covers multi-channel acquisition (Google Ads, LinkedIn Ads, Meta Ads), CAC analysis, MQL/SQL workflows, attribution modeling, technical SEO, and co-marketing partnerships for hybrid PLG/Sales-Led motions in EU/US/Canada markets.

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。

Analyzes campaign performance with multi-touch attribution, funnel conversion analysis, and ROI calculation for marketing optimization. Use when analyzing marketing campaigns, ad performance, attribution models, conversion rates, or calculating marketing ROI, ROAS, CPA, and campaign metrics across channels.

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。

Shows you how to use Cowork's parallel workers to run multiple tasks at the same time instead of one after another — dramatically cutting the time on batch work. For processing large batches of files or research tasks, when running multiple independent analyses simultaneously, or during a performance optimization session. Use when the user says "speed up", "parallel workers", "batch task", "process multiple files", "why is cowork so slow", "faster", "do several things at once", or "can cowork multitask".

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 quantitative analyst specializing in financial modeling, algorithmic trading, and risk analytics. Masters statistical methods, derivatives pricing, and high-frequency trading with focus on mathematical rigor, performance optimization, and profitable strategy 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。

Expert Flutter specialist mastering Flutter 3+ with modern architecture patterns. Specializes in cross-platform development, custom animations, native integrations, and performance optimization with focus on creating beautiful, native-performance 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。

Provides guidance for automatically evolving and optimizing AI agents across any domain using LLM-driven evolution algorithms. Use when building self-improving agents, optimizing agent prompts and skills against benchmarks, or implementing automated agent evaluation loops.

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。

MATLAB R2026a data analysis workflow for tables, timetables, statistics, fitting, optimization-assisted analysis, visualization, report artifacts, and reproducible paper figures. Use whenever the user asks MATLAB to analyze data, plot results, fit models, process CSV/Excel/MAT files, or reproduce figures.

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。

Generative Engine Optimization plus agent readiness. Use this skill whenever the user asks about GEO, AEO, LLMO, optimizing content for AI search engines (ChatGPT, Perplexity, Google AI Overviews, Claude, Gemini, Copilot), getting cited by LLMs, brand mentions in AI responses, llms.txt, robots.txt for AI crawlers, Schema.org JSON-LD, FAST framework, agent readiness, MCP server discovery, Web Bot Auth, OAuth for agents, API Catalog, agentic commerce (x402, ACP, UCP), Markdown content negotiation, or auditing a site or page for AI visibility. Also trigger when the user says "AI SEO", asks how to "rank in ChatGPT", "show up in Perplexity", "appear in AI Overviews", or wants a site to "speak to AI agents". Trigger even when the request is implicit, such as "review this article so AI search picks it up" or "make our docs agent-friendly".

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 oversized agent instruction files using progressive disclosure. Handles CLAUDE.md (Claude Code), AGENTS.md (OpenAI Codex / GitHub Copilot agents), and copilot-instructions.md (GitHub Copilot). Auto-detects all three formats in your repo, analyzes content tiers, creates sub-documents using each format's native mechanism, and rewrites the main file with zero information loss. Triggers: optimize CLAUDE.md, AGENTS.md too long, reduce copilot-instructions.md, agent instructions file too large, shrink context file, apply progressive disclosure, instructions file bloated

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。

"Apply Sociotechnical Systems Theory to analyze and design work systems through joint optimization of social and technical subsystems. Use this skill when the user needs to diagnose why a technology implementation disrupted work practices, design IT-enabled work systems that balance human and technical needs, or when they ask 'why did this system hurt productivity despite being technically sound', 'how do we design work around new technology', or 'why are people resisting this technically superior system'.".

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。

"Habitual Domain (HD) theory by Taiwanese scholar P.L. Yu (游伯龍, 1977) — integrates four domains (potential/actual/reachable/activated), eight general hypotheses, seven-layer decision structure, and nine HD-expansion tools to analyze decision blind spots, break cognitive inertia, and support cross-domain integration. Use for diagnosing why alternatives are invisible, decision paralysis, breaking cognitive frames, or explaining perception gaps between people. Triggers: 『思維框架』『打破盲點』『創新思考』『決策盲區』『跨域整合』『游伯龍』『習慣領域』『HD 理論』『七層決策』『四領域』『八大通性』『P.L. Yu』. Especially for NYCU EMBA HD course, 政大科管所, 中山企管系 decision-analysis/innovation/leadership courses. Taiwan-origin theory cited in J. Optimization Theory, Theory & Decision, Human Systems Management.".

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 SQL query performance using EXPLAIN analysis, indexing strategies, and common anti-pattern fixes. Use this skill when the user needs to speed up slow queries, design indexes, fix N+1 problems, or optimize database performance — even if they say 'this query is slow', 'optimize our database', 'which indexes do we need', or 'our dashboard takes 30 seconds to load'.".

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 push notification and messaging strategies including channel selection, timing optimization, personalization, and fatigue management. Use this skill when the user needs to improve notification engagement, reduce opt-out rates, plan multi-channel messaging, or A/B test notification content — even if they say 'our push open rates are low', 'users are unsubscribing', 'when should we send notifications', or 'which channel to use for alerts'.".

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。

"Execute content SEO strategy from keyword research through content planning, writing, and on-page optimization. Use this skill when the user needs to create SEO-optimized content, perform keyword research, identify content gaps, or improve existing content rankings — even if they say 'content strategy', 'keyword research', or 'how to rank for this topic'.".

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 search rankings and user experience. Use this skill when the user needs to diagnose page speed issues, improve Largest Contentful Paint, reduce layout shift, or pass Google's page experience signals — even if they say 'my site is slow', 'Core Web Vitals failing', or 'page speed 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。

"Solve vehicle routing problems to optimize delivery routes under capacity and time constraints. Use this skill when the user needs to plan delivery routes, minimize transportation costs, or optimize fleet utilization — even if they say 'delivery route optimization', 'fleet routing', or 'minimize driving distance'.".

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 e-commerce search relevance across the full pipeline from query understanding to result presentation. Use this skill when the user needs to improve search quality, implement query processing features, or diagnose search relevance issues — even if they say 'search results are bad', 'improve product search', or 'search relevance 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。

"Optimize advertising budget allocation across campaigns using marginal returns analysis. Use this skill when the user needs to distribute budget across multiple campaigns, optimize spend pacing, or maximize overall ROAS under budget constraints — even if they say 'how to split my ad budget', 'campaign budget optimization', or 'diminishing returns on ad spend'.".

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。

Performs a lightweight pre-release readiness review of Atlassian Forge apps across manifest/module wiring, architecture, runtime compatibility, dependency posture, tests, deploy readiness, and obvious security, cost, or reliability smells. Use when the user asks "review my Forge app", "pre-deploy check", "is this app ready to ship", "review manifest", "general app review", "release readiness", or asks for a broad quality pass. Do not use for deep security audits/SAST/exploitability review, cost optimization, or diagnosing a known broken app; route those to forge-security-review, forge-cost-optimizer, or forge-debugger respectively.

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。

Optimizes Atlassian Forge apps to reduce platform consumption and avoid unnecessary costs using Atlassian's "Optimise Forge platform costs" guidance. Use when the user asks to optimize Forge app costs, reduce Forge invocations, lower GB-seconds, reduce storage or log usage, tune memory, replace polling, improve scheduled triggers, reduce KVS writes, move work to the frontend, use bridge APIs, batch API calls, add caching, or evaluate Forge Remote trade-offs. By default, perform an audit first and offer to make the recommended changes after presenting the audit. Only modify files immediately when the user explicitly asks the agent to implement or apply optimizations.

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。