Results for “optimize”

生のプロンプトを分析し、意図とギャップを特定し、ECCコンポーネント （スキル/コマンド/エージェント/フック）にマッチングし、すぐに貼り付けて 使える最適化済みプロンプトを出力します。アドバイザリー専用 — タスク自体は 実行しません。 トリガー条件: ユーザーが「optimize prompt」「improve my prompt」 「how to write a prompt for」「help me prompt」「rewrite this prompt」 と言った場合、またはプロンプト品質の向上を明示的に依頼した場合。中国語の 同等表現でもトリガーされます: 「优化prompt」「改进prompt」「怎么写prompt」 「帮我优化这个指令」。 トリガーしない条件: ユーザーがタスクの直接実行を求めている場合、または 「just do it」/「直接做」と言った場合。「优化代码」「优化性能」 「optimize performance」「optimize this code」と言った場合もトリガーしません — これらはリファクタリング/パフォーマンスタスクであり、プロンプト最適化 ではありません。

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Apply staged skill improvements selected by the user after a fresh review of the actual changes. It backs up target files, applies passing patches, and records review evidence and change history for later inspection or reversal.

• Apply selected patches after a meta-optimize run
• Review staged changes immediately before applying them
• Keep backups for reverting skill changes

Optimize existing blog posts and web content for GEO (Generative Engine Optimization) — AI search visibility on ChatGPT, Perplexity, Claude, and Google AI Overviews. Use whenever a user provides a blog URL or pastes existing content and asks to optimize it for AI search, rewrite for LLM visibility, improve GEO score, make content answer-first, add FAQ sections, restructure headings for AI retrieval, improve citation potential, or audit content for AI readiness. Also triggers on "GEO optimize this", "make this AI-search ready", "rewrite for Perplexity", "optimize for ChatGPT", "optimize for Google AI Overviews", "improve AI visibility", "reformat for AI search", "make this content citation-worthy", "content isn't showing up in AI search", "why isn't my blog cited by AI", "GEO audit", "answer-first rewrite", "restructure for AI", "make my blog AI-friendly", "improve LLM citability". Do NOT trigger for writing new content from scratch — use geo-content for that.

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Improve code, prompts, skills, or agent definitions through repeated changes and evaluation. Measure numerical targets such as latency or size, or use an LLM to judge pass-or-fail criteria, keeping changes that improve the result.

• Improving page load performance
• Reducing build output size
• Refining prompts against evaluation criteria

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.

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Use when modifying or reviewing the Qwik optimizer under packages/optimizer, Rust transform code, WASM/NAPI bindings, optimizer snapshots, or optimizer-facing runtime behavior.

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既存のパイプライン、個別プロジェクトフォルダ、ドキュメント構造、またはソフトウェアスタックを改善、改修、再構築するための構造化された6ステップのプロシージャ。「pipeline optimizer」（ソフトウェア、研究、ゲーム開発パイプラインなど、トピック全体のパイプライン対象）または「project-folder optimizer」（単一のソフトウェアツールや論文プロジェクトなど、パイプライン内の個別プロジェクトフォルダ対象）として呼び出し可能。「パイプラインXを改善する」、「スタックを最適化する」、「Yを再構築する」、「改修」、「パイプラインのリファクタリング」、「プロジェクトフォルダのクリーンアップ」、「フォルダ構造の改善」、「規約の統一」、「ドキュメントの統合」、「既存システムへの統合」、または確立された構造に対する実質的な介入タスクで起動。既存資産の調査、目的の明確化、理想像のスケッチ、ギャップ計画、経験的課題の特定、および新しいサブエージェントによる再テストを提供。並行標準の発生、重複、パイプラインの破綻を防止。

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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.

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Restructure repeated LLM API requests so shared instructions and reference material can be cached, meaning reused across calls. Track cached input, write and storage costs, and response times to assess whether the changes actually save money.

• Reordering shared instructions in repeated API requests
• Estimating cache read, write, and storage costs
• Measuring cache hit rates and response times

Turn a draft request into a ready-to-paste prompt by clarifying intent, missing context, and completion criteria. Recommend suitable ECC commands, skills, and agents, then provide full and compact versions with explanations for the changes.

• Make a vague development request more specific
• Add completion criteria and checks to a prompt
• Choose ECC commands and skills for a task

Analyze application and system performance to find bottlenecks, the points that slow processing. Use resource usage and performance data to plan improvements to resource allocation, load balancing, caching, and algorithms, then compare results after changes.

• Investigating slow application processing
• Reviewing CPU and memory allocation
• Improving workload distribution across nodes

Audit how a brand appears in AI search answers and identify improvements to its content and website access. Review citation patterns, clear answer structure, supporting evidence, and whether AI systems can retrieve key information.

• Check brand citations across AI search services
• Improve existing articles for clearer answers
• Investigate why AI systems cannot read a site

Build, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and resources end to end. USE FOR: foundry, azd ai agent, azd provision/deploy, hosted agent scaffold/develop/run/deploy/troubleshoot, prompt agent create, managed harness agent create, GitHub Copilot harness agent, create agent, update agent, add tool to agent, invoke agent, agent insights, evaluate agent, batch eval, continuous eval, continuous monitoring, agent CI/CD, optimize prompt, improve prompt, optimize agent instructions, Agent Optimizer scaffold, dataset curation from traces, deploy model, model fine-tuning (SFT/DPO/RFT), Foundry project, RBAC, role assignment, permissions, quota, capacity, region, deployment failure, AI Services, create Foundry resource, knowledge index, customize deployment, onboard, availability, training-data, grader, distillation, large file upload. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).

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Build, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and resources end to end. USE FOR: foundry, azd ai agent, azd provision/deploy, hosted agent scaffold/develop/run/deploy/troubleshoot, prompt agent create, managed harness agent create, GitHub Copilot harness agent, create agent, update agent, add tool to agent, invoke agent, agent insights, evaluate agent, batch eval, continuous eval, continuous monitoring, agent CI/CD, optimize prompt, improve prompt, optimize agent instructions, Agent Optimizer scaffold, dataset curation from traces, deploy model, model fine-tuning (SFT/DPO/RFT), Foundry project, RBAC, role assignment, permissions, quota, capacity, region, deployment failure, AI Services, create Foundry resource, knowledge index, customize deployment, onboard, availability, training-data, grader, distillation, large file upload. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).

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Optimizes Simulink models for Embedded Coder generated code. Use when asked to optimize or improve generated code, or reduce code metrics for a Simulink model. Targets: execution time, memory footprint (RAM, ROM, stack, data copies), code size, MISRA compliance, or any semantically similar generated-code metric. Works iteratively — measures baseline, suggests changes, applies, and re-measures to confirm improvement. Triggers can be prompts similar to: optimize generated code runtime, reduce runtime, shrink code size, improve code efficiency, reduce memory usage, speed up generated code, follow MISRA compliance and so on. CAUTION: Do NOT attempt to optimize Simulink models for generated code efficiency without following this skill — the iterative measurement, gating, and rollback workflow is essential for safe optimization.

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Optimize RF PCB dimensions for bandwidth, return loss, or area via patternsearch and surrogateopt with constraints. TRIGGER: user asks to optimize an RF PCB component for performance (bandwidth, return loss, insertion loss, area) or apply constraints to a design. Invoke BEFORE writing optimization code — RF PCB Toolbox has a built-in optimize() function that differs from generic fmincon/ga approaches. SKIP: designing a component from scratch without an optimization objective (use the specific matlab-design-pcb-* skill), EM analysis without optimization (use matlab-analyze-em), material/stackup setup only (use matlab-manage-pcb-material).

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When the user wants to optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers. Also use when the user mentions 'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization,' 'generative engine optimization,' 'LLM optimization,' 'AI Overviews,' 'optimize for ChatGPT,' 'optimize for Perplexity,' 'AI citations,' 'AI visibility,' 'zero-click search,' 'how do I show up in AI answers,' 'LLM mentions,' 'optimize for Claude/Gemini,' 'llms.txt,' 'OKF,' 'Open Knowledge Format,' 'knowledge bundle,' or 'agent-readable site.' Use this whenever someone wants their content to be cited or surfaced by AI assistants and AI search engines. For traditional technical and on-page SEO audits, see seo-audit. For structured data implementation, see schema.

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Optimize DSPy programs with dspy.GEPA — a reflective/evolutionary optimizer to consider against task-specific baselines within an authorized evaluation budget. Use when the user says optimize, compile, GEPA, reflective optimization, or "make this program better" and a DSPy program + metric + trainset exist.

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When the user wants to optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers. Also use when the user mentions 'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization,' 'generative engine optimization,' 'LLM optimization,' 'AI Overviews,' 'optimize for ChatGPT,' 'optimize for Perplexity,' 'AI citations,' 'AI visibility,' 'zero-click search,' 'how do I show up in AI answers,' 'LLM mentions,' 'optimize for Claude/Gemini,' 'llms.txt,' 'OKF,' 'Open Knowledge Format,' 'knowledge bundle,' or 'agent-readable site.' Use this whenever someone wants their content to be cited or surfaced by AI assistants and AI search engines. For traditional technical and on-page SEO audits, see seo-audit. For structured data implementation, see schema.

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AWS Cost Optimizer workflow skill. Use this skill when the user needs Comprehensive AWS cost analysis and optimization recommendations using AWS CLI and Cost Explorer and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Multi-Agent Optimization Toolkit workflow skill. Use this skill when the user needs Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Multi-Agent Optimization Toolkit workflow skill. Use this skill when the user needs Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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When the user wants to optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers. Also use when the user mentions 'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization,' 'generative engine optimization,' 'LLM optimization,' 'AI Overviews,' 'optimize for ChatGPT,' 'optimize for Perplexity,' 'AI citations,' 'AI visibility,' 'zero-click search,' 'how do I show up in AI answers,' 'LLM mentions,' or 'optimize for Claude/Gemini.' Use this whenever someone wants their content to be cited or surfaced by AI assistants and AI search engines. For traditional technical and on-page SEO audits, see seo-audit. For structured data implementation, see schema.

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Suede-affiliated AI search optimization discipline. Use when the user wants to optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers. Also use when the user mentions 'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization,' 'generative engine optimization,' 'LLM optimization,' 'AI Overviews,' 'optimize for ChatGPT,' 'optimize for Perplexity,' 'AI citations,' 'AI visibility,' 'zero-click search,' 'how do I show up in AI answers,' 'LLM mentions,' 'optimize for Claude/Gemini,' 'llms.txt,' 'OKF,' 'Open Knowledge Format,' 'knowledge bundle,' or 'agent-readable site.' The durable job: make content structurally extractable, citable, and agent-readable so AI systems surface and cite it. NOT FOR: traditional technical SEO audit (use suede-seo-audit) or launch-appeal grading (use suede-visibility-grader).

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