「optimization」の検索結果

When the user wants help with pricing decisions, packaging, or monetization strategy. Use when the user mentions 'pricing,' 'pricing model,' 'pricing tiers,' 'freemium,' 'free trial,' 'packaging,' 'price increase,' 'value metric,' 'Van Westendorp,' 'willingness to pay,' 'monetization,' 'pricing strategy,' 'pricing experiments,' 'price anchoring,' 'enterprise pricing,' 'GTM pricing,' 'price setting,' 'value-based pricing,' 'revenue optimization,' or 'pricing psychology.' This skill covers pricing research, tier structure, packaging strategy, discount frameworks, upsell mechanics, and pricing experiments.

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。

Comprehensive marketing expertise combining campaign execution, strategy frameworks, psychological principles, and 140+ marketing tactics. Use when planning campaigns, developing content strategy, applying behavioral science, generating marketing ideas, or building go-to-market strategies. Covers email, social, SEO, paid ads, content, and psychology-driven 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。

ESPHome device configuration, firmware, and IoT product development. Covers ESP32, ESP32-S3, ESP32-C3, ESP32-C6, ESP32-H2, ESP32-P4, ESP8266, RP2040, RP2350, nRF52, LibreTiny, Shelly, Sonoff, Tuya, BLE proxy, Matter firmware, Thread, Zigbee, GPIO, sensor YAML, LVGL displays, LED strips, voice assistant hardware, device flashing, Arduino conversion, alarm_control_panel, lock, valve, media_player, microphone, speaker, audio DAC, event entities, datetime entities, Z-Wave proxy, MIPI DSI displays, and DLMS smart meters. Also covers designing new ESPHome-based products: hardware selection, component sourcing, PCB design (KiCad), enclosures, 3D printing, CE/FCC certification, BOM optimization, and manufacturing from prototype to production 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。

Master Godot 4 GDScript patterns including signals, scenes, state machines, and optimization. Use when building Godot games, implementing game systems, or learning GDScript best practices.

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。

Export Blender scenes to glTF/GLB (web/AR), FBX (game engines), OBJ (universal), USD (VFX pipelines), STL (3D printing). Includes Unreal collision hulls and Multires normal-map baking for low-poly export, per-format settings, embed/unpack textures, axis conversion, polygon optimization (Decimate), and target-platform validation. Use whenever the user asks to "export this", "save as glTF / FBX / OBJ / STL / USD", "package for Unity / Unreal / Three.js / web / AR / 3D print", or any output format conversion. Make sure to use this skill even if the user does not say "export" — also covers "package this for the web", "make it work in Unity", "send to Unreal", "save for 3D printing".

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。

Diagnoses slow services and regressions via profiling and tail-latency math. Use when diagnosing slow services, profiling hot paths, or judging whether an optimization is real.

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。

Audits SaaS, cloud, payment, and AI spend. Use when analyzing bills, right-sizing plans, sizing Savings Plans or CUDs, cutting payment-processing fees, or LLM self-host breakeven.

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。

Formulates LP, MILP, CP-SAT models; diagnoses infeasibility and gaps. Use when a MILP is infeasible, shifts need scheduling under capacity, or choosing a solver.

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。

Chooses DPO, GRPO/RLVR, reward models, and over-optimization controls. Use when adapting a pretrained or SFT model with preference or verifiable reward signals.

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。

Diagnoses and tunes SQL for OLTP workloads on PostgreSQL, MySQL, and SQL Server. Use when tuning queries, reading plans, indexing, or fixing lock contention.

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。

Structured performance profiling workflow. Identifies bottlenecks, measures against budgets, and generates optimization recommendations with priority rankings.

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。

Manage Docker containers, images, volumes, networks, and Compose stacks — lifecycle ops, debugging, cleanup, and Dockerfile 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。

Nexscope Ozon keyword reverse lookup: reverse-looks up Ozon (and Wildberries) search keywords by a list of product SKUs (up to 20), returning which search terms those products appear under (organic/ad search terms), with multi-dimensional filtering by search volume, growth, product count, seller count, competitor count, natural rank, ad rank, exposure, conversion, cart-add conversion, etc. Each keyword carries monthly search volume, growth, market space, competitor/seller counts, average price, cart-add conversion, top products, and organic/ad channel, rank, exposure, and conversion (dimension) market profiles. Use for Ozon keyword reverse lookup, listing keyword optimization, competitor traffic word mining, and ad keyword analysis. Trigger when the user mentions Ozon keyword reverse lookup, Ozon reverse keyword search, Ozon SKU keyword reverse, Ozon product traffic keywords, Ozon competitor ranking keywords, Ozon organic/ad keyword reverse lookup, Nexscope Ozon, Ozon keyword back search, Ozon reverse keyword lookup, Ozon SKU keyword reverse. Also trigger when the intent is to reverse-lookup Ozon search keywords by product SKU and view market profiles, even without explicitly mentioning Nexscope.

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-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score verification.

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 AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.

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。

Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive 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。

Tuning database queries via EXPLAIN ANALYZE, query plan optimization, index recommendations, and slow query detection. Not for schema/migrations (Schema) or non-DB performance (Bolt).

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。

Designing search engines and vector DBs for full-text, vector, and hybrid retrieval, including permission-aware retrieval for multi-tenant or per-role corpora. Use for search design, index optimization, the RAG retrieval layer, or deciding where ACL filtering belongs in the query path.

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。

Optimizing SEO (meta/OGP/JSON-LD/headings), SMO (social sharing), CRO (CTA/form/exit-intent), and GEO (AI citation optimization). Use for search ranking, conversion, or AI visibility.

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。

Designing and evaluating AI/ML systems: prompt engineering, RAG design, LLM application patterns, AI safety, evaluation frameworks, MLOps, cost optimization. Use for AI pipelines or eval harnesses.

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。

シミュレーションと数理最適化（モンテカルロ / Monte Carlo / 離散事象シミュレーション / DES / 待ち行列 / リスク分析 / シナリオ分析、線形計画 / LP / 整数計画 / MIP / スケジューリング / 配送計画 / VRP / 割当 / メタヒューリスティクス）を 実行したら必ずセットで出す図と値のルーター。SeedSequence.spawn, scipy.stats の rvs, simpy, SALib, pulp, LpProblem, mip.Model, ortools, pywraplp, cp_model, pyomo, cvxpy, linprog, milp がコードに現れたとき、または ユーザーが「シミュレーションして」「モンテカルロで見積もって」「待ち時間をシミュレートして」「最適化して」 「最適な割当を求めて」「スケジュールを組んで」と言ったときに使う。収束・標準誤差・ソルバー status・制約違反・ 感度分析に言及がなくても適用する。SKILL.md のルーティング表で手法を特定し、対応する references/<手法>.md を 読んでから実行する。MCMC は statistical-inference-diagnostics、ML のハイパラ探索は predictive-modeling-diagnostics を使う。

Run metric-driven optimization loops. Use when improving measurable outcomes such as search relevance, clustering quality, build performance, prompt quality, or scored behavior through experiments.

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 add, fix, or optimize schema markup and structured data on their site. Also use when the user mentions "schema markup," "structured data," "JSON-LD," "rich snippets," "schema.org," "FAQ schema," "product schema," "review schema," "breadcrumb schema," "Google rich results," "knowledge panel," "star ratings in search," or "add structured data." Use this whenever someone wants their pages to show enhanced results in Google. For broader SEO issues, see seo-audit. For AI search optimization, see ai-seo.

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 optimize post-signup onboarding, user activation, first-run experience, or time-to-value. Also use when the user mentions "onboarding flow," "activation rate," "user activation," "first-run experience," "empty states," "onboarding checklist," "aha moment," "new user experience," "users aren't activating," "nobody completes setup," "low activation rate," "users sign up but don't use the product," "time to value," or "first session experience." Use this whenever users are signing up but not sticking around. For signup/registration optimization, see signup. For ongoing email sequences, see emails.

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。