279 件 ・ 関連度順
概要と使いどころ
Devin Akin 视角 — CWNP 联合创始人 / CWNE 流派奠基 / Wi-Fi Vitae 播客主持 / Divergent Dynamics 创办人. 加载后让 AI 用「RF 物理是地基, vendor 是装修」/「教育是 bottleneck 不是技术」/「AIOps 多数是营销层叠加」三大镜片审视企业 Wi-Fi 决策, 特别是 vendor 选型 / 培训路径 / 厂商话术辨别 / AIOps 评估场景. 反 marketing 倾向 + 25 年 instructor 视角 + vendor-neutral 立场. 触发词: 「Akin 怎么看」「这个 vendor 培训值不值」「vendor neutral 路径」「这个 AIOps 营销真的吗」「我团队该学什么」「先 CWNA 还是先 UEWA」「Mist Marvis 真的有用吗」「我们要不要上 AI WLAN」.
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。
Applies the reasoning, materials design principles, and research philosophy of Zhenan Bao (chemical engineer, Stanford University, flexible electronics). Use this skill whenever you are advising on hardware innovation, materials science, wearable/implantable technology, bioelectronics, or deep-tech research strategy. Trigger this when users face engineering bottlenecks requiring biological inspiration, trade-offs between mechanical and electronic performance, scaling lab fabrication to commercial manufacturing, or managing long-term academic research and mentorship. Apply her frameworks to shift from reactive problem-solving to proactive, integrated design.
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。
Applies the reasoning of Zhong Lin Wang (nanotechnology pioneer, Georgia Tech) to problems involving energy harvesting, IoT power scaling, sensor networks, and fundamental physics applications. Reach for this skill whenever the user is discussing self-powered systems, scaling distributed hardware, overcoming battery bottlenecks, or translating fundamental scientific phenomena (like static electricity or mechanical strain) into novel engineering applications. It is highly relevant for hardware roadmapping, optoelectronics, piezotronics, and challenging established scientific assumptions (like classical Maxwell's equations) to model dynamic 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。
This skill applies the first-principles engineering and capital allocation frameworks of Elon Musk (CEO of Tesla and SpaceX, founder of X.com / PayPal). Reach for this skill whenever the user is facing complex manufacturing bottlenecks, evaluating existential risks, debating free speech vs. censorship, dealing with bureaucratic overregulation, or trying to accelerate ambitious technical timelines. It is highly effective for situations involving hardware production, AI symbiosis, radical innovation, and aggressive execution. Use this skill proactively when discussing multi-planetary expansion, government efficiency, or scaling prototypes to mass production, even if the user doesn't explicitly name him.
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。
Applies the engineering and research philosophies of Jeff Dean, Chief Scientist at Google DeepMind and Google Research. Reach for this skill whenever you are designing large-scale distributed systems, optimizing latency and energy efficiency, or making architectural decisions about machine learning infrastructure. It should trigger automatically for topics involving hardware-ML co-design, model distillation, sparse activation, massively multi-task models, or scaling systems by 5x to 10x. Use this skill to evaluate system bottlenecks, transition from specialized to unified models, and optimize experimental velocity. Apply his mental models to avoid premature 100x scaling and to treat AI models as reasoning engines rather than memorization databases.
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。
Guidance for scaling systems from startup to enterprise scale. Use when planning for growth, diagnosing bottlenecks, or designing systems that need to handle 10x-1000x current 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。
Diagnoses and improves measured latency, throughput, memory, CPU, bundle, rendering, query, or resource performance by defining a target, profiling a representative workload, locating the bottleneck, making one change at a time, and comparing before/after evidence. Use for performance work or regressions. Not for guessing optimizations without a reproducible workload or for generic refactoring.
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。
Theory of Constraints primitives for focusing steps, drum-buffer-rope, throughput accounting, critical chain, and policy constraints. Use when sequencing by bottleneck.
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 @aleabitoreddit ("Serenity")'s distilled stock-analysis method to ANY ticker, sector, or situation — critical-chokepoint / supply-chain-OSINT idea discovery, first-principles value-chain decomposition, a Buffett-style quality gate (moat / profitability / customer-replacement risk, all default unverified), and narrative-vs-fundamentals hygiene. Produces a beginner-friendly Chinese analysis (她的观点 / 小白解释 / 第一性原理 / Buffett 直接判断 / 当前结论) that classifies an idea as 研究地图 vs 可投资结论. Trigger on "analyze like Serenity / 用 aleabito 的方法分析 / 用 Serenity 框架 / critical chokepoint 分析 / 第一性原理 + Buffett 判断这只股 / supply-chain bottleneck thesis". Never emits buy/sell calls.
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。
Advising founders YC-style — socratic probing to surface the #1 bottleneck, anti-pattern detection, and 1-2 week action items. Not for brainstorming (Riff) or long-term scenarios (Helm).
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 application performance across frontend, backend, queries, and databases. Use when performance requirements exist, when you suspect performance regressions, when Core Web Vitals or load times need improvement, when N+1 query patterns need fixing, or when profiling reveals bottlenecks.
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。
Systematic backlink building for indie founders. Covers 5 channel types ranked by GEO + SEO ROI: Wikipedia (entities), authoritative media (PR), industry reviews (G2/Capterra/Product Hunt), Reddit/Quora discussions, and HARO/Featured.com expert quotes. Designed for 0→1 sites where domain authority is the bottleneck. Aligns with 2026 Google/Bing GEO guidance: backlinks are the strongest brand-authority signal AI search engines use to decide citations. Defaults to dev/B2B link sources; for 2C products (education, consumer apps) authority links differ — see gingiris-seo-geo/references/2c-adaptation.md.
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。
Reviews Remix v2 code for caching header misuse, missing server/client split, hydration mismatches (Date, Math.random, locale), prefetch hygiene, and asset bottlenecks. Use when reviewing routes that export headers, use .server.ts/.client.ts, or render dates/IDs in a Remix v2 codebase.
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。
Reviews Elixir code for performance issues including GenServer bottlenecks, memory usage, and concurrency patterns. Use when reviewing high-throughput code or investigating performance 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。
Operations reconnaissance — audit process documentation, vendor contracts, compliance posture, OKR health, and cross-functional friction points to understand where operations is the bottleneck. Use when asked to "audit our operations", "where are we slow", "what processes are broken", or "before designing a compliance program".
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。
Carbon, nitrogen, phosphorus, sulfur, and water cycles — pools, fluxes, residence times, and anthropogenic perturbations. Covers the fast and slow carbon cycles, the Haber-Bosch disruption of the nitrogen cycle, the phosphorus bottleneck, the hydrological cycle, ocean acidification, and the planetary boundaries framework. Use when tracking chemical elements through atmosphere, hydrosphere, biosphere, and lithosphere; computing pool sizes and fluxes; or evaluating anthropogenic perturbations to global cycles.
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 and lean production techniques for turning inputs into outputs under variability. Covers the Toyota Production System, seven wastes, just-in-time, kanban, jidoka, single-minute die exchange, 5 Whys, value stream mapping, bottleneck theory, and the distinction between mass production and flexible production. Use when diagnosing operational waste, designing production flows, setting up quality systems, or comparing batch-and-queue to one-piece flow.
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。
Investigate performance bottlenecks and validate optimizations without weakening retrieval quality or correctness.
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。
Multi-step reasoning engine for complex analysis and systematic problem solving. Use when: (1) Complex debugging scenarios with multiple layers, (2) Architectural analysis and system design, (3) Problems requiring hypothesis testing and validation, (4) Multi-component failure investigation, (5) Performance bottleneck identification. Triggers: "--think", "--think-hard", "--ultrathink", "analyze step by step", "break down this problem", "systematic analysis". IMPORTANT: Do NOT use for simple single-step tasks.
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。
Скилл IWE — см. тело файла
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。
System bottleneck identification, resource optimization, and performance analysis
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 Bazel build dependency graphs to identify bottlenecks and optimize build times. Uses the Bazel Query Language (BQL), Action Graph API, and Build Event Protocol (BEP) for deep build analysis.
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。
CTO-level architectural advisor for AI-native development. Use this skill whenever you encounter code design decisions, architecture discussions, system resilience questions, or any work touching: "architecture", "design", "scale", "dependencies", "state", "failure", "blast radius", "refactor", "migrate", "optimize", "resilience", "consistency", "observability", "bottleneck", "coupling", "monolith", "microservices", "distributed", "concurrency", "data flow", "system design", or any prompt suggesting code-first thinking when design-first thinking is needed. This skill integrates with Claude Code to review generated code for architectural soundness, define design systems via design.md, and guide teams toward CTO-level thinking. Trigger aggressively on architectural questions—this is where AI adds the most leverage.
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。