144 件 ・ 関連度順
概要と使いどころ
Produce a scored heuristic critique of the UI using Nielsen's 10 + 6 design laws + optional persona walkthroughs. Outputs a machine-parseable scorecard plus a 0-100 UsabilityScore (the judged companion to the deterministic UICraftScore). Invoke when the user asks for heuristic on their UI, or mentions 'heuristic' alongside design / UI / frontend work.
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。
State-first design pass — inventories and implements all non-happy states (loading, empty, error, partial, conflict, offline) before the happy path, and refactors impossible boolean state to proper state machines. Use when starting a new screen, reviewing an existing one for edge-case gaps, or when the user says "handle the error state" / "add loading states" / "what happens when data is missing". Invoke when the user asks for unhappy on their UI, or mentions 'unhappy' alongside design / UI / frontend work.
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 style of Daphne Koller (machine learning pioneer, co-founder of Coursera, founder and CEO of Insitro). Use this skill whenever you encounter problems involving AI and machine learning in biology, drug discovery, interdisciplinary collaboration, data generation vs. data mining, or transitioning from academia to industry. Trigger this skill when advising on career trade-offs, building cross-functional teams (especially bridging engineers and domain experts), designing data pipelines, evaluating causality vs. correlation, or applying AI to physical systems ('where bits meet atoms'). Channel her focus on fit-for-purpose data, pragmatism, and disproportionate 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。
Applies the reasoning of Pieter Abbeel, robotics and reinforcement learning expert, UC Berkeley professor, and co-founder of Covariant. Use this skill whenever you are designing AI systems, tackling Sim2Real transfer, deploying machine learning in the physical world, or evaluating reinforcement learning architectures. Trigger this skill for questions about domain randomization, reward design, bootstrapping real-world AI, robotics hardware assumptions, or shifting from hard-coded rules to data-driven deep learning. It helps ground theoretical AI in physical embodiment and pragmatic deployment.
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。
Applies the reasoning of Pieter Abbeel, robotics and reinforcement learning expert, UC Berkeley professor, and co-founder of Covariant. Use this skill whenever you are designing AI systems, tackling Sim2Real transfer, deploying machine learning in the physical world, or evaluating reinforcement learning architectures. Trigger this skill for questions about domain randomization, reward design, bootstrapping real-world AI, robotics hardware assumptions, or shifting from hard-coded rules to data-driven deep learning. It helps ground theoretical AI in physical embodiment and pragmatic deployment.
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 analytical frameworks of Ronald C. Kessler, psychiatric epidemiologist at Harvard University, to problems in precision psychiatry, clinical trial design, and healthcare machine learning. Use this skill whenever you are designing clinical trials, evaluating mental health interventions, analyzing epidemiological survey data, or building clinical decision support algorithms. It is highly relevant when the user asks about sample sizes, treatment matching, patient attrition, suicide prevention, or predictive modeling in healthcare. Trigger this skill to prioritize massive observational data, tiered predictive models, and human-in-the-loop AI over underpowered randomized trials and expensive, unscalable biomarkers.
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 style of Daphne Koller (machine learning pioneer, co-founder of Coursera, founder and CEO of Insitro). Use this skill whenever you encounter problems involving AI and machine learning in biology, drug discovery, interdisciplinary collaboration, data generation vs. data mining, or transitioning from academia to industry. Trigger this skill when advising on career trade-offs, building cross-functional teams (especially bridging engineers and domain experts), designing data pipelines, evaluating causality vs. correlation, or applying AI to physical systems ('where bits meet atoms'). Channel her focus on fit-for-purpose data, pragmatism, and disproportionate 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。
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。
Expert guide for designing Machine-to-Machine (M2M) micro-economies, autonomous agent wallets, and swarm budget allocation / Panduan ahli merancang ekonomi mikro antar-agen (M2M), dompet agen otonom, dan alokasi anggaran swarm.
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。
Build sequential (stateful) logic circuits including latches, flip-flops, registers, counters, and finite state machines. Covers SR latch, D and JK flip-flops, binary/BCD/ring counters, and Mealy/Moore FSM design with clock signal and timing analysis. Use when a circuit must remember past inputs, count events, or implement a state-dependent control sequence.
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。
把一个目标/主题/文章/截图/数据转成有观点、有节奏、有视觉约束的单文件 HTML 演示（网页 PPT、科技战报、产品拆解、行业观察、路演/发布会 deck、社媒可传播长图）。**7 套各锚定公认设计体系的视觉人格**——技术崇高派 Technical Sublime（工程冷光·技术战报/算力）/ 构成主义 Constructivism（宣言/号召）/ 信息·数据设计（Tufte·数据/图表）/ 判断震慑极简 Luxury（高端品牌/判断）/ De Stijl 新造型（标准/秩序）/ 编辑主义 Editorial（衬线·深度观察）/ 演示禅 Zen（暗墨·思想/品牌 keynote）——用户可指定，否则按内容自动判选并透明告知。token + validator 机检（P0/P1/P2）+ 32 维审计**机器强制设计一致性**；反伪造（不造数据/logo/截图/来源·示意标注）；字体本地子集；图按角色路由（默认 CSS/SVG·要图先探测生图环境）。Generates single-file HTML decks with 7 design-system-anchored visual personas, a semantic layout grammar, locked design tokens, evidence-grading, machine validation (P0/P1/P2 + 32-dimension audit), an anti-fabrication discipline, and image-generation environment detection. Use when the user wants a presentation / deck / slides / 网页 PPT / 战报 / 产品拆解 / 行业观察 / 路演 / 发布会 / 社媒长图, or asks to turn an article / screenshot / dataset into slides. Do NOT use for large data-table training material, strict legal/medical/financial compliance documents, multi-author collaborative PPT editing, plain-text summaries, requests to replicate a brand's official keynote visuals, or any request to fabricate realistic-looking data / fake logos / fake screenshots / fake sources.
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。
Generate KiCad schematics from a Circuit Weaver design.yaml or a project's own checked-in generator. Use for large, repetitive, or machine-synchronized designs. Prefer the Circuit Weaver CLI unless the project actually contains and documents its own generation scripts.
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, implement, and review XState v5 state machines and @xstate/react integrations in TypeScript. Use when modeling finite workflows, async actors, statecharts, guarded transitions, delayed transitions, actor systems, or machine-driven React behavior.
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。
Makes sessions durable across `/clear`, machine reboots, and IDE switches. SessionStart catchup pulls scratch + last MemPalace recall + planning files into context. Stop promotion writes a handoff note and runs memory-keeper. Manual `/mddesign:handoff` re-runs either at any time.
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 leakage-safe machine learning experiments that mirror real deployment and support fair model comparisons. Use when defining prediction timing, feature eligibility, train-validation-test splits, baselines, metrics, or controlled model iterations; not for auditing whether raw labels are trustworthy.
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。
Guides type-driven design in Rust. Use when encoding invariants in types, applying newtype pattern, implementing type state machines, using PhantomData, or making invalid states unrepresentable.
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。
機械学習・AIの研究論文を、関連文献の調査から実験設計、結果分析、執筆、投稿準備まで支援します。引用の検証や査読への対応も扱います。
A gamer's journey building a retro-arcade collection — the obsession, the hunt, and what the machines came to mean. Built as a decision-grade story deck for friends, hobby community.
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。
Mandatory preflight for web frontend visual design. If available, call read_skill for bundled:taste before the first frontend create or edit tool call whenever creating or visually styling a human-viewed web page, component, app, dashboard, or interactive report. Do not load it for non-visual frontend logic or machine-only HTML. If unavailable, continue without warning. Negative constraints only; user requests and other explicit styles still win.
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。
Mistake-proof code so misuse cannot be expressed, rather than warning against it. Use when designing an interface, schema, or state machine and the user wants it hard to get wrong ("make invalid states unrepresentable", "so callers cannot screw it up", "type-safe API", "pit of success"); when auditing existing code for footguns ("what could bite us here", "what is easy to misuse", "poka-yoke this repo", "review this diff for ways to get it wrong"); or when a bug has recurred and the fix must close the class rather than the case ("make sure this never happens again", "this is the third time"). Especially for money, auth, permissions, deletion, migrations, and pipelines where failure is silent. Classifies every finding by what happens when the mistake occurs and how the device notices, which is what keeps it from collapsing into generic code review.
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 should be used to run a sandboxed deep verification of a qwen-code PR — "/verify-pr <n>", "深度验证这个 PR", A/B load-bearing proof against the base build, mock-free harnesses with wire oracles, and targeted gates — producing tmp/pr<n>-verify-<ts>/report.md plus a machine-readable verdict. Designed for the token-free CI verify job; also usable locally.
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。
Use when coordinating product work across the 12 bundled product sub-skills (RICE, OKRs, UX research, design tokens, competitive teardown, analytics, experiments, discovery, roadmaps, spec-to-repo, landing pages, SaaS scaffolding) or the 4 standalone product-team plugins (user stories, Apple HIG, code-to-PRD, research summarizer). Triggers on 'help me prioritize', 'plan a product experiment', 'we ship features nobody uses', 'run the discovery loop', 'is our OST sound'. Forks context to route to one sub-skill via a deterministic signal router and returns a digest; can also drive a continuous-discovery loop (Torres cadence tracker + OST linter as machine gates) or a full goal→plan→execute→verify→close run through the repo-wide agent-harness. Distinct from project-management (how to deliver vs what to build), marketing/landing (from-scratch pages), and engineering/agent-harness (the generic loop engine this orchestrator plugs into).
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。
Use when building game systems, implementing Unity/Unreal Engine features, or optimizing game performance. Invoke to implement ECS architecture, configure physics systems and colliders, set up multiplayer networking with lag compensation, optimize frame rates to 60+ FPS targets, develop shaders, or apply game design patterns such as object pooling and state machines. Trigger keywords: Unity, Unreal Engine, game development, ECS architecture, game physics, multiplayer networking, game optimization, shader programming, game AI.
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。