280 件 ・ 関連度順
概要と使いどころ
Generate and operate Tencent Hunyuan video through the managed TokenHub HY-Video-1.5 API or official local HunyuanVideo repositories. Use for text-to-video, image-to-video, hosted job lifecycle, pricing and region planning, local checkpoint selection, hardware and acceleration, prompt design, licensing, safety, provenance, and delivery QA.
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。
Select, run, and govern NVIDIA Cosmos world-video generation across Cosmos 3 Generator, Predict2.5, Transfer2.5, downloadable checkpoints, self-hosted NIMs, and hosted preview surfaces. Use for text/image/video-to-world, controlled world transfer, multiview or action-conditioned physical-AI video, local checkpoint pinning, hardware planning, inference approval, and output validation; do not use for Cosmos Reason/Embed-only 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。
Embedded hardware debugging workflow for probe-rs targets using embedded-debugger-mcp. Use when Codex or Claude Code needs to inspect debug probes, validate embedded debugger setup, start the MCP server, guide a user through ARM Cortex-M/RISC-V flashing/debugging/RTT workflows, or operate without installing an MCP client by using the CLI plus prompts.
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。
Trigger this skill when the user wants to train, fine-tune, or adapt Gemma models (e.g. SFT, DPO, RLHF, Reward Modeling) on local hardware. Covers TRL, Unsloth, dataset preparation, validation, and GGUF/LiteRT conversion.
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。
Patterns for embedded software development including real-time systems, memory management, hardware abstraction, interrupt handling, and debugging techniques for resource-constrained environments. Use when ", " mentioned.
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 Ollama configuration for the current machine's hardware. Use when asked to speed up Ollama, tune local LLM performance, or pick models that fit available GPU/RAM. Don't use for LM Studio, llama.cpp, vLLM, or hosted-API LLM providers.
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 product philosophy and leadership frameworks of Steve Jobs, co-founder and CEO of Apple Inc. Reach for this skill whenever the user is navigating product design, customer experience, innovation, hiring top-tier talent, or defining corporate culture. Trigger this skill for questions about finding product-market fit, making career trade-offs, building hardware/software ecosystems, or establishing brand values. Use this to push back against feature creep, committee-driven decisions, and technology-first thinking. Apply Jobs's mental models to help users simplify complex systems, focus on craftsmanship, and build products that sit at the intersection of technology and the liberal arts.
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, principles, and mental models of Sebastian Thrun (robotics and self-driving cars pioneer, founder of Google X, Waymo, Udacity, Stanford University). Reach for this skill whenever Claude is asked to advise on hardware/software systems engineering, autonomous vehicles, moonshot ideation, probabilistic robotics (SLAM), or leading high-stakes engineering teams. Trigger this skill for discussions on democratizing education, regulating AI, transitioning from academic research to product development, or managing technical teams with empathy. Use it to shift focus from incremental component debates to end-to-end execution and audacious goals.
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。
Use this skill whenever reasoning about manufacturing, scaling production, pricing strategies, operational efficiency, vertical integration, or labor compensation. Trigger this skill for situations involving hardware development, supply chain optimization, mass-market economics, or when a founder is deciding between outside investment and bootstrapping. Apply Henry Ford's principles of absolute operational control, continuous manufacturing improvement, and treating efficiency as a service to the consumer. Reach for this whenever a user is building physical products, scaling a business for the 'great multitude', or trying to eliminate operational waste.
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 Geoffrey Hinton, deep learning pioneer and 2018 Turing Award winner. Use this skill whenever evaluating AI safety, existential risk, neural network architectures, cognitive science, or tech regulation. Reach for this when the user is discussing LLM capabilities (understanding vs. autocomplete), the biological vs. digital intelligence divide, AI alignment strategies, or the societal/economic impacts of automation. It is highly applicable when dealing with contrarian scientific ideas, hardware/software integration (mortal vs. immortal computing), or global cooperation on technological threats. Do not wait for the user to name Hinton; trigger this skill proactively for any deep learning or AI existential risk 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。
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。
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。
Senior UI/UX Engineer. Architect digital interfaces overriding default LLM biases. Enforces metric-based rules, strict component architecture, hardware-accelerated CSS, and balanced design engineering. By Leon Lin & blueemi. Multi-runtime improved by shokunin.
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。
Windows 11 system administration with PowerShell — system info reporting, hardware monitoring, package management (winget/scoop), disk cleanup, performance optimization, environment configuration, PowerShell profile setup with aliases and autocomplete, and Task Scheduler automation. Use when user asks to check system info, install tools on Windows, clean up disk space, set up PowerShell profile, or automate Windows tasks. Do NOT use for Linux administration, cross-platform scripting, or network infrastructure 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。
Visual design audit for iOS apps on real hardware. (gstack)
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。
Bench instruments and measurement techniques for electronics — digital multimeter, oscilloscope, logic analyzer, spectrum analyzer, function generator, power supply, and their proper use. Covers probing technique, ground loops, loading effects, trigger modes, FFT mode pitfalls, signal integrity, noise budget analysis, and debugging workflows from "it doesn't work" to "here is the failure mechanism." Use when measuring, debugging, or characterizing any electronic circuit or firmware-hardware interaction.
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。
Embedded firmware development for microcontrollers including memory-mapped I/O, GPIO, interrupts, timers, UART/SPI/I2C serial protocols, PWM generation, ADC sampling, interrupt-driven versus polled architectures, RTOS fundamentals, and the bring-up sequence from power-on reset through first printf. Covers AVR/Arduino, ARM Cortex-M, and ESP32 patterns. Use when writing, porting, debugging, or reviewing firmware that directly manipulates hardware peripherals.
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。
Computational thinking as a problem-solving discipline independent of programming languages. Covers the four pillars (decomposition, pattern recognition, abstraction, algorithm design), computational problem-solving methodology (understand, decompose, generalize, formalize, verify), abstraction levels (from hardware through user interface), modeling and simulation, automata and formal languages (DFA, NFA, regular expressions, context-free grammars, Turing machines), computational complexity classes (P, NP, NP-complete, undecidable), and constructionist pedagogy (learning by building, Logo, Scratch, physical computing). Use when approaching unfamiliar problems, teaching problem-solving strategies, analyzing problem complexity, or bridging between domain knowledge and computational solutions.
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。
Understanding how digital systems actually work -- hardware, operating systems, networks, and information representation -- at a depth sufficient to reason about them rather than just use them. Covers CPU/RAM/storage/GPU roles, what an OS does, how packets travel the internet, and how computers represent numbers, text, images, and sound as binary. Use when a learner needs to build a mental model of computing rather than memorize menu paths.
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。
Systems programming from memory management through networking. Covers memory models (stack vs heap, manual allocation, garbage collection, ownership/borrowing), concurrency (threads, mutexes, channels, async/await, actor model, data races vs race conditions), operating system concepts (processes, virtual memory, page tables, system calls, file descriptors, signals), compilation (lexing, parsing, code generation, linking, static vs dynamic libraries), networking fundamentals (TCP/IP, sockets, HTTP, DNS, TLS), and the hardware-software boundary (caches, cache lines, false sharing, memory-mapped I/O). Use when working with low-level code, diagnosing system-level bugs, understanding performance characteristics, or bridging between high-level languages and machine 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。
Essential Zephyr RTOS kernel services. Covers thread lifecycle and priority, the logging subsystem for diagnostics, and the interactive shell for real-time hardware inspection and debugging. Trigger when writing application flows, adding logging to modules, or creating interactive CLI commands.
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。
Integration of advanced IoT protocols for Zephyr RTOS. Covers OpenThread mesh networking, Matter-over-Thread device development, Golioth Cloud SDK patterns, and LoRaWAN basics. Trigger when building smart home devices, wide-area sensor networks, or cloud-integrated hardware fleets.
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。