280 件 ・ 関連度順
概要と使いどころ
Plan, scaffold, implement, and integrate a complete new emulated computer or console in the EMULATORS repository. Use when Codex creates a new machine, platform, hardware model, or emulator executable; decides which existing CPUs, chips, memory, I/O, video, sound, ROM, and framework classes to reuse or extend; or coordinates several emulator subsystems into a bootable tested system.
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 and implement unit, conformance, integration, timing, boot, trace, video, audio, and regression tests for EMULATORS processors, chips, peripherals, and complete machines. Use when creating a verification strategy for a new emulator, converting hardware documentation into tests, adding diagnostic programs or golden artifacts, measuring behavioral coverage, or proving that components interact correctly.
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。
Detect bootkits such as BlackLotus and Bootkitty and Secure Boot bypass via DBX and binary checks.
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 Intel CHIPSEC to assess platform firmware configuration, SPI flash write protection, BIOS lock, SMM/SMRR, and Secure Boot variable state, dump SPI flash, and triage UEFI variables for firmware-level threats.
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。
Complete SolanaOS agent skill — install, configure, and operate the autonomous Solana trading runtime with Honcho v3 epistemological memory, multi-venue perp trading (Hyperliquid + Aster), on-chain intelligence with USD pricing, Telegram bot, gateway API, Tailscale mesh, hardware integration, and cross-session recall. Use when asked to install SolanaOS, query Solana blockchain data, manage wallets, run OODA trading loops, configure strategies, control BitAxe mining fleets, pair Seeker devices, or operate any SolanaOS runtime surface.
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 formal IT/hardware purchase request documents from informal requirements. Use when creating purchase justifications, cost-benefit analyses, ROI calculations, vendor comparisons, or MARP presentation slides for management approval.
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。
Judge performance or resource claims on real hardware: benchmark design or review, latency, throughput, memory or headroom numbers, and accept-or-reject decisions on optimizations. Not for refactors with no resource claim.
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 designing, reviewing, modifying, migrating, optimizing, securing, operating, troubleshooting, or decommissioning data architectures across data model exploration, SQL, ISO SQL conformance, NoSQL, vector, graph, document, time-series, NewSQL, search, in-memory, SQLite, CDC/Debezium, DBMS storage/query/recovery internals, database incident prevention, hardware acceleration, cache, big-data, lakehouse, and warehouse 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。
Use when developing, reviewing, debugging, porting, or engineering Linux kernel device drivers, kernel modules, device tree bindings, bus clients, subsystem drivers, DMA/IRQ/memory paths, userspace interfaces, or hardware bring-up plans.
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。
Documentation structure and maintenance rules for the Megahub project. Use whenever making user-facing changes — adding features, changing behavior, fixing hardware compatibility, or modifying configuration. Describes which files to update, the README structure, and writing style.
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。
FlutterSecureStorage (v10.x) for encrypted key-value storage of sensitive data using platform-native secure enclaves with biometric authentication support. Use this skill when storing OAuth tokens (access tokens, refresh tokens, ID tokens), API keys and secrets, user passwords or PINs, session tokens, encryption keys, private keys for cryptography, certificate data, biometric enrollment data, banking credentials, health data, or any sensitive information requiring platform-level encryption (iOS Keychain with Secure Enclave, Android Keystore with Hardware-backed keys). Supports Touch ID, Face ID, and Android biometric prompt integration. Handles biometric change invalidation (resetOnError), data migration from SharedPreferences/EncryptedSharedPreferences, Google Drive backup exclusion, automatic key rotation, configurable encryption algorithms (AES), and prevents data extraction even on rooted/jailbroken devices. Essential for authentication flows, secure credential management, or compliance requirements (PCI DSS, HIPAA, GDPR data protection).
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。
Maintain IT asset inventory and configuration management database. Track hardware, software, and cloud resources. Use when managing IT assets.
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 working with Device42 — device42 IT infrastructure management covering data center infrastructure management (DCIM), application dependency mapping, IP address management, and asset lifecycle tracking. Use when documenting data center rack layouts and power chains, mapping application dependencies and communication flows, managing IP address allocation and subnets, or tracking hardware assets from procurement through decommission.
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。
Topics on Secure Hardware, Fall 2018 Decentralized Technologies Decentralized Finance MOOC, Fall 202
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。
I-A LLMs Large Language Models (LLMs) are extensive, general-purpose models that can be pre-trained and adapted for specific 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。
Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
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, deploy, and maintain applications on Hugging Face Spaces — Gradio / Docker / Static SDKs, ZeroGPU and dedicated hardware, model loading, debugging, buckets, inference providers, community grants. Use whenever the user asks to create or host an app on Hugging Face, port code onto ZeroGPU, fix a Space that won't build or run, or otherwise work with `hf spaces …`, `@spaces.GPU`, Space README frontmatter, or the `spaces` Python package.
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。
Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.
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 and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA. Use when someone asks to create, generate, ship, or publish a Space, demo, Gradio app, or playground for a LoRA — including LoRAs for Qwen-Image, Qwen-Image-Edit, LTX-Video, Wan, FLUX, SDXL, or other diffusion base models. Also triggers when someone describes a LoRA they trained or hosts on the Hub and wants to share it. Covers picking the right base pipeline and `diffusers` inference recipe, designing a UI tailored to the LoRA's task and inputs (Union/multi-task control, edit, video, image, etc.), respecting model-card recommendations (trigger words, steps, guidance, LoRA scale, example inputs), and shipping to ZeroGPU hardware as a private Space by default.
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。
Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Use for backend selection, local GPU evals, and choosing between vLLM / Transformers / accelerate. Not for HF Jobs orchestration, model-card PRs, .eval_results publication, or community-evals automation.
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。
Migrate a .NET 10 project or solution to .NET 11 and resolve all breaking changes. This is a MIGRATION skill — use it when upgrading from .NET 10 to .NET 11, NOT for writing new programs. USE FOR: upgrading TargetFramework from net10.0 to net11.0, fixing build errors after updating the .NET 11 SDK, resolving source-breaking and behavioral changes in .NET 11 runtime, C# 15 compiler, and EF Core 11, adapting to updated minimum hardware requirements (x86-64-v2, Arm64 LSE), and updating CI/CD pipelines and Dockerfiles for .NET 11. DO NOT USE FOR: .NET Framework migrations, upgrading from .NET 9 or earlier, greenfield .NET 11 projects, or cosmetic modernization unrelated to the upgrade. NOTE: .NET 11 is in preview. Covers breaking changes through Preview 3.
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 for real-time performance and multimedia operator work: technical show-control execution (signal flows, OSC/DMX, MCP orchestration, hardware limits) for tools like grandMA3, Resolume, Unreal, Rhino, Vectorworks, and Adobe MCP.
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。
Access security tokens and smart cards using CryptoTokenKit. Use when building token driver extensions with TKTokenDriver and TKToken, communicating with smart cards via TKSmartCard, implementing certificate-based authentication, managing token sessions, or integrating hardware security tokens with the system keychain.
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, CSS hardware acceleration, and balanced design engineering.
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。