4,508 件 ・ 関連度順
概要と使いどころ
Hunter mindset, recon setup, and target scoring for Web3 bug bounty. Use at the START of any new protocol hunt - scoring targets, setting up environment, understanding architecture.
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 publication-quality AI illustrations for academic papers using Gemini image generation. Creates architecture diagrams, method illustrations with Codex-supervised iterative refinement loop. Use when user says "生成图表", "画架构图", "AI绘图", "paper illustration", "generate diagram", or needs visual figures for papers.
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。
Autonomous design space exploration loop for computer architecture and EDA. Runs a program, analyzes results, tunes parameters, and iterates until objective is met or timeout. Use when user says \"DSE\", \"design space exploration\", \"sweep parameters\", \"optimize\", \"find best config\", or wants iterative parameter tuning.
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 publication-quality AI illustrations for academic papers using Gemini image generation. Creates architecture diagrams, method illustrations with Claude-supervised iterative refinement loop. Use when user says "生成图表", "画架构图", "AI绘图", "paper illustration", "generate diagram", or needs visual figures for papers.
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。
Autonomous design space exploration loop for computer architecture and EDA. Runs a program, analyzes results, tunes parameters, and iterates until objective is met or timeout. Use when user says "DSE", "design space exploration", "sweep parameters", "optimize", "find best config", or wants iterative parameter tuning.
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。
Search and read an existing repository OpenWiki when requested or needed to resolve a concrete architecture or dependency uncertainty, or initialize and update one through the resumable page-job lifecycle.
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。
KubeSphere central controller Skill. Routes to specific Skills based on user requests: multi-cluster management (kubesphere-cluster-management), multi-tenant management (kubesphere-multi-tenant-management), extension management (kubesphere-extension-management). Also provides core architecture, API routing, and API utilities.
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。
Repository-grounded threat modeling that enumerates trust boundaries, assets, attacker capabilities, abuse paths, and mitigations, and writes a concise Markdown threat model. Trigger only when the user explicitly asks to threat model a codebase or path, enumerate threats/abuse paths, or perform AppSec threat modeling. Do not trigger for general architecture summaries, code review, or non-security design 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。
Create OpenShell RFC proposals in rfc/ from a design request. Use when the user asks to write, draft, start, create, or update an RFC, Request for Comments, architecture proposal, API proposal, process proposal, or cross-cutting design proposal that should follow the OpenShell RFC process and template.
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。
Guide for developing the OpenShell TUI — a ratatui-based terminal UI for the OpenShell platform. Covers architecture, navigation, data fetching, theming, UX conventions, and development workflow. Trigger keywords - term, TUI, terminal UI, ratatui, openshell-tui, tui development, tui feature, tui bug.
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 the Fable-derived 10-stage OBSERVE, OBSERVE, LISTEN, THINK, CONNECT, CONNECT, FEEL, ACCEPT, CREATE, GROW loop to consequential or ambiguous reasoning and decisions. Use for think this through, deep think, architecture review, postmortem, tradeoff analysis, or challenges to assumptions. This is a reasoning review, not a deterministic vault health check or repair workflow.
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。
Enforce this repository's React and TypeScript frontend module architecture standards. Use whenever creating, modifying, refactoring, or reviewing frontend code under `src/`, including feature modules, components, hooks, contexts, shared UI, API access, state, utilities, and frontend tests. Do not apply these rules to backend code under `server/` or non-frontend scaffolding.
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。
Enforce this repository's TypeScript backend module architecture standards. Use whenever creating, modifying, refactoring, or reviewing backend code under `server/`, including routes, services, modules, repositories, shared backend code, and backend tests. Do not apply these rules to frontend code or non-backend scaffolding.
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。
Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.
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。
Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.
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。
Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.
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。
Provides guidance for interpreting and manipulating neural network internals using nnsight with optional NDIF remote execution. Use when needing to run interpretability experiments on massive models (70B+) without local GPU resources, or when working with any PyTorch architecture.
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。
Evaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution. Use when needing scalable evaluation on local Docker, Slurm HPC, or cloud platforms. NVIDIA's enterprise-grade platform with container-first architecture for reproducible benchmarking.
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。
High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.
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。
Reverse-engineering specialist that extracts specifications from existing codebases. Use when working with legacy or undocumented systems, inherited projects, or old codebases with no documentation. Invoke to map code dependencies, generate API documentation from source, identify undocumented business logic, figure out what code does, or create architecture documentation from implementation. Trigger phrases: reverse engineer, old codebase, no docs, no documentation, figure out how this works, inherited project, legacy analysis, code archaeology, undocumented features.
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。
Generates test files, creates mocking strategies, analyzes code coverage, designs test architectures, and produces test plans and defect reports across functional, performance, and security testing disciplines. Use when writing unit tests, integration tests, or E2E tests; creating test strategies or automation frameworks; analyzing coverage gaps; performance testing with k6 or Artillery; security testing with OWASP methods; debugging flaky tests; or working on QA, regression, test automation, quality gates, shift-left testing, or test maintenance.
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。
Generates Spring Boot 3.x configurations, creates REST controllers, implements Spring Security 6 authentication flows, sets up Spring Data JPA repositories, and configures reactive WebFlux endpoints. Use when building Spring Boot 3.x applications, microservices, or reactive Java applications; invoke for Spring Data JPA, Spring Security 6, WebFlux, Spring Cloud integration, Java REST API design, or Microservices Java architecture.
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。
Builds iOS/macOS/watchOS/tvOS applications, implements SwiftUI views and state management, designs protocol-oriented architectures, handles async/await concurrency, implements actors for thread safety, and debugs Swift-specific issues. Use when building iOS/macOS applications with Swift 5.9+, SwiftUI, or async/await concurrency. Invoke for protocol-oriented programming, SwiftUI state management, actors, server-side Swift, UIKit integration, Combine, or Vapor.
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 .NET 8 applications with minimal APIs, clean architecture, or cloud-native microservices. Invoke for Entity Framework Core, CQRS with MediatR, JWT authentication, AOT compilation.
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。