「machine」の検索結果

Answers "can CARLA do X here, and is there a vetted procedure for it" before you promise anything, and detects client/server/tree version skew — a support matrix over the whole collection (covered by a skill / works but undocumented / present but untested / broken or removed), an index of the features that are verified BROKEN on 0.10.0 (gbuffers crash the server, map layers do nothing, Landmark.waypoint is None, standalone asset packages are gone), and a probe that reports what this machine's build flags and running server actually offer. Use when the user asks whether CARLA supports something, when a feature has no skill, when a UE4 tutorial's steps do not exist, or before improvising a procedure.

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 NVIDIA Cosmos Policy on LIBERO and RoboCasa simulation environments. Use when setting up cosmos-policy for robot manipulation evaluation, running headless GPU evaluations with EGL rendering, or profiling inference latency on cluster or local GPU machines.

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。

Compiles any research input — PDF papers, GitHub repositories, experiment logs, code directories, or raw notes — into a complete Agent-Native Research Artifact (ARA) with cognitive layer (claims, concepts, heuristics), physical layer (configs, code stubs), exploration graph, and grounded evidence. Use when ingesting a paper or codebase into a structured, machine-executable knowledge package, building an ARA from scratch, or converting research outputs into a falsifiable, agent-traversable form.

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。

Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.

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。

Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.

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。

Use when the user asks about finding the best, top, or recommended model for a task, wants to know what AI model to use, or wants to compare models by benchmark scores. Triggers on: "best model for X", "what model should I use for", "top models for [task]", "which model runs on my laptop/machine/device", "recommend a model for", "what LLM should I use for", "compare models for", "what's state of the art for", or any question about choosing an AI model for a specific use case. Always use this skill when the user wants model recommendations or comparisons, even if they don't explicitly mention HuggingFace or benchmarks.

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 Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained models from Hugging Face Hub.

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。

Deep file-focused code review for correctness bugs. Unlike shallow-review which runs 6 specialists in parallel across the entire patch, deep-review focuses on user-specified files with full pattern catalogs (500+ patterns), codebase investigation, and source-level context gathering. Use when: the user specifies particular files for focused review, a shallow review flagged areas that need deeper investigation, reviewing critical-path code changes, examining complex serialization/lifecycle/state-machine changes. The user instructs which files to focus on (typically a subset of files in the patch).

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, run, and verify TLA+ and PlusCal formal specifications. Use for modeling distributed systems, protocols, concurrent algorithms, state machines. Can compose specs from code, find divergences between spec and implementation, spot concurrency bugs and invariant violations. Use when asked to "write a TLA+ spec", "model check", "verify protocol", "find race conditions", or "formal verification".

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 polished, validated architecture, workflow, sequence, data-flow, and lifecycle/state diagrams as explorable standalone HTML with inline SVG, dark/light themes, optional trace motion, and PNG/JPEG/WebP/SVG/WebM export. Accept plain-language requirements or pasted Mermaid flowchart, sequenceDiagram, and stateDiagram input; inspect repository evidence when the diagram must reflect real code. Use when the user asks to visualize system architecture, infrastructure, cloud/security/network topology, technical workflows, API call sequences, request lifecycles, data pipelines, ETL/ELT, data lineage, state machines, or to convert/beautify Mermaid.

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。

Renders every state of a component you choose on a throwaway page, with mock data and a switcher, so you can work on each state.

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。

Business logic vulnerability testing for web/mobile/API engagements. Covers workflow bypass, state machine violations, multi-step process abuse, price/quantity/discount manipulation, currency confusion, coupon stacking, refund/chargeback abuse, race conditions on logic boundaries, parameter tampering for hidden flows, role/tenant boundary violations, time-of-check vs use, anti-automation defeat, fraud-detection evasion, and subscription/quota abuse. Use when scoping an application after surface-level OWASP Top 10 has been covered, or when the asset is a transactional/marketplace/fintech/e-commerce/SaaS app where logic flaws produce direct financial impact.

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 prerequisite — load this skill BEFORE every `generate_diagram` tool call. NEVER call `generate_diagram` directly without loading this skill first. Trigger whenever the user asks to create, generate, draw, render, sketch, or build a diagram — flowchart, architecture diagram, sequence diagram, ERD or entity-relationship diagram, state diagram or state machine, gantt chart, or timeline. Also trigger when the user mentions Mermaid syntax or wants a system architecture, decision tree, dependency graph, API call flow, auth handshake, schema, or pipeline visualized in FigJam. Routes to type-specific guidance, sets universal Mermaid constraints, and tells you when to use a different diagram type or skip the tool entirely (mindmaps, pie charts, class diagrams, etc.).

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。

Choose and apply automatic layout strategies for node-link diagrams and connected-node visuals. Use when the user asks how to auto-arrange nodes, reduce line crossings, route edges, avoid overlaps, stabilize layout, or choose graph-layout algorithms for network diagrams, dependency graphs, database schema diagrams, ERDs, state machines, decision trees, flow diagrams, box-and-line editors, or other line-connected nodes.

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, critique, read, write, render, and implement UML and UML-like software diagrams. Use when the user mentions UML, sequence diagrams, class diagrams, activity diagrams, state machines, use case diagrams, component diagrams, deployment diagrams, object diagrams, package diagrams, profile diagrams, timing diagrams, communication diagrams, interaction overview diagrams, composite structure diagrams, ERDs, database schema diagrams, C4, BPMN, swimlanes, flowcharts, network diagrams, application architecture diagrams, software architecture diagrams, diagram-as-code, model-as-code, XMI, UMLDI, PlantUML, Mermaid, Graphviz DOT, D2, Structurizr, DBML, diagrams.net/draw.io, Kroki, or interactive diagram editors and explorers.

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。

Research a game, engine or modding technique on the open web with archive-aware, rate-limit-safe searches — Wayback Machine/archive.today for dead forums (XeNTaX, Zenhax, old threads), GitHub code/repo search, Nexus/Steam Workshop/Thunderstore APIs, Reddit JSON, YouTube transcripts, plus archive snapshots as evidence. Use when starting work on a game ("how did people mod X before?"), when a documented tool/link is dead, when forum threads are login-gated (semi-auto handoff to the human), and before writing a field note so claims carry source URLs.

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 an agent running inside OpenRig needs the right skill or context for fleet recovery, seat handover, new-seat orientation, a watchdog wake, cross-host reach to an agent on another machine, rig packaging, an OpenRig upgrade, systematic debugging, queue triage, or implementation planning; also when the agent doesn't know what applies or nothing is projected on cold boot. Installing, starting or updating OpenRig, getting back to a team or bringing a stopped one back from a person's own session is the rigs skill.

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 authoring or installing a rig bundle (packaged, shareable artifact that instantiates an opinionated OpenRig topology + workflow), reasoning about the bundle vs extension boundary, or auditing a bundle for portability. Covers the 4 failure modes that prevent bundles from working anywhere but the operator's machine, and the inspect-before-install discipline.

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 prerequisite — load this skill BEFORE every `generate_diagram` tool call. NEVER call `generate_diagram` directly without loading this skill first. Trigger whenever the user asks to create, generate, draw, render, sketch, or build a diagram — flowchart, architecture diagram, sequence diagram, ERD or entity-relationship diagram, state diagram or state machine, gantt chart, or timeline. Also trigger when the user mentions Mermaid syntax or wants a system architecture, decision tree, dependency graph, API call flow, auth handshake, schema, or pipeline visualized in FigJam. Routes to type-specific guidance, sets universal Mermaid constraints, and tells you when to use a different diagram type or skip the tool entirely (mindmaps, pie charts, class diagrams, etc.).

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, debug, balance, or test combat enemy AI for playable action games. Use for aggro, target selection, navigation, spacing, attack choices, telegraphs, retreats, boss behavior, behavior-state machines, and deterministic AI regression tests.

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。

Yellow Network Protocol app sessions for AI agents - a shared room where several agents pool funds, reallocate off-chain at machine speed, and settle one final split. Use for multiparty settlement among agents. Covers connecting, the funded-account prerequisite, creating a session with participants + weights + quorum, deposit, operate, withdraw, close, and the trust boundary. Grounded in the official @yellow-org/sdk lifecycle example.

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。

x402 agentic payments (Coinbase's HTTP 402 protocol). Custody-free tools to pay x402-protected endpoints (build the EIP-3009 transferWithAuthorization the payer signs, assemble the X-PAYMENT header), call a facilitator (verify/settle/supported), and monetize your own API by generating PaymentRequirements. USDC on Base. Triggers: x402, HTTP 402, pay per request, agentic payment, machine payment, X-PAYMENT, transferWithAuthorization, EIP-3009, facilitator, monetize API, pay for API, agent pays.

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。

Entry point for Internet Court — the trust layer for agent-to-agent commerce. Use whenever an agent needs to transact with another agent or a paid service, or a user mentions agent payments, paid APIs (HTTP 402/x402), wallet custody or trust concerns, spending mandates, delegated permissions (ERC-7710/7715), escrow, agent identity or reputation (ERC-8004), negotiation between agents (A2A), agent jobs (ERC-8183), machine payments (MPP, AP2), supervision of agent behavior, revocation, verification, or dispute resolution (GenLayer) — even if they never say "Internet Court". Routes to the vendored protocol skills and connector skills in this 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。