「nodes」の検索結果

Expert en ComfyUI workflows avancé (install, custom nodes, model management, workflow 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。

Write Python code in n8n Code nodes. Use when writing Python in n8n, using _input/_json/_node syntax, working with standard library, or need to understand Python limitations in n8n Code 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。

mssql (node-mssql, Node.js 向け Microsoft SQL Server クライアント) の API リファレンス。 tedious / msnodesqlv8 ドライバー、SQL Server / T-SQL / TDS 接続、ConnectionPool、 `sql.query` タグ付きテンプレート、bulk insert、Table-Valued Parameter (TVP)、 prepared statement、isolation level、Diagnostics Channel、Azure AD 認証、CLI。 Drizzle ORM 経由の SQL Server 利用 (drizzle-orm/mssql-core) は別スキル drizzle が対象。

An innate exploratory drive that guarantees no graph node remains permanently invisible to an agent population. After a warm-up period (step > 3), each agent fires a coverage scan with probability |unseen_nodes| / |all_nodes|. When triggered, the agent teleports directly to the globally unseen node with the highest belief uncertainty, works it, and returns control to the normal action-selection loop. Probability is self-annealing: it starts near 1.0 when almost everything is unseen and decays toward 0.0 as coverage saturates, requiring no manual schedule. The mechanism is "innate" (hard-coded into the agent step loop) as distinct from the "adaptive" EFE path, which requires local neighbors and learned precision weights. Together they form a two-layer exploration architecture: adaptive for locally-guided foraging, innate for topological completeness.

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。

Itôで予約済みのGPUを使ったモデル配信の相談に対し、現状の未対応範囲と将来必要な確認事項を整理するスキル。現在は配信を開始せず、制約を案内します。

• Itô GPU予約後の配信対応を確認したいとき
• OpenAI互換の接続先の制約を知りたいとき
• 将来のモデル配信設定を確認したいとき

予約済みのItô GPU環境で機械学習の訓練が利用可能かを確認するスキル。現状は訓練を実行せず、未提供の機能と将来の引き渡し条件を整理します。

• GPU予約後に訓練可否を確認したいとき
• 追加学習の引き渡し要件の確認
• 予約・予算・確認手続きの整理

SVG curve connectors, sticky-note nodes, and cursor interaction with a whiteboard-brainstorm feel.

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。

Reach RuView sensing hardware from the machine it is attached to — ESP32 CSI nodes (serial + UDP stream), 60 GHz MR60BHA2 / 24 GHz LD2410 mmWave radars, RPLIDAR and iPhone LiDAR — and from other machines over SSH, read-only.

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。

ESP32-S3 / ESP32-C6 firmware build, flash, WiFi provisioning, and serial monitoring for RuView CSI sensing nodes. Use when setting up physical hardware, reflashing a node, or debugging a device that isn't streaming CSI.

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。

Advanced RuView capabilities — RuvSense multistatic sensing (attention-weighted fusion, geometric diversity, persistent field model), cross-viewpoint fusion across multiple nodes, RF tomography (ISTA L1 solver, voxel grids), longitudinal biomechanics drift, pre-movement intention signals, adversarial signal detection, and multistatic mesh security hardening. Use for research-grade or multi-node deployments.

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 MiniMax H3 (Hailuo 3.0) video through the official MiniMax v2 API, fal.ai, Runway, ComfyUI Partner Nodes, or local open weights in ComfyUI. Use for 4-15 second 2K clips, first/last-frame animation, and image/video/audio reference-conditioned video.

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 4-30 second cinematic video with ByteDance Seedance 2.5 through fal.ai, Volcengine Ark, Runway, or ComfyUI Partner Nodes. Use for long single generations, synchronized audio, and large multimodal reference sets (up to 30 images, 10 videos, and 10 audio clips). Also covers the 2.5 prompt contract: section order, multi-shot "Hard cut" breakdown, named locks for continuity across cuts, the asset and character-sheet method, voice conditioning, and iteration 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。

Create and edit JSON Canvas files (.canvas) with nodes, edges, groups, and connections. Use when working with .canvas files, creating visual canvases, mind maps, flowcharts, or when the user mentions Canvas files in Obsidian.

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。

Queries a pinned Precision Medicine Knowledge Graph (PrimeKG) CSV for typed gene, drug, disease, and phenotype nodes, direct associations, disease context, and one- or two-hop paths. Use for PrimeKG reproducibility, biological association lookup, and hypothesis generation with relation and data provenance preserved.

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 the frozen-class/visitor pattern for discriminated unions that have multiple dispatch sites. Use when creating a new set of variants (commands, IR nodes, factory calls) that will be switched over in 2+ places, or when refactoring an existing union type that has grown multiple switch sites.

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。

Azure Batch SDK for Java. Run large-scale parallel and HPC batch jobs with pools, jobs, tasks, and compute 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。

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for Active Directory, Kerberos, LDAP, OAuth, enterprise messaging, Windows host forensics, credential material, and lateral-movement challenges. Use when the user asks to trace tickets or tokens, inspect mailbox rules, analyze Windows host evidence, understand an AD trust path, or explain a lateral-movement chain across sandbox-linked nodes. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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。

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for Linux credential artifacts, service tokens, SSH material, cloud and container secrets, socket-level trust, and host-to-host pivot chains. Use when the user asks to trace Linux auth artifacts, accepted token or key replay, socket or service-account trust edges, sudo or capability abuse, or explain lateral movement across Linux challenge nodes. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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。

Compile documents, notes, web content, transcripts, research materials, or code repositories into an evidence-grounded, visualization-ready knowledge graph with semantically typed entity nodes, statement-level provenance, and typed relationship edges. Use with ov compile to create or incrementally refresh `entities/*.md` node artifacts and a root `relations.jsonl` edge file for people, organizations, groups, animals, places, products, projects, systems, services, modules, datasets, standards, documents, events, and other identifiable things.

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。

Deploys and configures Tailscale (or self-hosted Headscale) as a WireGuard-based zero trust mesh VPN, setting up identity-aware ACLs, exit nodes, subnet routers, and MagicDNS for encrypted peer-to-peer connectivity. Use when replacing traditional VPN servers with an identity-authenticated mesh network or enforcing granular per-device access control lists.

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。

Installs and runs the kube-bench tool against a Kubernetes cluster as a Job, DaemonSet, or standalone binary, selecting the correct benchmark version and targets (control plane, etcd, kubelet, worker nodes) and emitting JSON or JUnit output for pipelines. Use when setting kube-bench up for the first time, choosing which benchmark version and node targets to run, wiring it into CI, or troubleshooting skipped or misdetected checks. Keywords: kube-bench, DaemonSet, --benchmark, --targets, JSON output, JUnit, CI integration. Do not use for interpreting the findings or producing an audit report - use performing-kubernetes-cis-benchmark-with-kube-bench.

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 the Figma MCP server to fetch design context, screenshots, variables, and assets from Figma, and to translate Figma nodes into production code. Trigger when a task involves Figma URLs, node IDs, design-to-code implementation, or Figma MCP setup and troubleshooting.

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。

Translate Figma nodes into production-ready code with 1:1 visual fidelity using the Figma MCP workflow (design context, screenshots, assets, and project-convention translation). Trigger when the user provides Figma URLs or node IDs, or asks to implement designs or components that must match Figma specs. Requires a working Figma MCP server connection.

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。