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概要と使いどころ
Enable or disable MCP tools by name in batch. Persists the change via `UnityMcpPluginEditor.Instance.Save()` only when at least one tool actually flipped. Returns per-input success flags plus optional operation logs.
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。
Duplicate a batch of GameObjects in the currently opened Prefab or active Scene. Marks each affected scene as dirty after duplication. Use 'gameobject-find' to locate the source GameObjects first.
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。
Decompose large tasks across parallel agents — use for migrations, multi-file refactors, or batch 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。
Check domain registration in batches using public RDAP APIs, without keys or login. Use only when the user explicitly invokes /domain-checker.
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 to run a holistic regrounding pass on the entire microsoft/apm documentation corpus against current source code, page-by-page, and emit surgical fixes for stale claims. Activate when the maintainer wants a WHOLE-CORPUS audit (not per-PR review) -- typical triggers include "audit the docs", "reground the corpus", "check every page against code", "pre-release docs sweep", "the docs have drifted everywhere", or "we just reshaped the TOC, find dead links". Wave-batched and S7-verified; scales to the full ~112-page corpus in ~10 minutes wall-time. This is a SIBLING to docs-sync, not a replacement: docs-sync is per-PR (triggered by a diff); this skill is per-corpus (triggered by a maintainer ask). They share agent personas, schemas, and the docs index, but their triggers MUST NOT collide. Does NOT auto-merge, does NOT push without maintainer review, and does NOT replace per-PR drift detection.
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。
Plan and execute large refactor or rewrite efforts efficiently with parallel multi-agent analysis and implementation. Use when a user asks to refactor many files, split workstreams, analyze a target code area, and coordinate sub-agents with clear ownership and dependency-aware execution.
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 experiments and studies BEFORE data is collected — choosing a design, randomizing, blocking, and laying out treatment combinations so the results will actually be interpretable. Use whenever someone is planning a study, asks how to assign subjects/samples to groups, mentions randomization, blocking, stratification, controls, factorial or fractional-factorial designs, design of experiments (DOE), screening many factors, response-surface optimization, crossover or repeated-measures or split-plot designs, cluster/group randomization, Latin squares, plate layouts, batch/run-order effects, replication vs. pseudoreplication, or sequential/adaptive/group-sequential designs. Trigger this even for informal phrasings like "how should I set up this experiment", "how do I avoid confounding", "what's the best way to test these 6 factors", or "assign these mice to conditions". For computing the sample size or power once the design is chosen, use statistical-power; for analyzing data already collected, use statistical-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。
Create complete specs (requirements, design, tasks) for all features in roadmap.md sequentially by dependency wave in Cascade.
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 complete specs for roadmap features by dependency wave using Devin subagents when available, with permission-aware sequential and inline fallbacks.
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 complete specs (requirements, design, tasks) for all features in roadmap.md using parallel subagent dispatch by dependency wave.
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 complete specs (requirements, design, tasks) for all features in roadmap.md using parallel sub-agent dispatch by dependency wave.
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 for text-to-speech narration or voiceover, accessibility reads, audio prompts, or batch speech generation via the OpenAI Audio API; run the bundled CLI (`scripts/text_to_speech.py`) with built-in voices and require `OPENAI_API_KEY` for live calls. Custom voice creation is out of scope.
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。
Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across 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。
Single-cell RNA-seq and retained scverse workflow owner. Load .h5ad/10X data, manage AnnData metadata, plan scVI/scANVI batch-correction workflows, QC, normalization, PCA/UMAP/t-SNE, Leiden clustering, marker genes, cell type annotation, trajectory, for scRNA-seq 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。
Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.
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。
Direct REST API access to PubMed. Advanced Boolean/MeSH queries, E-utilities API, batch processing, citation management. For Python workflows, prefer biopython (Bio.Entrez). Use this for direct HTTP/REST work or custom API implementations.
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 to generate or edit images via the OpenAI Image API (for example: generate image, edit/inpaint/mask, background removal or replacement, transparent background, product shots, concept art, covers, or batch variants); run the bundled CLI (`scripts/image_gen.py`) and require `OPENAI_API_KEY` for live calls.
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 to "analyze video", "watch this video", "what happens in this video", "describe this clip", "review this footage", "classify these videos", "compare videos", "analyze this image", "what's in this screenshot", or when the user provides a video/image file path and expects visual understanding. Also trigger on: "qwen", "video bridge", "multimodal analysis", "motion analysis", "video reference", "video breakdown", "batch classify", or any task requiring understanding of video content that Claude cannot do natively.
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。
Automate Segment tasks via Rube MCP (Composio): track events, identify users, manage groups, page views, aliases, batch operations. Always search tools first for current schemas.
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。
电商素材工坊（中英双语）。用户需要生成电商主图、详情图、场景图、产品图合成、品牌叠加时使用。自动识别品类→匹配风格→场景感知合成→统一文字→自动质检→多平台适配→标准化交付。E-commerce product image studio: category detection, style matching, scene-aware compositing, text overlay, quality check, multi-platform adaptation, batch delivery.
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, refine, or fix NVIDIA voice agents (Cascaded or Omni) with Pipecat or LiveKit. Use when building, scaffolding, or iterating on a real-time voice-agent pipeline, including speech (ASR/TTS) customization and cloud or local deployment. Not for offline/batch speech-to-text, text-only chat or RAG, or generic Docker or CUDA work unrelated to a voice agent.
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。
Convert single-node scripts to multi-node Slurm sbatch jobs and debug common multi-node failures. Covers srun-native vs uv run torch.distributed approaches, container setup, NCCL timeouts, OOM sizing for MoE models, and interactive allocation.
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。
How to launch distributed Megatron-LM training jobs on a SLURM cluster. Covers a minimal sbatch skeleton, environment-variable setup for torch.distributed.run, CUDA_DEVICE_MAX_CONNECTIONS rules across hardware and parallelism modes, container conventions, monitoring, and per-rank failure diagnosis.
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 when the user is measuring GPU-kernel-initiated RDMA WRITE latency through doca-gpunetio — building and running the `gpunetio_ib_write_lat` client + server pair under `doca/tools/gpunetio_ib_write_lat/`, checking GPU-NIC pairing, reading the half-iter / full-iter / CUDA-side usec columns, characterizing median / p99 / jitter for a real-time control loop, picking GPUNetIO vs GPI vs CPU-initiated `perftest`, or weighing the latency-vs-batching trade-off. Trigger even without 'GPUNetIO' or 'ib_write_lat': 'GPU kernel RDMA latency benchmark', 'how fast can a CUDA kernel post a WRITE', 'p99 RDMA latency on H100 + ConnectX', 'kernel-launched WR tail latency', or 'compare GPU-init vs CPU-init perftest'. Route elsewhere for bandwidth runs (doca-gpunetio-ib-write-bw), the GPI surface (doca-gpi), library debugging (doca-gpunetio), or DOCA install.
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。