905 件 ・ 関連度順
概要と使いどころ
Drive the macOS desktop in the background — screenshots, mouse, keyboard, scroll, drag — without stealing the user's cursor, keyboard focus, or Space. Works with any tool-capable model. Load this skill whenever the `computer_use` tool is available.
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。
Operate Orca-managed worktrees, folder contexts, terminals, repos, automations, artifacts, skill sharing, worktree comments, and Orca's embedded browser through the `orca` CLI. Use when the user says "$orca-cli", "Orca worktree", "child worktree", "spawn codex/claude in a worktree", "read/wait/send Orca terminal", "handoff" / "handover" / "give this to another agent", "Orca browser", "orca artifacts", or "share skills". Prefer it over raw git worktree, ad hoc PTYs, or Computer Use when Orca state is involved. Use Computer Use only for external windows or desktop UI that needs OS-level control, and Playwright or CDP for external pages.
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 numerical and quantum compute skill for scientific workloads; use when working on numeric algorithms, simulation, or acceleration.
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 Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.
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。
ToolUniverse plugin router. STEP 1 BEFORE ANY ANALYSIS: if the data folder contains `*_executed.ipynb`, run `tu run read_executed_notebook '{"data_folder":"<path>","search":"<keyword>"}'` to extract its cell outputs and apply EVERY filter/sample-exclusion the notebook used — even when the question says 'Using DESeq2/Run X/Compute Y' (this describes the METHOD the notebook used, not a request to rerun). The notebook's cell outputs are the only published authoritative answers; reimplementing or reading stale pre-computed CSVs in the data folder produces different numbers because of outlier-sample removal, library version, and filter steps you don't see by skimming. STEP 2 routing — pick a sub-skill name from this exact list (never invent): tooluniverse-rnaseq-deseq2 (RNA/miRNA-seq DE, correlation, PCA, clustering, dispersion), tooluniverse-gene-enrichment (GO/KEGG/Reactome/GSEA/pathway enrichment), tooluniverse-statistical-modeling (regression, ANOVA, ordinal/logistic, chi-square, correlation, power), tooluniverse-image-analysis (microscopy, colony, fluorescence, dose-response, .tif — including ANOVA / Dunnett / power-analysis on image-derived measurements), tooluniverse-epigenomics (DNA methylation, CpG, m6A, MeRIP-seq, bisulfite, ChIP-seq, chromatin), tooluniverse-sequence-analysis (FASTQ, Trimmomatic, BWA, samtools, coverage), tooluniverse-variant-analysis (VCF, VAF, SNP, mutation), tooluniverse-phylogenetics (treeness, PhyKIT, parsimony), tooluniverse-single-cell (scRNA, h5ad, scanpy), tooluniverse-crispr-screen-analysis (MAGeCK, sgRNA), tooluniverse-proteomics-analysis (mass spec, TMT). Use for CSV/Excel/VCF/FASTA/h5ad and any biology/chemistry/medicine analysis question.
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。
ルート直下の `skills-lock.json` の `computedHash` を upstream リポジトリの最新状態と照合して更新する。`source` が `Fandhe-AI/<repo>` に完全一致しないエントリは clone せず skip (安全弁)。submodule 配下の `skills-lock.json` は触らない。contribute-skill のマージ後や upstream 同期後、「ハッシュ更新」「skills-lock 同期」などで使用。
Vercel プラットフォームリファレンス。Vercel CLI、vercel.json / vercel.ts 設定、 Vercel Functions（Node.js / Edge / Fluid Compute / ISR / Middleware）、 Vercel Blob、Edge Config、Marketplace Storage（Neon / Upstash / Supabase）、 デプロイメント管理（Git 連携 / rollback / rolling release）、 環境変数、ドメイン、DNS、ログ、キャッシュ、CDN。
OpenAI API (developers.openai.com) の Agents SDK (Python / TypeScript) リファレンス。 Agent, Runner, handoffs, guardrails, tracing、 built-in tools（web search, file search, computer use, code interpreter, image generation）、 function tools, vector stores, MCP (Model Context Protocol) connectors, background mode, multi-agent orchestration、 SandboxAgent（隔離実行環境）。
Apple オンデバイス機械学習フレームワークリファレンス。 Core ML / Create ML / Vision / Natural Language / Speech。 MLModel, MLModelConfiguration, MLMultiArray, MLComputeUnits, MLImageClassifier, MLTextClassifier, MLDataTable, VNImageRequestHandler, VNRecognizeTextRequest, VNCoreMLRequest, NLTagger, NLLanguageRecognizer, NLEmbedding, SFSpeechRecognizer, SFSpeechAudioBufferRecognitionRequest, SFTranscription。
Claude API (platform.claude.com) の tool use / Agent Skills / MCP リファレンス。 tool definition (JSON Schema), tool_use / tool_result, server tools（code execution, computer use, web search / web fetch, memory tool, tool search）, Agent Skills (SKILL.md), Skills API, MCP connector, mcp_servers, MCP tunnels, Tunnels API。
visionOS / iOS 空間コンピューティングリファレンス。RealityKit、ARKit、visionOS SwiftUI API。 Entity, ModelEntity, AnchorEntity, Component, RealityView, MeshResource, Material, System, EntityAction, GaussianSplatComponent, ComputeGraphComponent, PhysicsJointsComponent, ForceEffectComponent, ClothBodyComponent, NavigationComponent, NavigationMeshComponent, NavigationController, ImmersiveSpace, ImmersionStyle, Model3D, WindowGroup, VolumetricWindowStyle, ObjectTrackingProvider, RoomTrackingProvider, ARSession, ARWorldTrackingConfiguration, ARKitSession, WorldTrackingProvider, HandTrackingProvider, SceneReconstructionProvider。
Claude Code (code.claude.com) の各サーフェス（Desktop / IDE / Web / モバイル / ターミナル UX）リファレンス。 Desktop app（launch.json, scheduled tasks, WSL, Linux, iOS simulator）、 VS Code / JetBrains / Chrome 拡張連携、claude.ai/code（Web）と mobile アプリ、 interactive mode, fullscreen, voice dictation, accessibility, computer use, artifacts。
Reads, queries, and writes bigWig indexed binary signal tracks (coverage, fold-change, conservation, methylation-rate) with pyBigWig (Python) and the UCSC Kent tools (bedGraphToBigWig, bigWigToBedGraph, bigWigInfo, bigWigSummary, bigWigAverageOverBed) and deepTools (multiBigwigSummary, computeMatrix, bigwigCompare). Covers the central trap that a wide query returns a precomputed zoom-level summary (by default the mean, which annihilates narrow peaks) not per-base data, when exact=True/values() is mandatory, the NaN-not-zero gap-handling fork, choosing mean vs max vs sum vs coverage by biological question, and the sorted-bedGraph plus chrom.sizes build requirement. Use when extracting signal at regions, computing mean signal per gene/peak, building a browser track from bedGraph, comparing tracks, or building TSS/gene-body metaprofiles.
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。
Build and deploy applications when the user selects or invokes the Prisma plugin, including requests like "Build a simple Todo app and deploy it" that do not name a stack. Use Prisma Composer for new apps and Prisma Compute for deployment. Also use for explicit Composer/Compute requests, deployment diagnostics, and recovering Composer deployments. Does not cover unrelated ORM or database administration.
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。
Reads, queries, and writes bigWig indexed binary signal tracks (coverage, fold-change, conservation, methylation-rate) with pyBigWig (Python) and the UCSC Kent tools (bedGraphToBigWig, bigWigToBedGraph, bigWigInfo, bigWigSummary, bigWigAverageOverBed) and deepTools (multiBigwigSummary, computeMatrix, bigwigCompare). Covers the central trap that a wide query returns a precomputed zoom-level summary (by default the mean, which annihilates narrow peaks) not per-base data, when exact=True/values() is mandatory, the NaN-not-zero gap-handling fork, choosing mean vs max vs sum vs coverage by biological question, and the sorted-bedGraph plus chrom.sizes build requirement. Use when extracting signal at regions, computing mean signal per gene/peak, building a browser track from bedGraph, comparing tracks, or building TSS/gene-body metaprofiles.
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。
Comprehensive guide for developing WebGPU-enabled Three.js applications using TSL (Three.js Shading Language). Covers WebGPU renderer setup, node materials, compute shaders, post-processing, and WGSL integration. Use when working with Three.js WebGPU, TSL shaders, or GPU compute 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。
Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).
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。
Govern Power Automate flows and Power Apps at scale using the FlowStudio MCP cached store. Classify flows by business impact, detect orphaned resources, audit connector usage, enforce compliance standards, manage notification rules, and compute governance scores — all without Dataverse or the CoE Starter Kit. Load this skill when asked to: tag or classify flows, set business impact, assign ownership, detect orphans, audit connectors, check compliance, compute archive scores, manage notification rules, run a governance review, generate a compliance report, offboard a maker, or any task that involves writing governance metadata to flows. Requires a FlowStudio for Teams or MCP Pro+ subscription — see https://mcp.flowstudio.app
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。
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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。
Expert in drone systems, computer vision, and autonomous navigation. Specializes in flight control, SLAM, object detection, sensor fusion, and path planning. Activate on "drone", "UAV", "SLAM", "visual odometry", "PID control", "MAVLink", "Pixhawk", "path planning", "A*", "RRT", "EKF", "sensor fusion", "optical flow", "ByteTrack". NOT for domain-specific inspection tasks like fire detection, roof damage assessment, or thermal analysis (use drone-inspection-specialist), GPU shader optimization (use metal-shader-expert), or general image classification without drone context (use clip-aware-embeddings).
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。
Discordのデスクトップアプリで、ログイン済みの本人アカウントから承認済みの告知などを投稿し、送信先・本文の確認から投稿結果の確認まで進めるスキル。