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概要と使いどころ
Build and ship Unity 6.3 LTS players: build settings and scenes, player/quality settings, the IL2CPP vs Mono scripting backend, managed code stripping, scripted BuildPipeline.BuildPlayer, and CI/headless builds. Use when configuring or automating a build, choosing a scripting backend, shrinking build size, or when the user mentions Unity build, player settings, IL2CPP, code stripping, or Addressables.
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。
Two pipelines ending at a scanner-look PDF. (1) Phone photos of paper documents (contracts, stamped certificates, receipts, forms, handwritten notes) → clean scanner-quality PDF: perspective rectification + noteshrink whitening + A4 assembly + mandatory whole-document check. Trigger: "把照片 做成扫描件", "photos to scanned PDF", "make this look scanned", "手机拍的 文档转 PDF", "盖章文件扫描", replacing pages in an existing scanned PDF, any CamScanner-like request. (2) A digital document with no signature yet (rendered docx/PDF, confirmation form, contract draft) → make it look hand-signed and scanned: synthesize a handwriting-style signature, composite it onto the signature line, apply the same scan-look post-processing. Trigger: "帮我做个手写签名", "生成签名盖到这份文件上", "做成签过字的扫描件", "synthesize a signature", any request for a document that needs to look signed without a real photographed signature. Do NOT hand-roll levels/contrast enhancement for scan-look — tried and rejected twice; this skill's pipeline is the proven one.
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。
Guides Docker, CI/CD pipelines, deployment strategies, infrastructure as code, and observability setup. Use when writing Dockerfiles, configuring GitHub Actions, planning deployments, setting up monitoring, or when asked about containers, pipelines, Terraform, or production infrastructure.
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。
Manages Fabric Dataflow Gen2 items, including creation, M editing, connections, output destinations, getDefinition and updateDefinition inspection, executeQuery, refresh history, and Gen1-to-Gen2 save-as upgrades via saveAsNativeArtifact. Use for Dataflow items or Power Query M definitions. For pipeline JSON use pipeline-migration; for Spark or Warehouse transformations use their own skills.
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 Sankey, alluvial, river, and CONSORT-style flow diagrams to visualize cohort transitions, cell-state changes, or pipeline filtering using ggalluvial, networkD3, plotly, and consort. Use when showing how entities move between categories across timepoints (cell states, drug response classes, patient flow through a trial) or filtering pipelines (variants filtered through QC stages).
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 read-only MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents. Use this skill whenever the user asks to write, create, or generate MongoDB queries, wants to filter/query/aggregate data in MongoDB, asks "how do I query...", needs help with query syntax, or discusses finding/filtering/grouping MongoDB documents. Also use for translating SQL-like requests to MongoDB syntax. Does NOT handle Atlas Search ($search operator), vector/semantic search ($vectorSearch operator), fuzzy matching, autocomplete indexes, or relevance scoring - use search-and-ai for those. Does NOT analyze or optimize existing queries - use mongodb-query-optimizer for that. Does NOT handle aggregation pipelines that involve write operations. Requires MongoDB MCP server.
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 troubleshoot Cloudflare Basin analytics workflows with Basin Pipelines, Basin Catalog, and Basin SQL. Use for streaming data into R2 Iceberg tables, managing catalogs, or querying those tables; also use for requests using the former Data Platform, Pipelines, R2 Data Catalog, or R2 SQL names.
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。
Turn raw MATLAB code into a published .mltbx toolbox — full pipeline from scope definition through packaging and release. Drives 8 phases with human checkpoints, enforcing ordering and dependencies. Use when asked to package, create a toolbox, or run the files-to-package pipeline.
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。
Creates MATLAB interfaces to Python image processing and computer vision models from GitHub repositories or pip-installable packages using MPyReq. Use when asked to interface MATLAB with a Python CV/image model (segmentation, depth estimation, object detection, image generation, super-resolution, etc.), given a GitHub repo URL for an image/vision model, or asked to create an MPyReq demo for a deep-learning vision pipeline. Do NOT use for general-purpose Python-MATLAB interfacing, non-vision models (NLP, tabular, audio), model deployment/serving, or MATLAB-only image processing 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。
Orchestrate AI/ML pipelines for data ingestion, model training, batch inference, and RAG indexing using Prefect, Airflow, or Dagster. Build reliable, observable, and retriable workflows for production AI systems.
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。
Set up Azure Pipelines for CI/CD, configure build and release pipelines, manage Azure DevOps projects, and integrate with Azure services. Use when working with Azure DevOps Services or Server for enterprise DevOps 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。
Orchestrate iOS screenshot automation with xcodebuild/simctl for build-run, AXe for UI actions, JSON settings and plan files, Koubou-based framing (`asc screenshots frame`), and screenshot upload (`asc screenshots upload`). Use when users ask for automated screenshot capture, AXe-driven simulator flows, frame composition, or screenshot-to-upload 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。
Legacy IR and XIR compiler pipeline, AST lowering, SSA IR, and optimization passes.
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。
Blender to web export workflows for 3D models and animations. Use this skill when exporting Blender models to glTF for web, optimizing 3D assets for Three.js or Babylon.js, batch processing models with Python scripts, automating Blender workflows, or creating web-ready 3D pipelines. Triggers on tasks involving Blender glTF export, bpy scripting, 3D asset optimization, model compression, texture baking, or Blender automation. Exports models for threejs-webgl, react-three-fiber, and babylonjs-engine skills.
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 automating a Unity 6.3 content pipeline or build: importing an art drop (FBX, PNG) with AssetPostprocessor rules and naming conventions, generating prefabs or editing scenes from script with correct Undo and SetDirty, Addressables groups, labels and content builds, content-validation tests, batch-mode, command-line or CI builds (Build Profiles, GameCI, GitHub Actions, Accelerator), editor tools; or when textures import wrong or reimport every run, a scripted edit vanishes, bundles duplicate assets, a batch build ignores its defines, or CI cannot activate Unity.
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 importing FBX, USD, OBJ or glTF from Maya, ZBrush or Blender into Unreal Engine 5.8 in bulk or reimporting changed files, writing Editor Python tools, Interchange pipelines or import presets, enforcing naming conventions, assigning material instances by script, setting Nanite, collision and LODs per asset class, writing data validators or automation tests, fixing redirectors, or cooking and packaging from the command line (BuildCookRun, RunUAT, CI, Perforce). Also when a scripted import ignores its settings or names, a Python validator never runs, or a Mac build will not 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。
Use when writing or fixing Maya pipeline code in Python: mayapy batch jobs over many scenes, validating and auto-fixing scenes, FBX export for Unreal or Unity (static, skeletal, animation clips, Game Exporter), Alembic or GPU caches, USD export, stages and layers, references and namespaces, PySide6 tool windows, OpenMaya 2.0 plug-ins, or slow playback. Also when an FBX lands with wrong scale, normals, tangents or extra takes, a USD export has one frame or smoothed meshes, or one bad file kills a batch.
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。
Reference for the `odu` runner — how to invoke a full pipeline, a single recipe, or a platform-pinned node, and how to attach to a live run, from a project whose CI odu runs. Trigger when the user asks to "run CI", "run the pipeline", "re-run a check", to run named lanes or recipes (e.g. "run fmt and nix", "just the e2e lane", bare selectors like `fmt`/`nix`/`e2e`), or names a recipe by `<recipe>@<platform>`. This skill — not a repo's local `just ci` / `just <recipe>` — is how an odu-run request is served.
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。
Designs production-grade ML pipeline infrastructure with experiment tracking, Kubeflow/Airflow DAGs, feature store schemas, model registries, and automated retraining 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。
Multi-regulation privacy compliance navigator. Use for GDPR, CCPA, LGPD, POPIA, PIPEDA, PDPA, Privacy Act, PIPL, UK GDPR compliance assessments, DPA reviews, and data subject request management.
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。
ML engineering skill for productionizing models, building MLOps pipelines, and integrating LLMs. Covers model deployment, feature stores, drift monitoring, RAG systems, and cost 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。
Orchestrate end-to-end release pipelines. Use when running pre-release validation, generating changelogs, bumping semantic versions, scoring deployment readiness, or gating releases with secret scanning and GO/NO-GO checks.
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。
Analyzes pipeline coverage, tracks forecast accuracy with MAPE, and calculates GTM efficiency metrics for SaaS revenue 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。