3,593 件 ・ 関連度順
概要と使いどころ
Implement, compose, test, and debug LuisaCompute XIR transformation and analysis passes. Use when modifying files under src/xir/passes or include/luisa/xir/passes, changing CFG or SSA invariants, authoring a PassPipeline, or diagnosing pass-order, reachability, PHI, dominance, aliasing, or memory-effect bugs.
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 guidance for the Fabric CLI (`fab`) and the Fabric and Power BI REST APIs: workspaces, items, lakehouses, notebooks, pipelines, semantic models, reports, capacities, OneLake, deployment and admin. Also estimates the capacity units (CU) an operation will consume and its impact on the capacity before running it. Automatically invoke whenever the user mentions Fabric, Power BI Service, a Fabric or Power BI workspace, a capacity or F SKU, OneLake, `fab`, or asks to create, run, refresh, schedule, deploy or delete anything in Fabric, including preview items such as Plan, Ontology, Graph or Copilot.
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 Godot 4.7 work must run without a mouse or at scale: batch import of hundreds of GLB props or an AI image-to-3D art drop, naming rules, import presets and post-import scripts, editor plugins, EditorScript and @tool, EditorImportPlugin, GUT or gdUnit4 tests in CI, a GitHub Actions export workflow, GDExtension basics, or a Godot MCP server; also when --import is slow, textures stay uncompressed, tests pass locally but CI fails, or an export ships test folders.
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 drives Unity 6.3 (6000.3) on a Mac for any task: batch mode and -executeMethod jobs, a running editor (the user's GUI editor or a headless one), the Unity CLI and com.unity.pipeline, MCP servers, the Test Framework, command-line builds; when a batch run fails silently, 'Scripts have compiler errors', a capture comes back black or pink, or a result must be proven with numbers and a frame; or when a Unity brief must go to the right scenario-unity-* specialist.
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 a task involves running a Scenario workflow through MCP, including anything the user calls a Scenario app, a saved pipeline, or a multi-step generation graph. Triggers include listing workflows, building workflow_run's inputs object, pricing a run with a dry run, approving or rejecting a stuck approval node, a run rejected for input length, or a workflows_list reply flooding the context. Creating or editing graphs is scenario-workflow-authoring. Keywords: workflow, app, approval gate.
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 creating or refining 3D assets with Meshy models on Scenario via MCP: image-to-3D from one photo or 1-4 multi-view angles, text-to-3D, retexturing an existing GLB, remeshing to a target polycount, UV unwrapping, auto-rigging a humanoid character, or applying a library animation clip. Keywords: Meshy 7 and Meshy 6, Ultra mode, Smart Topology, PBR maps, texture prompt, triangle or quad topology, game-ready mesh, A-pose, T-pose, GLB 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。
Adds Azure DevOps connector to a Power Apps code app. Use when querying work items, creating bugs, managing pipelines, or making ADO API 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。
Validation-first design for Cavekit — every kit requirement must be automatically verifiable. Six-gate validation pipeline, phase gates, merge protocol, completion signals, acceptance criteria patterns. Use when designing acceptance criteria, wiring CI gates, or debugging why an agent is producing output that nobody can verify.
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 delivery management for release planning, deployment strategy, incident response, change management, SLA/error-budget tracking, and DORA metrics across continuous delivery 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。
Full content production pipeline from blank page to publish-ready piece: research, briefs, drafting, SEO, readability, and editorial gates. Use when writing blog posts, articles, guides, or long-form content end-to-end at scale.
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。
Programmatic page generation at scale using template-based SEO and data pipelines: keyword pattern mining, template architecture, and indexation for 100-100K+ pages. Use when building SEO pages at scale or scoping a programmatic SEO build.
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 calibrated interview loops, competency-based question banks, and hiring calibration. Use when designing interview processes, creating hiring pipelines, generating scoring rubrics, analyzing interviewer bias, or building question banks.
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。
Computer vision engineering for object detection, segmentation, and visual AI, covering CNN and Vision Transformer architectures and ONNX/TensorRT deployment. Use when building detection pipelines, training models, or optimizing inference.
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 RAG pipelines: chunking, retrieval evaluation, and architecture. Use when building a RAG system, selecting a chunking strategy, choosing a vector database, optimizing retrieval quality, or evaluating with RAGAS metrics.
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 "design an experiment", "build a predictive model", "run A/B test analysis", "perform causal inference", "engineer features", "evaluate model performance", "set up MLOps pipeline", "analyze time series", "calculate sample size", or "deploy a model to production". Expert data science covering statistical modeling, experimentation, causal inference, feature engineering, ML deployment, and advanced analytics with Python, R, and SQL.
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。
DevOps for CI/CD, containers, Kubernetes, and Terraform. Use when building pipelines, containerizing apps, managing clusters, provisioning cloud infra, deploying with blue- green/canary, or handling infrastructure incidents.
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。
Context management engine for AI coding agents. Use when building agent memory systems, optimizing context windows, allocating token budgets, designing RAG pipelines for code, or managing persistent multi-session agent 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。
Generate and optimize GitHub Actions CI/CD workflows. Use when designing workflows, planning multi-environment deployments, optimizing pipeline cost and runtime, or implementing blue- green, canary, or rolling deployment strategies.
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。
This skill should be used when the user asks to "scan AI systems for security threats", "check for prompt injection vulnerabilities", "assess model security posture", "detect data poisoning risks", or "audit AI/ML pipeline security".
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。
Data science across machine learning, statistical modeling, and experimentation. Use when selecting ML algorithms, engineering features, designing A/B tests, evaluating model performance, or building predictive 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。
Design multi-agent orchestration with workflow DAGs, routing, handoff protocols, and state management. Use when building pipelines of specialized agents, designing fan-out/fan-in patterns, or implementing fault-tolerant 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。
Audit data quality across pipelines, warehouses, and stores. Use when designing a DQ program, defining DQ dimensions, building rule-based checks, detecting schema drift, monitoring freshness SLAs, or responding to a DQ incident.
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。
Histopathology image preprocessing and analysis 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。
Fully automated review pipeline: CEO, Design, and Eng reviews with auto-decisions using 6 principles (completeness, boil lakes, pragmatic, DRY, explicit over clever, bias toward action). Only pauses for premise confirmation. Classifies decisions as Mechanical (silent) or Taste (surfaced at final gate).
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。