「pipeline」の検索結果

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 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。

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。

Infrastructure-first security audit across 14 phases: attack surface mapping, secrets archaeology (git history), supply chain analysis, CI/CD pipeline security, LLM/AI threat detection, OWASP Top 10, STRIDE threat modeling. Confidence gates, exploit scenarios, parallel verification, trend tracking. 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。

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。

Writes Apex code, builds Lightning Web Components, optimizes SOQL queries, implements triggers, batch jobs, platform events, and Salesforce DX CI/CD 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。

Designs production-grade RAG systems with document chunking, embedding generation, vector store configuration, hybrid search pipelines, reranking, and retrieval quality evaluation.

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 Dockerfiles, configures CI/CD pipelines, writes Kubernetes manifests, and generates Terraform/Pulumi infrastructure templates for deployment automation and GitOps.

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。

Asset naming and pipeline validation

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。

Release pipeline checklist

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。

AWS SAM and AWS CDK deployment for serverless applications. Triggers on phrases like: use SAM, SAM template, SAM init, SAM deploy, CDK serverless, CDK Lambda construct, NodejsFunction, PythonFunction, SAM and CDK together, serverless CI/CD pipeline. For general app deployment with service selection, use deploy-on-aws plugin instead.

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。

Migrate, modernize, and upgrade codebases to AWS. Run analysis on repos for tech debt, security vulnerabilities, and modernization opportunities. Transforms .NET Framework to .NET 8/10, mainframe COBOL to Java, VMware VMs to EC2, SQL Server to Aurora, and upgrades Java/Python/Node.js versions and AWS SDKs. Use when the user says "migrate .NET to AWS", "upgrade Java to 17/21", "modernize COBOL", "modernize mainframe", "move VMware to EC2", "convert SQL Server to Aurora", "upgrade Python version", "migrate AWS SDK", "transform this codebase", "analyze for issues", "find tech debt", "what tech debt", "security vulnerabilities", "CVEs", "what's wrong with my code", "assess my repos", "where do I start", "find what's outdated", "analyze my repos", "AWS Transform - continuous modernization", "continuous modernization" or "continuous-modernization". Don't use for infrastructure provisioning, CI/CD pipelines, or general coding tasks.

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。

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。

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。

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。

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。

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。