「pipeline」の検索結果

Use when working with Singlestore — singleStore (MemSQL) workspace management, pipeline status, query tuning, memory analysis, and cluster health.

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 run multi-agent video production 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。

Teams meeting summaries, job replay, Graph subscriptions.

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。

LLM) can be manipulated through prompt injection, whether a retrieval pipeline leaks confidential da

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 production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.

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。

Patterns and architectures for autonomous Claude Code loops — from simple sequential pipelines to RFC-driven multi-agent DAG 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。

12-agent academic paper writing pipeline. 11 modes (full/plan/outline/revision/revision-coach/abstract/lit-review/format-convert/citation-check/disclosure/rebuttal-audit). 6 paper types, 5 citation formats, bilingual abstracts, LaTeX/DOCX-via-Pandoc/PDF output. Style Calibration + Writing Quality Check + Anti-Patterns with IRON RULE markers. Triggers: write paper, academic paper, guide my paper, parse reviews, audit my rebuttal, check my response draft, AI disclosure, 寫論文, 學術論文, 引導我寫論文, 審查意見, 評估回覆, 논문 작성, 초록 작성, 논문 수정, 논문 계획을 도와줘, 심사 의견 반영, 답변서 점검, AI 사용 고지.

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 or review production-ready Tavily SDK and API integrations for web search, extraction, crawling, mapping, and research. Use when implementing Tavily in an agent, RAG pipeline, or application rather than only running one CLI command.

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。

MongoDB with Mongoose — schemas, models, aggregation pipelines, migrations, and Atlas connections. Use when designing collections, writing queries, or integrating MongoDB into Node.js/Next.js apps.

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 publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA. Use when someone asks to create, generate, ship, or publish a Space, demo, Gradio app, or playground for a LoRA — including LoRAs for Qwen-Image, Qwen-Image-Edit, LTX-Video, Wan, FLUX, SDXL, or other diffusion base models. Also triggers when someone describes a LoRA they trained or hosts on the Hub and wants to share it. Covers picking the right base pipeline and `diffusers` inference recipe, designing a UI tailored to the LoRA's task and inputs (Union/multi-task control, edit, video, image, etc.), respecting model-card recommendations (trigger words, steps, guidance, LoRA scale, example inputs), and shipping to ZeroGPU hardware as a private Space by default.

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。

MANDATORY prerequisite — load this skill BEFORE every `generate_diagram` tool call. NEVER call `generate_diagram` directly without loading this skill first. Trigger whenever the user asks to create, generate, draw, render, sketch, or build a diagram — flowchart, architecture diagram, sequence diagram, ERD or entity-relationship diagram, state diagram or state machine, gantt chart, or timeline. Also trigger when the user mentions Mermaid syntax or wants a system architecture, decision tree, dependency graph, API call flow, auth handshake, schema, or pipeline visualized in FigJam. Routes to type-specific guidance, sets universal Mermaid constraints, and tells you when to use a different diagram type or skip the tool entirely (mindmaps, pie charts, class diagrams, etc.).

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。

Git operations, shell scripting, CI/CD pipelines, and terminal automation. Use for conventional commits, PowerShell/Bash scripting, configuring GitHub Actions, or automating development tooling 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。

NVIDIA DeepStream SDK development guidance for Python pyservicemaker pipelines, video analytics, TensorRT integration, and streaming inference 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。

NVIDIA DeepStream model-import guidance for bringing vision models from Hugging Face or NVIDIA NGC into DeepStream pipelines with export, TensorRT build, and benchmark steps.

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 doc generation with JSDoc/TSDoc, linters, and pre-commit hooks. Use when setting up markdownlint, configuring doc linting pipelines, integrating JSDoc/TSDoc, or building automated documentation 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。

Evaluate and improve AI-generated output with explicit rubrics, reflection loops, and stop conditions. Use when building self-critique workflows, evaluator-optimizer pipelines, or acceptance gates for code, docs, analysis, or plans.

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 en data engineering (ETL, Airflow, dbt, data lakes, streaming pipelines, data quality)

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 a .NET 10 project or solution to .NET 11 and resolve all breaking changes. This is a MIGRATION skill — use it when upgrading from .NET 10 to .NET 11, NOT for writing new programs. USE FOR: upgrading TargetFramework from net10.0 to net11.0, fixing build errors after updating the .NET 11 SDK, resolving source-breaking and behavioral changes in .NET 11 runtime, C# 15 compiler, and EF Core 11, adapting to updated minimum hardware requirements (x86-64-v2, Arm64 LSE), and updating CI/CD pipelines and Dockerfiles for .NET 11. DO NOT USE FOR: .NET Framework migrations, upgrading from .NET 9 or earlier, greenfield .NET 11 projects, or cosmetic modernization unrelated to the upgrade. NOTE: .NET 11 is in preview. Covers breaking changes through Preview 3.

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。

[Extended thinking: This workflow implements a sophisticated debugging and resolution pipeline that leverages AI-assisted debugging tools and observability platforms to systematically diagnose and res

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。

Fix MSBuild targets that generate files during the build but those files are missing from compilation or output. Only activate in MSBuild/.NET build context. USE FOR: generated source files not compiling (CS0246 for a type that should exist), custom build tasks that create files but they are invisible to subsequent targets, globs not capturing build-generated files because they expand at evaluation time before execution creates them, ensuring generated files are cleaned by the Clean target. Covers correct BeforeTargets timing (CoreCompile, BeforeBuild, AssignTargetPaths), adding to Compile/FileWrites item groups, using $(IntermediateOutputPath) instead of hardcoded obj/ paths. DO NOT USE FOR: C# source generators that already work via the Roslyn pipeline, T4 design-time generation that runs in Visual Studio, non-MSBuild build systems. INVOKES: no tools — pure knowledge skill.

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。

Guide for MSBuild extensibility: CustomBefore/CustomAfter hooks, wildcard imports with alphabetic ordering, import gating with control properties, NuGet package build extension layout (build/buildTransitive), and the MicrosoftCommonPropsHasBeenImported guard. Only activate in MSBuild/.NET build context. USE FOR: diagnosing and fixing MSBuild import and hook patterns, reviewing and fixing extension point anti-patterns in Directory.Build files, fixing missing Exists() guards on imports that break fresh clones, fixing NuGet package hooks being silently dropped instead of appended, making build targets extensible for other projects, injecting custom logic into the build pipeline, creating NuGet packages that extend the build, conditionally disabling imports. DO NOT USE FOR: target authoring patterns (use target-authoring), props vs targets placement (use directory-build-organization), general anti-patterns (use msbuild-antipatterns), non-MSBuild build 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。

Prometheus, Grafana, logging, alerting, and data pipeline observability

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。

Canonical patterns for writing custom MSBuild targets. Only activate in MSBuild/.NET build context. USE FOR: diagnosing and fixing custom target authoring anti-patterns, reviewing MSBuild target definitions for correctness, diagnosing broken SDK target chains across files (e.g., Directory.Build.targets silently redefining SDK targets), fixing targets that replace CompileDependsOn instead of extending it with $(CompileDependsOn), fixing query targets that return stale results due to Outputs vs Returns misuse, fixing missing Inputs/Outputs causing unnecessary rebuilds, fixing missing FileWrites registration. Covers DependsOnTargets vs BeforeTargets vs AfterTargets, the Build→CoreBuild three-level pattern, hooking into the build pipeline, the $(XxxDependsOn) chain-extension pattern. DO NOT USE FOR: incremental build tuning (use incremental-build), parallelization (use build-parallelism), general anti-patterns (use msbuild-antipatterns), non-MSBuild build 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。

Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and 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。