Guide for using MSBuild Server to improve CLI build performance. Only activate in MSBuild/.NET build context. Activate when developers report slow incremental builds from the command line, or when CLI builds are noticeably slower than IDE builds. Covers MSBUILDUSESERVER=1 environment variable for persistent server-based caching. Do not activate for IDE-based builds (Visual Studio already uses a long-lived process).
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Use the MSBuild Server to cache evaluation results across CLI builds, matching the performance advantage Visual Studio gets from its long-lived MSBuild process.
dotnet build) are slower than expected| Input | Required | Description |
|---|---|---|
| Shell context | No | The shell where the environment variable will be set (bash, PowerShell, or Windows persistent) |
Verify the developer is building from the command line (dotnet build), not from Visual Studio or another IDE. The MSBuild Server provides no benefit inside an IDE.
# Bash / CI
export MSBUILDUSESERVER=1
# PowerShell
$env:MSBUILDUSESERVER = "1"
# Windows (persistent)
setx MSBUILDUSESERVER 1
Run two sequential builds of the same project and compare times:
dotnet build -- server starts, no cache benefitdotnet build -- should be noticeably fasterThe most noticeable improvement is in repos with many projects or complex Directory.Build.props chains.
MSBUILDUSESERVER=1 is set in the shelldotnet build-server shutdown followed by a rebuild confirms the server restarts cleanly| Pitfall | Solution |
|---|---|
| Expecting improvement in Visual Studio | VS already uses long-lived MSBuild nodes; the server adds no benefit |
| Build correctness issues after enabling | Run dotnet build-server shutdown to reset; if issues persist, disable the server |
| Server process using unexpected memory | The server persists in background; shut down with dotnet build-server shutdown when idle |
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
12-agent academic paper writing pipeline. 10 modes (full/plan/outline/revision/revision-coach/abstract/lit-review/format-convert/citation-check/disclosure). 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, AI disclosure, 寫論文, 學術論文, 引導我寫論文, 審查意見.
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-perspective academic paper review with dynamic reviewer personas. Simulates 5 independent reviewers (EIC + 3 peer reviewers + Devil's Advocate) with field-specific expertise. Supports full review, re-review (verification), quick assessment, methodology focus, Socratic guided, and calibration modes. Triggers on: review paper, peer review, manuscript review, referee report, review my paper, critique paper, simulate review, editorial review, calibrate reviewer, reviewer calibration, measure reviewer accuracy.
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。
Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory integrity verification, two-stage peer review, and reproducible quality gates. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow.
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。
Create, iterate, and scale paid ad creative for Google Ads, Meta, LinkedIn, TikTok, and similar platforms. Use when generating headlines, descriptions, primary text, or large sets of ad variations for testing and performance 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。
This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.
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。
AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.
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。