Guide for interpreting ResolveProjectReferences time in MSBuild performance summaries. Only activate in MSBuild/.NET build context. Activate when ResolveProjectReferences appears as the most expensive target and developers are trying to optimize it directly. Explains that the reported time includes wait time for dependent project builds and is misleading. Guides users to focus on task self-time instead. Do not activate for general build performance -- use build-perf-diagnostics instead.
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。
Prevent misguided optimization of ResolveProjectReferences by explaining that its reported time is wall-clock wait time, not CPU work.
ResolveProjectReferences appears as the most expensive target in the Target Performance SummaryResolveProjectReferences directlybuild-perf-diagnostics instead)Csc, ResolveAssemblyReference)| Input | Required | Description |
|---|---|---|
| Build log or binlog | Yes | A diagnostic build log or binlog containing the Target Performance Summary |
Verify that ResolveProjectReferences appears as the top target in the Target Performance Summary. This is the misleading metric.
The reported time includes waiting for dependent projects to build while the MSBuild node is yielded (see dotnet/msbuild#3135). During this wait, the node may be doing useful work on other projects. The target itself does very little work.
Use the Task Performance Summary to identify the real bottleneck.
Use the binlog MCP server expensive_tasks tool to get task self-time rankings directly from the binlog.
dotnet msbuild build.binlog -noconlog -fl "-flp:v=diag;logfile=full.log;performancesummary"
grep "Task Performance Summary" -A 50 full.log
Focus on self-time of actual tasks:
build-perf-diagnostics skill (Section 2: Roslyn Analyzers)build-perf-diagnostics skill (Section 1: RAR)build-perf-diagnostics skill (Section 4: File I/O)build-parallelism skillResolveProjectReferences was not set as the optimization targetCsc, Copy, ResolveAssemblyReference) was identified as the true bottleneck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。