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Build Parallelism

OrganizationPopular
dotnet
build-parallelism

Diagnose and fix under-parallelized MSBuild builds. USE WHEN a multi-project solution build is slower than expected, doesn't speed up when you add cores, pegs a single core while others idle, or you want to know why `-m` isn't helping. Note: `/maxcpucount` default is 1 (sequential) — always pass `-m` for parallel builds. Covers finding the critical path (longest serial ProjectReference chain), graph build (`/graph`), BuildInParallel, and solution filters (`.slnf`). DO NOT USE FOR: single-project builds, incremental issues (use incremental-build), compilation slowness inside one project (use build-perf-diagnostics), non-MSBuild build systems.

Overview

Publisherdotnet
Repositoryskills
Skill namebuild-parallelism
Stars
5.4K
Forks
416
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by dotnet on GitHub. Read the source before you install it.

Installation

Install the Build Parallelism AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/dotnet/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/dotnet-msbuild/skills/build-parallelism .claude/skills/build-parallelism
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Build Parallelism in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Build Parallelism on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Build Parallelism is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Diagnose a slow parallel build (start here)

Work this checklist in order — it targets the usual root cause (a serial dependency chain that no number of cores can parallelize):

  1. Confirm parallelism is even on. Rebuild with dotnet build -m /bl:{} (PowerShell: dotnet build -m -bl:{{}}). -m with no number uses all logical processors; without -m MSBuild runs a single node (sequential).
  2. Find the critical path. From the binlog, read per-project timings and the node timeline. If total build time ≈ the sum of the projects on one dependency chain, that chain — not CPU count — is the bottleneck.
  3. Name the chain explicitly, e.g. Core → Api → Web → Tests. A long serial chain stays serial no matter how large -m is, because each project waits on its predecessor.
  4. Look for unnecessary ProjectReference edges that lengthen the chain — a reference that only needs build order (not the output assembly), or one that could be a PackageReference, forces serialization it doesn't need.
  5. Recommend flattening: break false dependencies so independent projects build concurrently, and consider /graph for better scheduling.

MSBuild Parallelism Model

  • /maxcpucount (or -m): number of worker nodes (processes)
  • Default: 1 node (sequential!). Always use -m for parallel builds
  • Recommended: -m without a number = use all logical processors
  • Each node builds one project at a time
  • Projects are scheduled based on dependency graph

Project Dependency Graph

  • MSBuild builds projects in dependency order (topological sort)
  • Critical path: longest chain of dependent projects determines minimum build time
  • Bottleneck: if project A depends on B, C, D and B takes 60s while C and D take 5s, B is the bottleneck
  • Diagnosis: replay binlog to diagnostic log with performancesummary and check Project Performance Summary — shows per-project time; grep for node.*assigned to check scheduling
  • Wide graphs (many independent projects) parallelize well; deep graphs (long chains) don't

Graph Build Mode (/graph)

  • dotnet build /graph or msbuild /graph
  • What it changes: MSBuild constructs the full project dependency graph BEFORE building
  • Benefits: better scheduling, avoids redundant evaluations, enables isolated builds
  • Limitations: all projects must use <ProjectReference> (no programmatic MSBuild task references)
  • When to use: large solutions with many projects, CI builds
  • When NOT to use: projects that dynamically discover references at build time

Optimizing Project References

  • Reduce unnecessary <ProjectReference> — each adds to the dependency chain
  • Use <ProjectReference ... SkipGetTargetFrameworkProperties="true"> to avoid extra evaluations
  • <ProjectReference ... ReferenceOutputAssembly="false"> for build-order-only dependencies
  • Consider if a ProjectReference should be a PackageReference instead (pre-built NuGet)
  • Use solution filters (.slnf) to build subsets of the solution

BuildInParallel

  • <MSBuild Projects="@(ProjectsToBuild)" BuildInParallel="true" /> in custom targets
  • Without BuildInParallel="true", MSBuild task batches projects sequentially
  • Ensure /maxcpucount > 1 for this to have effect

Multi-threaded MSBuild Tasks

  • Individual tasks can run multi-threaded within a single project build
  • Tasks implementing IMultiThreadableTask can run on multiple threads
  • Tasks must declare thread-safety via [MSBuildMultiThreadableTask]

Analyzing Parallelism with Binlog

Primary: binlog MCP (preferred)

Use the binlog MCP server (Microsoft.AITools.BinlogMcp, exposed under the binlog MCP namespace):

  1. Use expensive_projects tool → find the slowest projects and compare individual vs total build time
  2. Use expensive_targets tool → find bottleneck targets
  3. Use project_target_times tool → drill into a specific project's target-level timing
  4. Ideal: build time should be much less than sum of project times (parallelism)
  5. If build time ≈ sum of project times: too many serial dependencies, or one slow project blocking others

Fallback: text-log replay (when MCP is unavailable)

Step-by-step:

  1. Replay the binlog: dotnet msbuild build.binlog -noconlog -fl -flp:v=diag;logfile=full.log;performancesummary
  2. Check Project Performance Summary at the end of full.log
  3. Ideal: build time should be much less than sum of project times (parallelism)
  4. If build time ≈ sum of project times: too many serial dependencies, or one slow project blocking others
  5. grep 'Target Performance Summary' -A 30 full.log → find the bottleneck targets
  6. Consider splitting large projects or optimizing the critical path

CI/CD Parallelism Tips

  • Use -m in CI (many CI runners have multiple cores)
  • Consider splitting solution into build stages for extreme parallelism
  • Use build caching (NuGet lock files, deterministic builds) to avoid rebuilding unchanged projects
  • dotnet build /graph works well with structured CI pipelines

Frequently asked questions

What does the Build Parallelism AI skill do?

Diagnose and fix under-parallelized MSBuild builds. USE WHEN a multi-project solution build is slower than expected, doesn't speed up when you add cores, pegs a single core while others idle, or you want to know why `-m` isn't helping. Note: `/maxcpucount` default is 1 (sequential) — always pass `-m` for parallel builds. Covers finding the critical path (longest serial ProjectReference chain), graph build (`/graph`), BuildInParallel, and solution filters (`.slnf`). DO NOT USE FOR: single-project builds, incremental issues (use incremental-build), compilation slowness inside one project (use...

Why use Build Parallelism on TypingMind?

Because you install it once and use it with any model. Build Parallelism is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Build Parallelism in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/dotnet/skills/tree/main/plugins/dotnet-msbuild/skills/build-parallelism. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Build Parallelism?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Build Parallelism?

As many as you like. As long as a model supports skills, you can use Build Parallelism with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Build Parallelism AI skill free?

Yes. It is published on GitHub by dotnet under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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