Xcode Build Benchmark logo

Xcode Build Benchmark

CommunityPopular
AvdLee
xcode-build-benchmark

Benchmark Xcode clean and incremental builds with repeatable inputs, timing summaries, and timestamped `.build-benchmark/` artifacts. Use when a developer wants a baseline, wants to compare before and after changes, asks to measure build performance, mentions build times, build duration, how long builds take, or wants to know if builds got faster or slower.

Overview

PublisherAvdLee
RepositoryXcode-Build-Optimization-Agent-Skill
Skill namexcode-build-benchmark
Stars
1.2K
Forks
48
Bundled files
4
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.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Xcode Build Benchmark 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/AvdLee/Xcode-Build-Optimization-Agent-Skill.git /tmp/Xcode-Build-Optimization-Agent-Skill
mkdir -p .claude/skills
cp -r /tmp/Xcode-Build-Optimization-Agent-Skill/skills/xcode-build-benchmark .claude/skills/xcode-build-benchmark
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Xcode Build Benchmark 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 Xcode Build Benchmark 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 Xcode Build Benchmark 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.

Xcode Build Benchmark

Use this skill to produce a repeatable Xcode build baseline before anyone tries to optimize build times.

Core Rules

  • Measure before recommending changes.
  • Capture clean and incremental builds separately.
  • Keep the command, destination, configuration, scheme, and warm-up rules consistent across runs.
  • Write a timestamped JSON artifact to .build-benchmark/.
  • Do not change project files as part of benchmarking.

Inputs To Collect

Confirm or infer:

  • workspace or project path
  • scheme
  • configuration
  • destination
  • whether the user wants simulator or device numbers
  • whether a custom DerivedData path is needed

If the project has both clean-build and incremental-build pain, benchmark both. That is the default.

Worktree Considerations

When benchmarking inside a git worktree, SPM packages with exclude: paths that reference gitignored directories (e.g., __Snapshots__) will cause xcodebuild -resolvePackageDependencies to crash. Create those missing directories before running any builds.

Default Workflow

  1. Normalize the build command and note every flag that affects caching or module reuse.
  2. Run one warm-up build if needed to validate that the command succeeds.
  3. Run 3 clean builds.
  4. If COMPILATION_CACHE_ENABLE_CACHING = YES is detected, run 3 cached clean builds. These measure clean build time with a warm compilation cache -- the realistic scenario for branch switching, pulling changes, or Clean Build Folder. The script handles this automatically by building once to warm the cache, then deleting DerivedData (but not the compilation cache) before each measured run. Pass --no-cached-clean to skip.
  5. Run 3 zero-change builds (build immediately after a successful build with no edits). This measures the fixed overhead floor: dependency computation, project description transfer, build description creation, script phases, codesigning, and validation. A zero-change build that takes more than a few seconds indicates avoidable per-build overhead. Use the default benchmark_builds.py invocation (no --touch-file flag).
  6. Optionally run 3 incremental builds with a file touch to measure a real edit-rebuild loop. Use --touch-file path/to/SomeFile.swift to touch a representative source file before each build.
  7. Save the raw results and summary into .build-benchmark/.
  8. Report medians and spread, not just the single fastest run.

Preferred Command Path

Use the shared helper when possible:

bash
python3 scripts/benchmark_builds.py \
  --workspace App.xcworkspace \
  --scheme MyApp \
  --configuration Debug \
  --destination "platform=iOS Simulator,name=iPhone 16" \
  --output-dir .build-benchmark

If you cannot use the helper script, run equivalent xcodebuild commands with -showBuildTimingSummary and preserve the raw output.

Required Output

Return:

  • clean build median, min, max
  • cached clean build median, min, max (when COMPILATION_CACHE_ENABLE_CACHING is enabled)
  • zero-change build median, min, max (fixed overhead floor)
  • incremental build median, min, max (if --touch-file was used)
  • biggest timing-summary categories
  • environment details that could affect comparisons
  • path to the saved artifact

If results are noisy, say so and recommend rerunning under calmer conditions.

When To Stop

Stop after measurement if the user only asked for benchmarking. If they want optimization guidance, hand off the artifact to the relevant specialist by reading its SKILL.md and applying its workflow to the same project context:

Additional Resources

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Xcode Build Benchmark AI skill do?

Benchmark Xcode clean and incremental builds with repeatable inputs, timing summaries, and timestamped `.build-benchmark/` artifacts. Use when a developer wants a baseline, wants to compare before and after changes, asks to measure build performance, mentions build times, build duration, how long builds take, or wants to know if builds got faster or slower.

Why use Xcode Build Benchmark on TypingMind?

Because you install it once and use it with any model. Xcode Build Benchmark 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 Xcode Build Benchmark in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AvdLee/Xcode-Build-Optimization-Agent-Skill/tree/main/skills/xcode-build-benchmark. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Xcode Build Benchmark?

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 Xcode Build Benchmark?

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

Is the Xcode Build Benchmark AI skill free?

Yes. It is published on GitHub by AvdLee 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇