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Xcode Compilation Analyzer

CommunityPopular
AvdLee
xcode-compilation-analyzer

Analyze Swift and mixed-language compile hotspots using build timing summaries and Swift frontend diagnostics, then produce a recommend-first source-level optimization plan. Use when a developer reports slow compilation, type-checking warnings, expensive clean-build compile phases, long CompileSwiftSources tasks, warn-long-function-bodies output, or wants to speed up Swift type checking.

Overview

PublisherAvdLee
RepositoryXcode-Build-Optimization-Agent-Skill
Skill namexcode-compilation-analyzer
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 Compilation Analyzer 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-compilation-analyzer .claude/skills/xcode-compilation-analyzer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Xcode Compilation Analyzer 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 Compilation Analyzer 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 Compilation Analyzer 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 Compilation Analyzer

Use this skill when compile time, not just general project configuration, looks like the bottleneck.

Core Rules

  • Start from evidence, ideally a recent .build-benchmark/ artifact or raw timing-summary output.
  • Prefer analysis-only compiler flags over persistent project edits during investigation.
  • Rank findings by expected wall-clock impact, not cumulative compile-time impact. When compile tasks are heavily parallelized (sum of compile categories >> wall-clock median), note that fixing individual hotspots may improve parallel efficiency without reducing build wait time.
  • When the evidence points to parallelized work rather than serial bottlenecks, label recommendations as "Reduces compiler workload (parallel)" rather than "Reduces build time."
  • Do not edit source or build settings without explicit developer approval.

What To Inspect

  • Build Timing Summary output from clean and incremental builds
  • long-running CompileSwiftSources or per-file compilation tasks
  • SwiftEmitModule time -- can reach 60s+ after a single-line change in large modules; if it dominates incremental builds, the module is likely too large or macro-heavy
  • Planning Swift module time -- if this category is disproportionately large in incremental builds (up to 30s per module), it signals unexpected input invalidation or macro-related rebuild cascading
  • ad hoc runs with:
    • -Xfrontend -warn-long-expression-type-checking=<ms>
    • -Xfrontend -warn-long-function-bodies=<ms>
  • deeper diagnostic flags for thorough investigation:
    • -Xfrontend -debug-time-compilation -- per-file compile times to rank the slowest files
    • -Xfrontend -debug-time-function-bodies -- per-function compile times (unfiltered, complements the threshold-based warning flags)
    • -Xswiftc -driver-time-compilation -- driver-level timing to isolate driver overhead
    • -Xfrontend -stats-output-dir <path> -- detailed compiler statistics (JSON) per compilation unit for root-cause analysis
  • mixed Swift and Objective-C surfaces that increase bridging work

Analysis Workflow

  1. Identify whether the main issue is broad compilation volume or a few extreme hotspots.
  2. Parse timing-summary categories and rank the biggest compile contributors.
  3. Run the diagnostics script to surface type-checking hotspots:
    bash
    python3 scripts/diagnose_compilation.py \
      --project App.xcodeproj \
      --scheme MyApp \
      --configuration Debug \
      --destination "platform=iOS Simulator,name=iPhone 16" \
      --threshold 100 \
      --output-dir .build-benchmark
    This produces a ranked list of functions and expressions that exceed the millisecond threshold. Use the diagnostics artifact alongside source inspection to focus on the most expensive files first.
  4. Map the evidence to a concrete recommendation list.
  5. Separate code-level suggestions from project-level or module-level suggestions.

Apple-Derived Checks

Look for these patterns first:

  • missing explicit type information in expensive expressions
  • complex chained or nested expressions that are hard to type-check
  • delegate properties typed as AnyObject instead of a concrete protocol
  • oversized Objective-C bridging headers or generated Swift-to-Objective-C surfaces
  • header imports that skip framework qualification and miss module-cache reuse
  • classes missing final that are never subclassed
  • overly broad access control (public/open) on internal-only symbols
  • monolithic SwiftUI body properties that should be decomposed into subviews
  • long method chains or closures without intermediate type annotations

Reporting Format

For each recommendation, include:

  • observed evidence
  • likely affected file or module
  • expected wait-time impact (e.g. "Expected to reduce your clean build by ~2s" or "Reduces parallel compile work but unlikely to reduce build wait time")
  • confidence
  • whether approval is required before applying it

If the evidence points to project configuration instead of source, hand off to xcode-project-analyzer by reading its SKILL.md and applying its workflow to the same project context.

Preferred Tactics

  • Suggest ad hoc flag injection through the build command before recommending persistent build-setting changes.
  • Prefer narrowing giant view builders, closures, or result-builder expressions into smaller typed units.
  • Recommend explicit imports and protocol typing when they reduce compiler search space.
  • Call out when mixed-language boundaries are the real issue rather than Swift syntax alone.

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 Compilation Analyzer AI skill do?

Analyze Swift and mixed-language compile hotspots using build timing summaries and Swift frontend diagnostics, then produce a recommend-first source-level optimization plan. Use when a developer reports slow compilation, type-checking warnings, expensive clean-build compile phases, long CompileSwiftSources tasks, warn-long-function-bodies output, or wants to speed up Swift type checking.

Why use Xcode Compilation Analyzer on TypingMind?

Because you install it once and use it with any model. Xcode Compilation Analyzer 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 Compilation Analyzer 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-compilation-analyzer. 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 Compilation Analyzer?

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 Compilation Analyzer?

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

Is the Xcode Compilation Analyzer 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.

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