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Spm Build Analysis

CommunityPopular
AvdLee
spm-build-analysis

Analyze Swift Package Manager dependencies, package plugins, module variants, and CI-oriented build overhead that slow Xcode builds. Use when a developer suspects packages, plugins, or dependency graph shape are hurting clean or incremental build performance, mentions SPM slowness, package resolution time, build plugin overhead, duplicate module builds from configuration drift, circular dependencies between modules, oversized modules needing splitting, or modularization best practices.

Overview

PublisherAvdLee
RepositoryXcode-Build-Optimization-Agent-Skill
Skill namespm-build-analysis
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 Spm Build Analysis 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/spm-build-analysis .claude/skills/spm-build-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Spm Build Analysis 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 Spm Build Analysis 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 Spm Build Analysis 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.

SPM Build Analysis

Use this skill when package structure, plugins, or dependency configuration are likely contributing to slow Xcode builds.

Core Rules

  • Treat package analysis as evidence gathering first, not a mandate to replace dependencies.
  • Separate package-graph issues from project-setting issues.
  • Do not rewrite package manifests or dependency sources without explicit approval.

What To Inspect

  • Package.swift and Package.resolved
  • local packages vs remote packages
  • package plugin and build-tool usage
  • binary target footprint
  • dependency layering, repeated imports, and potential cycles
  • build logs or timing summaries that show package-related work

Verification Before Recommending

Before including any local package in a recommendation, verify that it is actually part of the project's dependency graph. A Vendor/ directory may contain packages that are not linked to any target.

  • Check project.pbxproj for XCLocalSwiftPackageReference entries that reference the package path.
  • Check XCSwiftPackageProductDependency entries to confirm the package's product is linked to at least one target.
  • If a local package exists on disk but is not referenced in the project, do not include it in build-time recommendations.

When recommending version pins for branch-tracked dependencies:

  • Use the helper script to scan all branch-pinned dependencies at once:
    bash
    python3 scripts/check_spm_pins.py --project App.xcodeproj
    This checks git ls-remote --tags for each branch-pinned package and reports which have tags available for pinning.
  • If no tags exist, recommend pinning to a specific commit revision hash for determinism instead.
  • Note which packages are branch-pinned because the upstream simply has no tags, versus packages that have tags but are intentionally tracking a branch.

Focus Areas

  • package graph shape and how much work changes trigger downstream
  • plugin overhead during local development and CI
  • checkout or fetch cost signals that show up in clean environments
  • configuration drift that forces duplicate module builds
  • risks from package targets that use different macros or options while sharing dependencies
  • dependency direction violations (features depending on each other instead of shared lower layers)
  • circular dependencies between modules (extract shared contracts into a protocol module)
  • oversized modules (200+ files) that widen incremental rebuild scope
  • umbrella modules using @_exported import that create hidden dependency chains
  • missing interface/implementation separation that blocks build parallelism
  • test targets depending on the app target instead of the module under test
  • Swift macro rebuild cascading: heavy use of Swift macros (e.g., TCA, swift-syntax-based libraries) can cause a trivial source change to cascade into near-full rebuilds because macro expansion invalidates downstream modules
  • swift-syntax building universally (all architectures) when no prebuilt binary is available, adding significant clean-build overhead
  • multi-platform build multiplication: adding a secondary platform target (e.g., watchOS) can cause shared SPM packages to build multiple times (e.g., iOS arm64, iOS x86_64, watchOS arm64), multiplying SwiftCompile, SwiftEmitModule, and ScanDependencies tasks

Modular SDK Migration Caveat

Migrating a dependency from a monolithic target to a modular multi-target SDK (e.g., replacing one umbrella library with separate Core, RUM, Logs, Trace modules) does not automatically reduce build time. Modular targets increase the number of SwiftCompile, SwiftEmitModule, and ScanDependencies tasks because each target must be compiled, scanned, and emit its module independently. The build-time trade-off depends on the project's parallelism headroom and how many of the modular targets are actually needed.

When considering a modular SDK migration:

  • Compare the total SwiftCompile task count before and after.
  • Benchmark both configurations before recommending the migration for build speed.
  • If the motivation is API surface reduction (importing only what you use), note that build time may stay flat or increase while import hygiene improves.
  • Only recommend modular SDK migration for build speed when the project currently compiles large portions of the monolithic SDK that it does not use, and the modular alternative lets it skip those unused portions entirely.

Explicit Module Dependency Angle

When the same module appears multiple times in timing output, investigate whether different package or target options are forcing extra module variants. Uniform options often matter more than shaving a small amount of source code.

Reporting Format

For each finding, include:

  • evidence
  • affected package or plugin
  • likely clean-build vs incremental-build impact
  • CI impact if relevant
  • estimated impact
  • approval requirement

If the main problem is not package-related, hand off to xcode-project-analyzer or xcode-compilation-analyzer by reading the target skill's 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 Spm Build Analysis AI skill do?

Analyze Swift Package Manager dependencies, package plugins, module variants, and CI-oriented build overhead that slow Xcode builds. Use when a developer suspects packages, plugins, or dependency graph shape are hurting clean or incremental build performance, mentions SPM slowness, package resolution time, build plugin overhead, duplicate module builds from configuration drift, circular dependencies between modules, oversized modules needing splitting, or modularization best practices.

Why use Spm Build Analysis on TypingMind?

Because you install it once and use it with any model. Spm Build Analysis 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 Spm Build Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AvdLee/Xcode-Build-Optimization-Agent-Skill/tree/main/skills/spm-build-analysis. 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 Spm Build Analysis?

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 Spm Build Analysis?

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

Is the Spm Build Analysis 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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