spm-build-analysis

avdlee/xcode-build-optimization-agent-skill · updated Apr 8, 2026

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$npx skills add https://github.com/avdlee/xcode-build-optimization-agent-skill --skill spm-build-analysis
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summary

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

skill.md

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:
    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

how to use spm-build-analysis

How to use spm-build-analysis on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add spm-build-analysis
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/avdlee/xcode-build-optimization-agent-skill --skill spm-build-analysis

The skills CLI fetches spm-build-analysis from GitHub repository avdlee/xcode-build-optimization-agent-skill and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/spm-build-analysis

Reload or restart Cursor to activate spm-build-analysis. Access the skill through slash commands (e.g., /spm-build-analysis) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.772 reviews
  • Kabir Kim· Dec 20, 2024

    spm-build-analysis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Chaitanya Patil· Dec 16, 2024

    spm-build-analysis has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Zara Shah· Dec 16, 2024

    Useful defaults in spm-build-analysis — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Zara Taylor· Dec 12, 2024

    Useful defaults in spm-build-analysis — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Omar White· Dec 12, 2024

    I recommend spm-build-analysis for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Soo Choi· Dec 8, 2024

    We added spm-build-analysis from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Isabella Reddy· Nov 27, 2024

    Useful defaults in spm-build-analysis — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Kabir Flores· Nov 11, 2024

    Keeps context tight: spm-build-analysis is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Piyush G· Nov 7, 2024

    Solid pick for teams standardizing on skills: spm-build-analysis is focused, and the summary matches what you get after install.

  • Lucas Martin· Nov 3, 2024

    We added spm-build-analysis from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

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