mcpbeat Sign in

Xcode Compilation Analyzer Agent Skill

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.

8k tokens
context cost
the whole folder, loaded on every use
5
files
ships runnable scripts
0
copies elsewhere
how many repositories repackaged it
1193
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/AvdLee/Xcode-Build-Optimization-Agent-Skill --skill xcode-compilation-analyzer

The instruction itself

8 sections, as written by the author

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

  • Identify whether the main issue is broad compilation volume or a few extreme hotspots.
  • Parse timing-summary categories and rank the biggest compile contributors.
  • Run the diagnostics script to surface type-checking hotspots:
   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.

  • Map the evidence to a concrete recommendation list.
  • 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

  • For the detailed audit checklist, see references/code-compilation-checks.md
  • For the shared recommendation structure, see references/recommendation-format.md
  • For source citations, see references/build-optimization-sources.md

Other skills for the same job

different authors, same section of the catalogue
D3 Viz
by chrisvoncsefalvay
×3

Creating interactive data visualisations using d3.js. This skill should be used when creating custom charts, graphs, network diagrams, geographic visualisations, or any complex SVG-based data visualisation that requires fine-grained control over visual elements, transitions, or interactions. Use this for bespoke visualisations beyond standard charting libraries, whether in React, Vue, Svelte, vanilla JavaScript, or any other environment.

20k tokens
Astropy
by christophacham
×3

Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.

16k tokens
Instrument Data To Allotrope
by anthropics
vendor ×2

Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.

33k tokens scripts
Qutip
by ComeOnOliver
×2

Quantum mechanics simulations and analysis using QuTiP (Quantum Toolbox in Python). Use when working with quantum systems including: (1) quantum states (kets, bras, density matrices), (2) quantum operators and gates, (3) time evolution and dynamics (Schrödinger, master equations, Monte Carlo), (4) open quantum systems with dissipation, (5) quantum measurements and entanglement, (6) visualization (Bloch sphere, Wigner functions), (7) steady states and correlation functions, or (8) advanced methods (Floquet theory, HEOM, stochastic solvers). Handles both closed and open quantum systems across various domains including quantum optics, quantum computing, and condensed matter physics.

27k tokens
Copilot Usage Metrics
by github
vendor ×1

Retrieve and display GitHub Copilot usage metrics for organizations and enterprises using the GitHub CLI and REST API.

1k tokens scripts
Mentoring Juniors
by github
vendor ×1

Socratic mentoring for junior developers and AI newcomers. Guides through questions, never answers. Triggers: "help me understand", "explain this code", "I''m stuck", "Im stuck", "I''m confused", "Im confused", "I don''t understand", "I dont understand", "can you teach me", "teach me", "mentor me", "guide me", "what does this error mean", "why doesn''t this work", "why does not this work", "I''m a beginner", "Im a beginner", "I''m learning", "Im learning", "I''m new to this", "Im new to this", "walk me through", "how does this work", "what''s wrong with my code", "what''s wrong", "can you break this down", "ELI5", "step by step", "where do I start", "what am I missing", "newbie here", "junior dev", "first time using", "how do I", "what is", "is this right", "not sure", "need help", "struggling", "show me", "help me debug", "best practice", "too complex", "overwhelmed", "lost", "debug this", "/socratic", "/hint", "/concept", "/pseudocode". Progressive clue systems, teaching techniques, and success metrics.

4k tokens
Astropy
by K-Dense-AI
×1

Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.

18k tokens
Polars
by K-Dense-AI
×1

High-performance DataFrame library for Python ETL, analytics, and pandas migration. Use for expression-based data manipulation with lazy query optimization, parallel execution, streaming out-of-core processing, Arrow interoperability, and optional GPU execution.

20k tokens

How to use it

Copy the folder

Take avdlee/xcode-compilation-analyzer from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.