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.
npx skills add https://github.com/AvdLee/Xcode-Build-Optimization-Agent-Skill --skill xcode-compilation-analyzer
Use this skill when compile time, not just general project configuration, looks like the bottleneck.
.build-benchmark/ artifact or raw timing-summary output.Build Timing Summary output from clean and incremental buildsCompileSwiftSources or per-file compilation tasksSwiftEmitModule 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-heavyPlanning 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-Xfrontend -warn-long-expression-type-checking=<ms>-Xfrontend -warn-long-function-bodies=<ms>-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 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.
Look for these patterns first:
AnyObject instead of a concrete protocolfinal that are never subclassedpublic/open) on internal-only symbolsbody properties that should be decomposed into subviewsFor each recommendation, include:
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.
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.
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.
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.
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.
Retrieve and display GitHub Copilot usage metrics for organizations and enterprises using the GitHub CLI and REST API.
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.
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.
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.
Take avdlee/xcode-compilation-analyzer from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
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.