xcode-compilation-analyzer▌
avdlee/xcode-build-optimization-agent-skill · updated Apr 8, 2026
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Use this skill when compile time, not just general project configuration, looks like the bottleneck.
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 Summaryoutput from clean and incremental builds- long-running
CompileSwiftSourcesor per-file compilation tasks SwiftEmitModuletime -- 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 moduletime -- 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:
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.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 - 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
AnyObjectinstead 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
finalthat are never subclassed - overly broad access control (
public/open) on internal-only symbols - monolithic SwiftUI
bodyproperties 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
How to use xcode-compilation-analyzer on Cursor
AI-first code editor with Composer
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 xcode-compilation-analyzer
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches xcode-compilation-analyzer from GitHub repository avdlee/xcode-build-optimization-agent-skill and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate xcode-compilation-analyzer. Access the skill through slash commands (e.g., /xcode-compilation-analyzer) 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
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★26 reviews- ★★★★★Harper Desai· Dec 24, 2024
xcode-compilation-analyzer has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Pratham Ware· Dec 20, 2024
I recommend xcode-compilation-analyzer for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Zaid Ghosh· Nov 27, 2024
Registry listing for xcode-compilation-analyzer matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Fatima Gonzalez· Nov 15, 2024
xcode-compilation-analyzer reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Sakshi Patil· Nov 11, 2024
Useful defaults in xcode-compilation-analyzer — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Diya Johnson· Oct 18, 2024
Keeps context tight: xcode-compilation-analyzer is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Benjamin Bansal· Oct 6, 2024
I recommend xcode-compilation-analyzer for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Chaitanya Patil· Oct 2, 2024
xcode-compilation-analyzer has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Piyush G· Sep 9, 2024
Solid pick for teams standardizing on skills: xcode-compilation-analyzer is focused, and the summary matches what you get after install.
- ★★★★★Noah Sethi· Sep 5, 2024
xcode-compilation-analyzer has been reliable in day-to-day use. Documentation quality is above average for community skills.
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