Swiftui Performance Audit V2

SwiftUI Performance Audit

Overview

This public intake copy packages plugins/antigravity-awesome-skills/skills/swiftui-performance-audit from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

SwiftUI Performance Audit

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: 1. Intake, 2. Code-First Review, 3. Guide the User to Profile, 4. Analyze and Diagnose, 5. Remediate, Outputs.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• When the user reports slow rendering, janky scrolling, layout thrash, or high CPU in SwiftUI.
• When you need a code-first audit plus Instruments guidance if profiling evidence is required.
• Use when the request clearly matches the imported source intent: Audit SwiftUI performance issues from code review and profiling evidence.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
• Use when provenance needs to stay visible in the answer, PR, or review packet.
• Use when copied upstream references, examples, or scripts materially improve the answer.

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Classify the symptom: slow rendering, janky scrolling, high CPU, memory growth, hangs, or excessive view updates.
2. If code is available, start with a code-first review using references/code-smells.md.
3. If code is not available, ask for the smallest useful slice: target view, data flow, reproduction steps, and deployment target.
4. If code review is inconclusive or runtime evidence is required, guide the user through profiling with references/profiling-intake.md.
5. Summarize likely causes, evidence, remediation, and validation steps using references/report-template.md.
6. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
7. Read the overview and provenance files before loading any copied upstream support files.

Imported Workflow Notes

Imported: Workflow

1. Classify the symptom: slow rendering, janky scrolling, high CPU, memory growth, hangs, or excessive view updates.
2. If code is available, start with a code-first review using references/code-smells.md.
3. If code is not available, ask for the smallest useful slice: target view, data flow, reproduction steps, and deployment target.
4. If code review is inconclusive or runtime evidence is required, guide the user through profiling with references/profiling-intake.md.
5. Summarize likely causes, evidence, remediation, and validation steps using references/report-template.md.

Imported: 6. Verify

Ask the user to re-run the same capture and compare with baseline metrics. Summarize the delta (CPU, frame drops, memory peak) if provided.

Imported: 1. Intake

Collect:

• Target view or feature code.
• Symptoms and exact reproduction steps.
• Data flow: @State, @Binding, environment dependencies, and observable models.
• Whether the issue shows up on device or simulator, and whether it was observed in Debug or Release.

Ask the user to classify the issue if possible:

• CPU spike or battery drain
• Janky scrolling or dropped frames
• High memory or image pressure
• Hangs or unresponsive interactions
• Excessive or unexpectedly broad view updates

For the full profiling intake checklist, read references/profiling-intake.md.

Examples

Example 1: Ask for the upstream workflow directly

Use @swiftui-performance-audit-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @swiftui-performance-audit-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @swiftui-performance-audit-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @swiftui-performance-audit-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Imported Usage Notes

Imported: Quick start

Use this skill to diagnose SwiftUI performance issues from code first, then request profiling evidence when code review alone cannot explain the symptoms.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills/skills/swiftui-performance-audit, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

• code-smells.md
• demystify-swiftui-performance-wwdc23.md
• optimizing-swiftui-performance-instruments.md
• profiling-intake.md
• openai.yaml
• openai.yaml

Imported Reference Notes

Imported: References

• Profiling intake and collection checklist: references/profiling-intake.md
• Common code smells and remediation patterns: references/code-smells.md
• Audit output template: references/report-template.md
• Add Apple documentation and WWDC resources under references/ as they are supplied by the user.
• Optimizing SwiftUI performance with Instruments: references/optimizing-swiftui-performance-instruments.md
• Understanding and improving SwiftUI performance: references/understanding-improving-swiftui-performance.md
• Understanding hangs in your app: references/understanding-hangs-in-your-app.md
• Demystify SwiftUI performance (WWDC23): references/demystify-swiftui-performance-wwdc23.md

Imported: 2. Code-First Review

Focus on:

• Invalidation storms from broad observation or environment reads.
• Unstable identity in lists and ForEach.
• Heavy derived work in body or view builders.
• Layout thrash from complex hierarchies, GeometryReader, or preference chains.
• Large image decode or resize work on the main thread.
• Animation or transition work applied too broadly.

Use references/code-smells.md for the detailed smell catalog and fix guidance.

Provide:

• Likely root causes with code references.
• Suggested fixes and refactors.
• If needed, a minimal repro or instrumentation suggestion.

Imported: 3. Guide the User to Profile

If code review does not explain the issue, ask for runtime evidence:

• A trace export or screenshots of the SwiftUI timeline and Time Profiler call tree.
• Device/OS/build configuration.
• The exact interaction being profiled.
• Before/after metrics if the user is comparing a change.

Use references/profiling-intake.md for the exact checklist and collection steps.

Imported: 4. Analyze and Diagnose

• Map the evidence to the most likely category: invalidation, identity churn, layout thrash, main-thread work, image cost, or animation cost.
• Prioritize problems by impact, not by how easy they are to explain.
• Distinguish code-level suspicion from trace-backed evidence.
• Call out when profiling is still insufficient and what additional evidence would reduce uncertainty.

Imported: 5. Remediate

Apply targeted fixes:

• Narrow state scope and reduce broad observation fan-out.
• Stabilize identities for ForEach and lists.
• Move heavy work out of body into derived state updated from inputs, model-layer precomputation, memoized helpers, or background preprocessing. Use @State only for view-owned state, not as an ad hoc cache for arbitrary computation.
• Use equatable() only when equality is cheaper than recomputing the subtree and the inputs are truly value-semantic.
• Downsample images before rendering.
• Reduce layout complexity or use fixed sizing where possible.

Use references/code-smells.md for examples, Observation-specific fan-out guidance, and remediation patterns.

Imported: Outputs

Provide:

• A short metrics table (before/after if available).
• Top issues (ordered by impact).
• Proposed fixes with estimated effort.

Use references/report-template.md when formatting the final audit.

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.