Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add patrickserrano/lacquer --skill native-app-profilinggit clone --depth 1 https://github.com/patrickserrano/lacquerWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/patrickserrano/lacquer/native-app-profiling)<a href="https://agentmods.dev/skills/patrickserrano/lacquer/native-app-profiling"><img src="https://agentmods.dev/badge/skills/patrickserrano/lacquer/native-app-profiling.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00047 | $0.00729 |
| Opus 5 | $0.00023 | $0.00365 |
| Sonnet 5 | $0.00009 | $0.00146 |
| Haiku 4.5 | $0.00005 | $0.00073 |
Grade A, and why
native-app-profiling scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 28 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Native App Performance Profiling
This is a redirect skill. This lacquer's Instruments trace recording and analysis workflow lives in swiftui-expert-skill — see its "Record a new Instruments trace" and "Trace-driven improvement" task-workflow sections, backed by scripts/record_trace.py and scripts/analyze_trace.py and documented in full in references/trace-recording.md and references/trace-analysis.md. That workflow already covers everything this skill used to teach manually: template selection by device kind, attach/launch/stop-file recording modes, and analysis with automatic symbolication, window scoping, main-thread coverage (main_running_coverage_pct), and SwiftUI cause-graph fan-in (--fanin-for).
Do not use raw xcrun xctrace, atos, vmmap, or pgrep for this. FlowDeck's ban on raw Apple CLI tools (xcodebuild, xcrun, simctl, devicectl) extends to Instruments tooling in this lacquer for the same reason — record_trace.py/analyze_trace.py wrap xctrace and handle symbolication automatically, so the manual atos/vmmap load-address dance this skill used to document is no longer part of the workflow.
When this applies
Any request to profile CPU usage, find performance hotspots, or diagnose hangs/hitches in a native iOS/macOS app — whether or not the code under investigation is SwiftUI. swiftui-expert-skill's trace workflow is not SwiftUI-specific for recording or the Time Profiler/Hangs/Animation Hitches lanes: only the swiftui and swiftui-causes analysis lanes are SwiftUI-specific, and they simply report available: false on non-SwiftUI code paths.
Gotchas not covered elsewhere
These are the two items from the old manual workflow that remain relevant and aren't already documented in swiftui-expert-skill's trace references:
- Idle time produces empty data. Trigger the slow code path during the recording window — profiling an idle app yields empty or low-signal traces regardless of capture duration.
- Instruments permissions.
xctrace(wrapped byrecord_trace.py) may prompt for Developer Tools access or Full Disk Access on first use on a given machine, or require elevated privileges for certain system-wide captures. This is separate from the per-device signing/trust failure mode already documented inreferences/trace-recording.md.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 28 lines · 47 tokens per session scan A eb35e3a90f86
native-app-profiling is a skill published in the GitHub repository patrickserrano/lacquer (3 stars, last pushed yesterday), licensed MIT. It adds 47 tokens to every session and 729 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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