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 cenconq25/claude-code-app-studio --skill perf-profilegit clone --depth 1 https://github.com/cenconq25/claude-code-app-studioWrote 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/cenconq25/claude-code-app-studio/perf-profile)<a href="https://agentmods.dev/skills/cenconq25/claude-code-app-studio/perf-profile"><img src="https://agentmods.dev/badge/skills/cenconq25/claude-code-app-studio/perf-profile/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/cenconq25/claude-code-app-studio/perf-profile"><img src="https://agentmods.dev/badge/skills/cenconq25/claude-code-app-studio/perf-profile.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00046 | $0.02237 |
| Opus 5 | $0.00023 | $0.01118 |
| Sonnet 5 | $0.00009 | $0.00447 |
| Haiku 4.5 | $0.00005 | $0.00224 |
Grade A, and why
perf-profile 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 5d 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 — 301 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Perf Profile
A repeatable mobile-performance pass. Each metric has a target tool, a budget, a measurement protocol, and a severity rule. The output is a prioritized backlog of optimizations.
Phase 1: Read Budgets
Read .claude/docs/technical-preferences.md for performance budgets.
Defaults if unset:
- Cold start (clean install, no caches): under 2.0s on Tier A iPhone, under 2.5s on Tier A Android, under 3.5s on a low-end device.
- Warm start: under 1.0s.
- Frame budget: 16.6 ms at 60fps, 8.3 ms at 120fps. Dropped frames budget: < 0.5% of total frames in a session.
- Memory after reaching home: under 200 MB on iOS, under 250 MB on Android.
- App size on disk: under 100 MB iOS, under 150 MB Android (including AAB expansion).
- Network: first-meaningful-content under 1.5s on 3G; offline path must render in under 500 ms.
- Battery: idle drain under 5%/hour foreground, under 1%/hour background.
Confirm budgets with the user. Capture the build under test.
Phase 2: Pick Target Metrics
Parse --target. If all, run every metric in order. Otherwise run
the requested one(s).
For each metric, the rest of the skill follows the same shape: tool, protocol, capture, compare, recommendation.
Phase 3: Cold Start
Tools:
- iOS: Instruments -> App Launch template; or
xcrun xctrace. - Android:
adb shell am start -W com.app/.MainActivity; Macrobenchmark StartupBenchmark. - React Native: TTI (time-to-interactive) marker via
Performance.now()on the home screen mount. - Flutter:
flutter run --profile --trace-startup.
Protocol:
- Force-stop the app.
- Reboot the device (or at minimum, kill all background apps).
- Wait 30 seconds for OS to settle.
- Launch with the chosen tool.
- Repeat 5 times; take the median.
Capture: total cold-start time, time-to-first-frame, time-to-interactive.
Common bottlenecks to surface:
- Synchronous work in the app entry point.
- Eager initialization of analytics SDKs, RUM, ads SDKs.
- Hermes / V8 startup overhead — bundle size > 5 MB is suspicious.
- Auto-loaded modules in
MainApplication/AppDelegate. - Heavy fonts loaded synchronously.
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.
- 5d ago First seen · 301 lines · 46 tokens per session scan A 55dc8531218f
perf-profile is a skill published in the GitHub repository cenconq25/claude-code-app-studio (40 stars, last pushed 4mo ago), licensed MIT. It adds 46 tokens to every session and 2,237 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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