Borrowing it
Nothing to install: this file belongs to conectlens/lenserfight. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/conectlens/lenserfight/main/.agents/skills/react-native-performance-reviewer/SKILL.mdgit clone --depth 1 https://github.com/conectlens/lenserfightWrote 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/conectlens/lenserfight/react-native-performance-reviewer)<a href="https://agentmods.dev/skills/conectlens/lenserfight/react-native-performance-reviewer"><img src="https://agentmods.dev/badge/skills/conectlens/lenserfight/react-native-performance-reviewer/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/conectlens/lenserfight/react-native-performance-reviewer"><img src="https://agentmods.dev/badge/skills/conectlens/lenserfight/react-native-performance-reviewer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00209 | $0.01635 |
| Opus 5.5 | $0.00084 | $0.00654 |
| Sonnet 5.5 | $0.00042 | $0.00327 |
| Haiku 4.5 | $0.00021 | $0.00163 |
Grade A, and why
react-native-performance-reviewer 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 1mo 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
React Native / Expo Go Performance Reviewer
When to activate
Any code touch on:
apps/mobile/**— components, screens, hooks, navigationlibs/*libraries bundled by Metro (Expo/Hermes)- Expo modules, SDK APIs, or native bridge call sites
- AsyncStorage, SecureStore, MMKV, or SQLite usage
- Push notification handlers or background fetch tasks
- State management (Zustand, Redux, Jotai, Context)
- Network layers, query clients (React Query, SWR, Apollo)
- FlatList, SectionList, FlashList, ScrollView, VirtualizedList
- Image components, video players, camera flows
- Reanimated, Animated API, or gesture handlers
Review workflow
- Load
references/PERFORMANCE_CHECKLIST.mdand apply all sections relevant to the changed files. - Inspect the diff for the detection rules below.
- Produce a severity-ordered finding list using the output format below.
- Use
assets/review-report-template.mdfor the full structured report.
Severity classification
| Severity | Meaning |
|---|---|
critical |
Causes crashes, ANRs, OOM kills, or severe jank on mid-range devices under normal load |
high |
Visible jank, memory growth, or excessive battery drain on mid-range devices at moderate usage |
medium |
Degrades experience on low-end devices or under stress (50+ concurrent users) |
low |
Minor inefficiency, acceptable now but risky at scale |
info |
Observation with no current risk |
Core detection rules
Rendering
- Flag components lacking
React.memo,useCallback, oruseMemowhere props are objects, arrays, or functions created inline. - Flag
useEffectwith no dependency array or an array including unstable references (new object/array each render). - Flag Context providers whose
valueis a new object literal on every render — re-renders all consumers. - Flag navigation screens that re-render on every tab switch due to missing
React.memooruseFocusEffectmisuse.
Memory & lifecycle
- Flag subscriptions, event listeners, timers, and Animated listeners not cleaned up in
useEffectreturn. - Flag state that accumulates over a session without a bound (unbounded log arrays, growing caches, stacked screens never popped).
- Flag
useRefstoring a value that updates every render (effectively a re-render vector).
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 138 lines · 209 tokens per session scan A aad4a40d951f
react-native-performance-reviewer is a skill published in the GitHub repository conectlens/lenserfight (18 stars, last pushed 1mo ago), licensed MIT. It adds 209 tokens to every session and 1,635 once invoked, about $0.0008 per session on Opus 5.5. 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 2026-08-30.
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