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 agentmods add skills/berkcangumusisik/claude-code-practices/react-native-componentnpx skills add berkcangumusisik/claude-code-practices --skill react-native-componentgit clone --depth 1 https://github.com/berkcangumusisik/claude-code-practicesWhat 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 | $0.00016 | $0.00414 |
| Opus 5 | $0.00008 | $0.00207 |
| Sonnet 5 | $0.00003 | $0.00083 |
| Haiku 4.5 | $0.00002 | $0.00041 |
Grade A, and why
react-native-component 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.
What it actually says
Generate React Native Component
Component: $1 Description: $2
- Detect stack:
cat package.json | grep -E '"react-native"|"expo"|"nativewind"|"@shopify/restyle"|"tamagui"'
ls src/components/ components/ 2>/dev/null | head -5
-
Read 1-2 existing components to match patterns.
-
Generate the component:
import { StyleSheet, Text, TouchableOpacity, Platform } from 'react-native'
interface $1Props {
label: string
onPress: () => void
disabled?: boolean
}
export function $1({ label, onPress, disabled }: $1Props) {
return (
<TouchableOpacity
style={[styles.container, disabled && styles.disabled]}
onPress={onPress}
disabled={disabled}
accessible
accessibilityRole="button"
accessibilityLabel={label}
accessibilityState={{ disabled }}
>
<Text style={styles.label}>{label}</Text>
</TouchableOpacity>
)
}
const styles = StyleSheet.create({
container: {
padding: 16,
borderRadius: 8,
...Platform.select({
ios: { shadowOffset: { width: 0, height: 2 }, shadowOpacity: 0.1 },
android: { elevation: 2 },
}),
},
// ...
})
- Include:
- Accessibility props (
accessibilityRole,accessibilityLabel,accessibilityState) - Platform-specific styles (
Platform.select) - Proper hitSlop for small touch targets
- Dark mode support with
useColorSchemeif project uses it
- Accessibility props (
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 · 68 lines · 16 tokens per session scan A 83a8ea3a8553
react-native-component is a skill published in the GitHub repository berkcangumusisik/claude-code-practices (10 stars, last pushed 4mo ago), licensed MIT. It adds 16 tokens to every session and 414 once invoked, about $0.0001 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 2026-08-31.
Other skills, from other repositories
app-store-optimization
Complete App Store Optimization (ASO) toolkit for researching, optimizing, and tracking mobile app performance on Apple App Store and Google Play Store.
golden-epub
Use when testing the epub golden build.
golden-pdf
Use when testing the pdf golden build.
golden-man
Use when testing the man golden build.
golden-man-single
Use when testing the man golden build.
skill-builder
Automatically detect source types and build AI skills using Skill Seekers. Use when the user wants to create skills from documentation, repos, PDFs, videos, or other knowledge sources.