用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/a5c-ai/babysitter --skill mobile-performance-profiling命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
Reference for querying the Atlas knowledge graph through its MCP tools — the SECONDARY enrichment/comparison layer that adds best-practice context to systems you have ALREADY scanned from your real sources (`az`, repos, dirs). Use when you need to look up nodes, edges, kinds, clusters, stats, or wiki pages in Atlas to compare against your real inventory. (atlas graph, query atlas, atlas mcp, search the graph, graph neighbors, atlas record, atlas kinds, enrichment layer)
Atlas turns your STATED NEED into a real systems atlas by SCANNING your actual sources (Azure via `az`, git repos, local dirs) and process/data mining them, THEN enriching against the Atlas knowledge graph. Use this skill when asked to inventory/map your real systems, scan your cloud + repos + directories, mine the real processes or data they contain, or collect their real constraints/gotchas. (atlas, scan my systems, inventory our azure account, map my repos, real systems atlas, process mining, data mining, collect nuances, system discovery)
This skill should be used when the user asks to "find skills in the wild", "assimilate popular workflows", "discover SKILL.md files in repos", "research external skills", "find workflow patterns", "survey the skill landscape", "what skills exist out there", or wants to investigate public repositories for extractable processes, babysitter plugins, and reusable procedural insights. Searches GitHub for SKILL.md files, classifies repos by archetype, and maintains structured research under docs/reference-repos/.
正在显示 SKILL.md
基于 SOC 职业分类
| name | Mobile Performance Profiling |
| description | Mobile app performance analysis and optimization |
| version | 1.0.0 |
| category | Performance |
| slug | mobile-perf |
| status | active |
| graph | {"domains":["domain:mobile"],"specializations":["specialization:mobile-development"],"skillAreas":["skill-area:battery-consumption-testing","skill-area:memory-usage-testing"],"roles":["role:mobile-engineer"],"workflows":["workflow:feature-development","workflow:release-management"],"topics":["topic:accessibility"]} |
This skill provides mobile app performance analysis and optimization capabilities. It enables profiling with Xcode Instruments, Android Profiler, Flipper, and Flutter DevTools to identify and fix performance issues.
bash - Execute profiling tools and build commandsread - Analyze performance reports and profileswrite - Generate optimization configurationsedit - Update performance-related codeglob - Search for performance filesgrep - Search for patternsTime Profiler
Allocations
Core Animation
CPU Profiler
Memory Profiler
Network Profiler
Flipper Performance
Hermes Profiler
App Startup
Memory Management
Rendering Performance
mobile-performance-optimization.js - Performance tuningmobile-testing-strategy.js - Performance testingjetpack-compose-ui.js - Compose optimizationswiftui-app-development.js - SwiftUI optimization# Record Time Profiler trace
xcrun xctrace record --device "iPhone 15 Pro" \
--template "Time Profiler" \
--attach "MyApp" \
--time-limit 30s \
--output ~/Desktop/profile.trace
# Export trace data
xcrun xctrace export --input ~/Desktop/profile.trace \
--xpath '/trace-toc/run/tracks/track[@name="Time Profiler"]' \
--output ~/Desktop/profile_data.xml
# Capture CPU trace
adb shell am profile start com.example.myapp /data/local/tmp/profile.trace
# Stop and pull trace
adb shell am profile stop com.example.myapp
adb pull /data/local/tmp/profile.trace ./profile.trace
# Capture heap dump
adb shell am dumpheap com.example.myapp /data/local/tmp/heap.hprof
adb pull /data/local/tmp/heap.hprof ./heap.hprof
// Optimize list rendering
import { FlashList } from '@shopify/flash-list';
const OptimizedList = () => {
const renderItem = useCallback(({ item }) => (
<MemoizedListItem item={item} />
), []);
return (
<FlashList
data={items}
renderItem={renderItem}
estimatedItemSize={100}
keyExtractor={item => item.id}
/>
);
};
// Memoize expensive components
const MemoizedListItem = memo(({ item }) => {
return (
<View style={styles.item}>
<Text>{item.title}</Text>
</View>
);
}, (prev, next) => prev.item.id === next.item.id);
// Optimize images
import FastImage ;
= () => (
);
// Optimize view updates
struct OptimizedView: View {
@State private var items: [Item] = []
var body: some View {
List {
ForEach(items) { item in
ItemRow(item: item)
.equatable() // Prevent unnecessary redraws
}
}
}
}
// Use Equatable for custom views
struct ItemRow: View, Equatable {
let item: Item
static func == (lhs: ItemRow, rhs: ItemRow) -> Bool {
lhs.item.id == rhs.item.id
}
var body: some View {
Text(item.title)
}
}
// Lazy loading
struct LazyImageView: View {
let url: URL
var body: some View {
AsyncImage(url: url) { phase in
switch phase {
case .empty:
()
.success( image):
image.resizable().aspectRatio(contentMode: .fit)
.failure:
(systemName: )
:
()
}
}
}
}
// Optimize recomposition
@Composable
fun OptimizedList(items: List<Item>) {
LazyColumn {
items(
items = items,
key = { it.id }
) { item ->
// Stable key prevents unnecessary recomposition
ItemCard(item = item)
}
}
}
// Use derivedStateOf for computed values
@Composable
fun SearchableList(items: List<Item>, query: String) {
val filteredItems by remember(items, query) {
derivedStateOf {
items.filter { it.title.contains(query, ignoreCase = true) }
}
}
LazyColumn {
items(filteredItems, key = { it.id }) { ItemCard(it) }
}
}
// Use remember for expensive calculations
@Composable
fun ExpensiveView(data: ComplexData) {
val processedData = remember(data) {
expensiveCalculation(data)
}
Text(processedData.result)
}
mobile-testing - Performance testingreact-native-dev - RN optimizationflutter-dart - Flutter optimizationkotlin-compose - Compose optimization