Make your Capacitor apps fast and responsive.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versioncapacitor-performanceExecute the skills CLI command in your project's root directory to begin installation:
Fetches capacitor-performance from cap-go/capgo-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate capacitor-performance. Access via /capacitor-performance in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Make your Capacitor apps fast and responsive.
// BAD - All plugins loaded at startup
import { Camera } from '@capacitor/camera';
import { Filesystem } from '@capacitor/filesystem';
import { Geolocation } from '@capacitor/geolocation';
// GOOD - Load when needed
async function takePhoto() {
const { Camera } = await import('@capacitor/camera');
return Camera.getPhoto({ quality: 90 });
}
# Analyze bundle
npx vite-bundle-visualizer
# Tree-shake imports
import { specific } from 'large-library'; // Good
import * as everything from 'large-library'; // Bad
// Use appropriate quality
const photo = await Camera.getPhoto({
quality: 80, // Not 100
width: 1024, // Limit size
resultType: CameraResultType.Uri, // Not Base64
});
// Lazy load images
<img loading="lazy" src={url} />
// BAD - Multiple bridge calls
for (const item of items) {
await Storage.set({ key: item.id, value: item.data });
}
// GOOD - Single call with batch
await Storage.set({
key: 'items',
value: JSON.stringify(items),
});
/* GPU accelerated */
.animated {
transform: translateX(100px);
will-change: transform;
}
/* Avoid - triggers layout */
.animated {
left: 100px;
}
// Use virtual list for long lists
import { VirtualScroller } from 'your-framework';
<VirtualScroller
items={items}
itemHeight={60}
renderItem={(item) => <ListItem item={item} />}
/>
import { debounce } from 'lodash-es';
const handleScroll = debounce((e) => {
// Handle scroll
}, 16); // ~60fps
import { App } from '@capacitor/app';
// Store listener handle
const handle = await App.addListener('appStateChange', callback);
// Cleanup on unmount
onUnmount(() => {
handle.remove();
});
// Clear large data when done
let largeData = await fetchLargeData();
processData(largeData);
largeData = null; // Allow GC
| Metric | Target |
|---|---|
| First Paint | < 1s |
| Time to Interactive | < 3s |
| Frame Rate | 60fps |
| Memory | Stable, no growth |
| Bundle Size | < 500KB gzipped |
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
I recommend capacitor-performance for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: capacitor-performance is the kind of skill you can hand to a new teammate without a long onboarding doc.
capacitor-performance is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
capacitor-performance reduced setup friction for our internal harness; good balance of opinion and flexibility.
capacitor-performance has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: capacitor-performance is the kind of skill you can hand to a new teammate without a long onboarding doc.
capacitor-performance fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Useful defaults in capacitor-performance — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for capacitor-performance matched our evaluation — installs cleanly and behaves as described in the markdown.
Solid pick for teams standardizing on skills: capacitor-performance is focused, and the summary matches what you get after install.
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