When implementing image loading in Jetpack Compose, use Coil (Coroutines Image Loader). It is the recommended library for Compose due to its efficiency and seamless integration.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versioncoil-composeExecute the skills CLI command in your project's root directory to begin installation:
Fetches coil-compose from new-silvermoon/awesome-android-agent-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 coil-compose. Access via /coil-compose 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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When implementing image loading in Jetpack Compose, use Coil (Coroutines Image Loader). It is the recommended library for Compose due to its efficiency and seamless integration.
AsyncImageUse AsyncImage for most use cases. It handles size resolution automatically and supports standard Image parameters.
AsyncImage(
model = ImageRequest.Builder(LocalContext.current)
.data("https://example.com/image.jpg")
.crossfade(true)
.build(),
placeholder = painterResource(R.drawable.placeholder),
error = painterResource(R.drawable.error),
contentDescription = stringResource(R.string.description),
contentScale = ContentScale.Crop,
modifier = Modifier.clip(CircleShape)
)
rememberAsyncImagePainterUse rememberAsyncImagePainter only when you need a Painter instead of a composable (e.g., for Canvas or Icon) or when you need to observe the loading state manually.
[!WARNING]
rememberAsyncImagePainterdoes not detect the size your image is loaded at on screen and always loads the image with its original dimensions by default. UseAsyncImageunless aPainteris strictly required.
val painter = rememberAsyncImagePainter(
model = ImageRequest.Builder(LocalContext.current)
.data("https://example.com/image.jpg")
.size(Size.ORIGINAL) // Explicitly define size if needed
.build()
)
SubcomposeAsyncImageUse SubcomposeAsyncImage when you need a custom slot API for different states (Loading, Success, Error).
[!CAUTION] Subcomposition is slower than regular composition. Avoid using
SubcomposeAsyncImagein performance-critical areas likeLazyColumnorLazyRow.
SubcomposeAsyncImage(
model = "https://example.com/image.jpg",
contentDescription = null,
loading = {
CircularProgressIndicator()
},
error = {
Icon(Icons.Default.Error, contentDescription = null)
}
)
ImageLoader instance for the entire app to share the disk/memory cache.crossfade(true) in ImageRequest for a smoother transition from placeholder to success.contentScale is set appropriately to avoid loading larger images than necessary.AsyncImage over other variants.contentDescription or set it to null for decorative images.crossfade(true) for better UX.SubcomposeAsyncImage in lists.ImageRequest for specific needs like transformations (e.g., CircleCropTransformation).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
Keeps context tight: coil-compose is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for coil-compose matched our evaluation — installs cleanly and behaves as described in the markdown.
coil-compose has been reliable in day-to-day use. Documentation quality is above average for community skills.
coil-compose fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: coil-compose is focused, and the summary matches what you get after install.
We added coil-compose from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
We added coil-compose from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: coil-compose is focused, and the summary matches what you get after install.
coil-compose is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: coil-compose is the kind of skill you can hand to a new teammate without a long onboarding doc.
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