Debug and optimize Android/Gradle build performance. Use when builds are slow, investigating CI/CD performance, analyzing build scans, or identifying compilation bottlenecks.
npx skills add https://github.com/new-silvermoon/awesome-android-agent-skills --skill gradle-build-performance
./gradlew assembleDebug --scan./gradlew assembleDebug --scan
./gradlew assembleDebug --profile
# Opens report in build/reports/profile/
./gradlew assembleDebug --info | grep -E "^\:.*"
# Or view in Android Studio: Build > Analyze APK Build
| Phase | What Happens | Common Issues |
|-------|--------------|---------------|
| Initialization | settings.gradle.kts evaluated | Too many include() statements |
| Configuration | All build.gradle.kts files evaluated | Expensive plugins, eager task creation |
| Execution | Tasks run based on inputs/outputs | Cache misses, non-incremental tasks |
Build scan → Performance → Build timeline
Caches configuration phase across builds (AGP 8.0+):
# gradle.properties
org.gradle.configuration-cache=true
org.gradle.configuration-cache.problems=warn
Reuses task outputs across builds and machines:
# gradle.properties
org.gradle.caching=true
Build independent modules simultaneously:
# gradle.properties
org.gradle.parallel=true
Allocate more memory for large projects:
# gradle.properties
org.gradle.jvmargs=-Xmx4g -XX:+UseParallelGC
Reduces R class size and compilation (AGP 8.0+ default):
# gradle.properties
android.nonTransitiveRClass=true
KSP is 2x faster than kapt for Kotlin:
// Before (slow)
kapt("com.google.dagger:hilt-compiler:2.51.1")
// After (fast)
ksp("com.google.dagger:hilt-compiler:2.51.1")
Pin dependency versions:
// BAD: Forces resolution every build
implementation("com.example:lib:+")
implementation("com.example:lib:1.0.+")
// GOOD: Fixed version
implementation("com.example:lib:1.2.3")
Put most-used repositories first:
// settings.gradle.kts
dependencyResolutionManagement {
repositories {
google() // First: Android dependencies
mavenCentral() // Second: Most libraries
// Third-party repos last
}
}
Composite builds are faster than project() for large monorepos:
// settings.gradle.kts
includeBuild("shared-library") {
dependencySubstitution {
substitute(module("com.example:shared")).using(project(":"))
}
}
# gradle.properties
kapt.incremental.apt=true
kapt.use.worker.api=true
Don't read files or make network calls during configuration:
// BAD: Runs during configuration
val version = file("version.txt").readText()
// GOOD: Defer to execution
val version = providers.fileContents(file("version.txt")).asText
Avoid create(), use register():
// BAD: Eagerly configured
tasks.create("myTask") { ... }
// GOOD: Lazily configured
tasks.register("myTask") { ... }
Symptoms: Build scan shows long "Configuring build" time
Causes & Fixes:
| Cause | Fix |
|-------|-----|
| Eager task creation | Use tasks.register() instead of tasks.create() |
| buildSrc with many dependencies | Migrate to Convention Plugins with includeBuild |
| File I/O in build scripts | Use providers.fileContents() |
| Network calls in plugins | Cache results or use offline mode |
Symptoms: :app:compileDebugKotlin takes too long
Causes & Fixes:
| Cause | Fix |
|-------|-----|
| Non-incremental changes | Avoid build.gradle.kts changes that invalidate cache |
| Large modules | Break into smaller feature modules |
| Excessive kapt usage | Migrate to KSP |
| Kotlin compiler memory | Increase kotlin.daemon.jvmargs |
Symptoms: Tasks always rerun despite no changes
Causes & Fixes:
| Cause | Fix |
|-------|-----|
| Unstable task inputs | Use @PathSensitive, @NormalizeLineEndings |
| Absolute paths in outputs | Use relative paths |
| Missing @CacheableTask | Add annotation to custom tasks |
| Different JDK versions | Standardize JDK across environments |
// settings.gradle.kts
buildCache {
local { isEnabled = true }
remote<HttpBuildCache> {
url = uri("https://cache.example.com/")
isPush = System.getenv("CI") == "true"
credentials {
username = System.getenv("CACHE_USER")
password = System.getenv("CACHE_PASS")
}
}
}
For advanced build analytics:
// settings.gradle.kts
plugins {
id("com.gradle.develocity") version "3.17"
}
develocity {
buildScan {
termsOfUseUrl.set("https://gradle.com/help/legal-terms-of-use")
termsOfUseAgree.set("yes")
publishing.onlyIf { System.getenv("CI") != null }
}
}
# Skip tests for UI-only changes
./gradlew assembleDebug -x test -x lint
# Only run affected module tests
./gradlew :feature:login:test
After optimizations, verify:
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take new-silvermoon/gradle-build-performance from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.