Now liveThe Skillselion MCP - thousands of ranked skills, loaded into your agent mid-task. No install.Get it →
dhruvanbhalara avatar

Dart Memory

  • 181 installs
  • 29 repo stars
  • Updated July 10, 2026
  • dhruvanbhalara/skills

Diagnose Dart heap growth, retained objects, image/cache leaks, and lifecycle mistakes before shipping Flutter builds that stutter, crash, or balloon on low-RAM devices.

About

Covers Dart and Flutter memory management: object lifecycles, weak references, disposal of controllers and subscriptions, cache bounds, and profiling workflows so mobile apps avoid OOM crashes, jank, and creeping RAM use in production.

  • Heap retention and leak triage
  • dispose/cancel lifecycle habits
  • Image and cache sizing guidance
  • Stream/subscription cleanup
  • Release profiling on real devices

Dart Memory by the numbers

  • 181 all-time installs (skills.sh)
  • Ranked #478 of 1,039 Mobile Development skills by installs in the Skillselion catalog
  • Data as of Jul 31, 2026 (Skillselion catalog sync)
npx skills add https://github.com/dhruvanbhalara/skills --skill dart-memory

Add your badge

Show developers this skill is listed on Skillselion. Paste this into your README.

Listed on Skillselion
Installs181
repo stars29
Last updatedJuly 10, 2026
Repositorydhruvanbhalara/skills

What it does

Diagnose Dart heap growth, retained objects, image/cache leaks, and lifecycle mistakes before shipping Flutter builds that stutter, crash, or balloon on low-RAM devices.

Files

SKILL.mdMarkdownGitHub ↗

Memory Management

Mobile devices have limited RAM. Efficient memory management is critical to prevent crashes and ensure a smooth user experience.

Resource Lifecycle

  • Explicit Disposal: Always close StreamController, Timer, FocusNode, and ChangeNotifier in the dispose() method.
  • Late Initialization: Use late to delay object creation until it's actually needed, reducing initial memory footprint.

Garbage Collection (GC) Pressure

  • Generational GC: Dart's GC is optimized for short-lived objects. However, creating thousands of objects in a single frame can still cause jank.
  • Object Re-use: Avoid creating new objects in build() or high-frequency loops. Reuse data structures where possible.
  • Large Collections: Clearing a large list (list.clear()) is better than re-assigning it to a new list if the list itself is long-lived.

Mobile Specifics

  • Isolates: Use Isolate.run() for heavy computations (JSON parsing > 1MB, image processing). This keeps the main thread free and prevents UI freezes.
  • Image Memory: Use cacheWidth and cacheHeight in Image.network or Image.asset to avoid loading high-resolution images into memory at full size.
  • Memory Leaks: Use the DevTools Memory View to identify "leaking" objects that stay in the heap after their context (like a screen) is closed.

Large Data Handling

  • Pagination: Never load entire datasets into memory. Use server-side or local database pagination (Isar, SQLite).
  • Streaming: For large files or real-time data, use Stream to process data in chunks rather than buffering the entire content in memory.

Related skills

This week in AI coding

Five minutes, every Monday - the tools, releases and tactics for developers.

unsubscribe anytime.