agentleFS
Sign inStart free
krutikJainkrutikJain/android-agent-skills/.github/skills/android-local-persistence-datastore/SKILL.md

android-local-persistence-datastore

Skill14 starsChanged 8 months agoMITView on GitHub

Persist lightweight user and app preferences with DataStore, schema-safe models, and migration-aware defaults.

The file

We split this skill into the rules it gives an agent

5 rules. About 0 are common, 5 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈5

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use this skill when the request is about: android datastore setup, replace sharedpreferences android, typed local preferences android.

    line 17
  2. Distinctive:

    Prefer typed models and explicit serializers over ad-hoc maps or bundles.

    line 31
  3. Distinctive:

    Keep background work idempotent and cancellation-aware.

    line 32
  4. Distinctive:

    Do not leak storage, media, or networking details into presentation code.

    line 33
  5. Distinctive:

    Treat user data durability, privacy, and migration paths as part of the implementation.

    line 34

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Android Local Persistence DataStore

When To Use

  • Use this skill when the request is about: android datastore setup, replace sharedpreferences android, typed local preferences android.
  • Primary outcome: Persist lightweight user and app preferences with DataStore, schema-safe models, and migration-aware defaults.
  • Handoff skills when the scope expands:
  • android-room-database
  • android-modernization-upgrade

Workflow

  1. Confirm the data source, persistence boundary, sync model, and device capability involved.
  2. Model contracts explicitly before wiring network, storage, media, or background APIs.
  3. Apply the recommended AndroidX or platform pattern with migration-safe defaults.
  4. Validate offline, retry, and process death behavior against the sample apps and scenarios.
  5. Escalate security, performance, or release risk to the linked supporting skills when needed.

Guardrails

  • Prefer typed models and explicit serializers over ad-hoc maps or bundles.
  • Keep background work idempotent and cancellation-aware.
  • Do not leak storage, media, or networking details into presentation code.
  • Treat user data durability, privacy, and migration paths as part of the implementation.

Anti-Patterns

  • Blocking the main thread with disk or network calls.
  • Treating retryable sync failures as terminal user-facing errors.
  • Mixing cache models and wire models without a mapping layer.
  • Requesting broad storage or notification capabilities when a narrower API exists.

Examples

Happy path

  • Scenario: Store reminder and filter preferences for OrbitTasks safely.
  • Command: cd examples/orbittasks-compose && ./gradlew :app:testDebugUnitTest

Edge case

  • Scenario: Migrate old preference keys without breaking the XML fixture.
  • Command: cd examples/orbittasks-xml && ./gradlew :app:testDebugUnitTest

Failure recovery

  • Scenario: Avoid mixing lightweight preference storage requests with Room persistence work.
  • Command: python3 scripts/eval_triggers.py --skill android-local-persistence-datastore

Done Checklist

  • The implementation path is explicit, minimal, and tied to the right Android surface.
  • Relevant example commands and benchmark prompts have been exercised or updated.
  • Handoffs to adjacent skills are documented when the request crosses boundaries.
  • Official references cover the chosen pattern and the main migration or troubleshooting path.

Official References

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

Check your setup, free