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Android バックグラウンドタスクスペシャリスト

WorkManager、Foreground Service、Dozeモード、バッテリー最適化に精通したAndroidバックグラウンドタスクの専門家。アクティベート条件: WorkManager、バックグラウンドタスクAndroid、Foreground Service、Dozeモード、バッテリー最適化、定期的な処理、バックグラウンド同期Android、JobScheduler。対象外: iOSバックグラウンドタスク(swiftui-data-flow-expertを使用)、React Nativeバックグラウンド(mobile-offline-sync-architectを使用)、UIアーキテクチャ(jetpack-compose-navigation-expertを使用)。

Android バックグラウンドタスクスペシャリスト とは?

このスキルは、Androidプラットフォームにおけるバックグラウンド実行の専門家です。WorkManager、Foreground Service、Dozeモード、バッテリー最適化を活用した、効率的でバッテリーに優しいバックグラウンド処理の設計と実装を支援します。 WorkManagerを使用した遅延可能なタスクのスケジューリング、Foreground Serviceによるユーザー可視の継続タスク、DozeモードとApp Standbyへの準拠、Constraints(ネットワーク、バッテリー、ストレージ)の設定、WorkContinuationによるタスクチェーン、PeriodicWorkRequestによる定期的な同期など、幅広いユースケースをカバーします。 Antipatternの回避(AlarmManagerの不適切な使用、Foreground Serviceのタイプ未宣言など)や、adbを用いたDozeモード・バッテリー最適化のテスト方法も習得しています。 対応プラットフォーム: Android(API 26+)。Kotlin CoroutinesとHiltのサポートを含む、モダンな実装パターンを提供します。

対応~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/curiositech/windags-skills/tree/HEAD/skills/android-background-task-specialist

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ドキュメント

Android バックグラウンドタスクスペシャリスト は何をしますか?

WorkManager、Foreground Service、Dozeモード処理、バッテリー効率の良い処理を用いたAndroidバックグラウンド実行のエキスパート。

アクティベートトリガー

アクティベート条件: "WorkManager", "background task Android", "Foreground Service", "Doze mode", "battery optimization", "periodic work", "background sync", "JobScheduler", "background processing Android"

対象外: iOSバックグラウンドタスク → swiftui-data-flow-expert | React Nativeバックグラウンド → mobile-offline-sync-architect | UIアーキテクチャ → jetpack-compose-navigation-expert

クイックスタート

  1. 適切なAPIを選択 — 遅延可能な処理にはWorkManager、ユーザー可視の継続タスクにはForeground Service、短い非同期処理にはコルーチン
  2. ワーカーを定義 — クリーンな非同期コードのためにsuspend関数を持つCoroutineWorkerを拡張
  3. 制約を設定 — ネットワークタイプ、バッテリーレベル、ストレージ要件
  4. DozeモードとApp Standbyを処理 — Doze互換のスケジューリングにはAlarmManagerではなくWorkManagerを使用
  5. adbでテスト — Dozeモードを強制し、制約が満たされたときに処理が実行されることを確認

コア機能

領域技術
遅延可能な処理WorkManager 2.10, OneTimeWorkRequest, PeriodicWorkRequest
フォアグラウンドForeground Serviceタイプ (dataSync, location, mediaPlayback, shortService)
制約NetworkType, BatteryNotLow, StorageNotLow, DeviceIdle
チェーンWorkContinuation, パラレルチェーン, ユニークワークポリシー
テストTestWorkerBuilder, TestListenableWorkerBuilder, WorkManagerTestInitHelper

アーキテクチャパターン

WorkManager 判定ツリー

タスクはユーザーが開始し、完了する必要があるか?
  ├─ YES → 短い(< 10分)か?
  │   ├─ YES → Foreground Service (shortService type)
  │   └─ NO → Foreground Service (dataSync, location, etc.)
  │
  └─ NO → 遅延可能か?
      ├─ YES → WorkManager
      │   ├─ 1回限り?→ OneTimeWorkRequest
      │   └─ 定期的?→ PeriodicWorkRequest(最小15分間隔)
      │
      └─ NO → アプリ表示中のみか?
          ├─ YES → CoroutineScope (viewModelScope または lifecycleScope)
          └─ NO → おそらくWorkManagerが必要

リトライと進捗状況を備えたCoroutineWorker

@HiltWorker
class SyncWorker @AssistedInject constructor(
    @Assisted context: Context,
    @Assisted params: WorkerParameters,
    private val syncRepository: SyncRepository,
    private val notificationHelper: NotificationHelper,
) : CoroutineWorker(context, params) {

    override suspend fun doWork(): Result {
        // UI観測のための進捗を報告
        setProgress(workDataOf("progress" to 0))

        return try {
            val pendingItems = syncRepository.getPendingChanges()

            pendingItems.forEachIndexed { index, item ->
                syncRepository.syncItem(item)
                setProgress(workDataOf(
                    "progress" to ((index + 1) * 100 / pendingItems.size)
                ))
            }

            Result.success(workDataOf("synced" to pendingItems.size))
        } catch (e: IOException) {
            if (runAttemptCount < 3) {
                Result.retry()  // デフォルトで指数バックオフ
            } else {
                Result.failure(workDataOf("error" to e.message))
            }
        }
    }

    override suspend fun getForegroundInfo(): ForegroundInfo {
        return ForegroundInfo(
            NOTIFICATION_ID,
            notificationHelper.createSyncNotification()
        )
    }
}

制約とチェーンを使用したスケジューリング

// 1時間ごとの定期的な同期をスケジュール(最小15分)
val syncRequest = PeriodicWorkRequestBuilder<SyncWorker>(1, TimeUnit.HOURS)
    .setConstraints(
        Constraints.Builder()
            .setRequiredNetworkType(NetworkType.CONNECTED)
            .setRequiresBatteryNotLow(true)
            .build()
    )
    .setBackoffCriteria(
        BackoffPolicy.EXPONENTIAL,
        WorkRequest.MIN_BACKOFF_MILLIS,
        TimeUnit.MILLISECONDS
    )
    .addTag("sync")
    .build()

WorkManager.getInstance(context).enqueueUniquePeriodicWork(
    "periodic-sync",
    ExistingPeriodicWorkPolicy.KEEP,  // すでにスケジュールされている場合は再開しない
    syncRequest
)

// チェーン: download → process → upload(逐次)
WorkManager.getInstance(context)
    .beginWith(downloadWork)
    .then(processWork)
    .then(uploadWork)
    .enqueue()

アンチパターン

  1. 遅延可能な処理にAlarmManagerを使用 — AlarmManagerのアラームはDoze互換ではなく、バッテリーを消費します。Doze、App Standby、バッテリーセーバーを尊重するWorkManagerを使用してください。
  2. フォアグラウンド通知なしのバックグラウンドサービス — Android 12+はバックグラウンドサービスを強制終了します。適切なタイプ宣言を持つForeground Service、またはWorkManagerを使用してください。
  3. 15分未満のPeriodicWork — WorkManagerは最小15分の間隔を強制します。より頻繁な処理には、Foreground Serviceまたはライフサイクル認識を持つコルーチンを使用してください。
  4. フォアグラウンドサービスタイプの未宣言 — Android 14+ではマニフェストに明示的なforegroundServiceTypeが必要です。省略するとSecurityExceptionが発生します。
  5. 処理の観測の無視 — 処理を開始してもユーザーに進捗を表示しない。getWorkInfoByIdLiveData()またはgetWorkInfoByIdFlow()を介してWorkInfoを観測し、UIを更新します。

品質チェックリスト

[ ] 遅延可能なバックグラウンドタスクにはAlarmManagerではなくWorkManagerを使用
[ ] Foreground ServiceがAndroidManifestで正しいタイプで宣言されている
[ ] 制約が設定されている(ネットワーク、バッテリー、ストレージ)
[ ] 一時的な障害に対してバックオフ付きのリトライポリシー
[ ] 定期的/1回限りの処理に対してユニークワークポリシーが設定されている
[ ] 処理の進捗がUIから観測可能
[ ] Dozeモードがadbでテスト済み: `adb shell dumpsys deviceidle force-idle`
[ ] バッテリー最適化がテスト済み: `adb shell am set-inactive <package> true`
[ ] ワーカーがTestWorkerBuilderでテスト済み
[ ] フォアグラウンドサービス通知用の通知チャネルが作成されている
[ ] ワークチェーンが部分的な失敗を適切に処理する
[ ] バックグラウンド処理がユーザーのバッテリーセーバー設定を尊重する

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

curiositech/windags-skills

Expert in 2000s-era music visualization (Milkdrop, AVS, Geiss) and modern WebGL implementations. Specializes in Butterchurn integration, Web Audio API AnalyserNode FFT data, GLSL shaders for audio-reactive visuals, and psychedelic generative art. Activate on "Milkdrop", "music visualization", "WebGL visualizer", "Butterchurn", "audio reactive", "FFT visualization", "spectrum analyzer". NOT for simple bar charts/waveforms (use basic canvas), video editing, or non-audio visuals.

curiositech/windags-skills

Expert legal research agent for finding and scraping expungement data state by state. Knows authoritative sources, URL patterns, Firecrawl configuration, and 2026 legal landscape.

curiositech/windags-skills

Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).

curiositech/windags-skills

Implement WCAG 2.2 AA/AAA compliance with automated testing, keyboard navigation, screen reader support, and focus management. Activate on: accessibility audit, WCAG compliance, keyboard navigation, screen reader, aria attributes, axe-core, focus trap. NOT for: design-level accessibility review (use design-accessibility-auditor), color contrast only (use css-in-js-architect).

curiositech/windags-skills

Time-blind friendly planning, executive function support, and daily structure for ADHD brains. Specializes in realistic time estimation, dopamine-aware task design, and building systems that actually work for neurodivergent minds.

curiositech/windags-skills

Designs digital experiences for ADHD brains using neuroscience research and UX principles. Expert in reducing cognitive load, time blindness solutions, dopamine-driven engagement, and compassionate design patterns. Activate on 'ADHD design', 'cognitive load', 'accessibility', 'neurodivergent UX', 'time blindness', 'dopamine-driven', 'executive function'. NOT for general accessibility (WCAG only), neurotypical UX design, or simple UI styling without ADHD context.

curiositech/windags-skills

>- Apply crisis decision-making research to agent routing, uncertainty triage, and coordination failure analysis in time-pressured systems. Use when diagnosing handoff failures, analytical paralysis, or expert judgment under incomplete information. NOT for routine coding, simple CRUD design, or static single-agent tasks with complete information.

curiositech/windags-skills

Extend and modify the admin dashboard, developer portal, and operations console. Use when adding new admin tabs, metrics, monitoring features, or internal tools. Activates for dashboard development, analytics, user management, and internal tooling.

curiositech/windags-skills

Conversation patterns and interaction protocols for multi-agent systems. Covers request/response, pub/sub, blackboard, delegation chains, debate, critique, consensus, fan-out/fan-in, supervisor-worker, and peer negotiation. Deep analysis of AutoGen conversation patterns, CrewAI delegation, LangGraph state passing, and FIPA-ACL performatives. Teaches how to design what agents say to each other and in what order. Activate on: "agent conversation", "agent protocol", "multi-agent debate", "agent delegation", "supervisor worker pattern", "agent voting", "consensus protocol", "fan-out fan-in", "agent negotiation", "blackboard pattern", "agent dialogue", "conversation topology", "agent handoff". NOT for: wire format or serialization (use agent-interchange-formats), orchestration infrastructure (use agentic-infrastructure-2026), single agent behavior (use agentic-patterns).

curiositech/windags-skills

Meta-agent for creating new custom agents, skills, and MCP integrations. Expert in agent design, MCP development, skill architecture, and rapid prototyping. Activate on 'create agent', 'new skill', 'MCP server', 'custom tool', 'agent design'. NOT for using existing agents (invoke them directly), general coding (use language-specific skills), or infrastructure setup (use deployment-engineer).

curiositech/windags-skills

AI-powered calendar management and agent-based scheduling coordination. Covers calendar APIs (Google Calendar, CalDAV/iCal), AI scheduling assistants (Reclaim, Clockwise, Motion, Cal.com), building custom calendar agents with MCP, multi-calendar merging, timezone management, focus block protection, meeting fatigue detection, and agent-to-agent meeting negotiation protocols. Activate on: "calendar agent", "AI scheduling", "calendar coordination", "meeting scheduling", "calendar API", "focus time protection", "calendar optimization", "Google Calendar MCP", "Reclaim", "Clockwise", "Motion", "Cal.com", "smart scheduling", "calendar-aware agent", "timezone scheduling", "agent negotiation meetings". NOT for: manual calendar UI component design (use form-validation-architect), project management scheduling or Gantt charts (use project-management-guru-adhd), general time-tracking or pomodoro apps (use adhd-daily-planner for time-awareness), building the agent itself from scratch (use agent-creator).

curiositech/windags-skills

Build and adopt production AI agent infrastructure in 2026. Covers framework selection (LangGraph, CrewAI, AutoGen, MCP), orchestration patterns, evaluation, observability, memory systems, and tool use. Also covers the SOCIAL dimension: how to sell agent infrastructure internally, change management, measuring ROI, building trust in autonomous systems, and scaling adoption across teams. Activate on: "agent infrastructure", "agent framework comparison", "which agent framework", "sell AI tools internally", "agent adoption", "agent observability", "agent evaluation", "MCP architecture", "agentic mesh", "enterprise AI agents", "AI change management", "agent ROI". NOT for: building specific agents (use ai-engineer), designing agent behavior patterns (use agentic-patterns), prompt tuning (use prompt-engineer).

curiositech/windags-skills

Fundamental patterns for effective agentic behavior. Teaches decomposition, tool orchestration, error recovery, context management, quality self-assessment, and knowing when to stop. Model-agnostic principles that make any agent more effective regardless of domain. Activate on: "how should I structure this agent", "agentic workflow", "agent patterns", "multi-step task", "tool orchestration", "/agentic-patterns", "decompose this", "agent best practices", "chain of actions", "when should the agent stop", "agent loop design". NOT for: creating agent infrastructure (use agent-creator), building DAGs (use windags-architect), specific tool implementation.

curiositech/windags-skills

Automated discovery and matching of agent skills for dynamic task routing and capability assessment

curiositech/windags-skills

Cryptographic security for agentic systems — zero-trust agent networking, signed message envelopes (JWS/JWE), capability-based security (ocaps), Merkle tree audit trails, WASM sandboxing, and formal verification. Covers CLI dev tool security, mTLS between agents, permission boundaries (least privilege for AI agents), and supply chain security for skills/plugins. Activate on: "agent security", "zero trust agents", "secure agent communication", "capability-based security", "ocap", "signed messages between agents", "agent audit trail", "sandbox agent execution", "agent permissions", "mTLS agents", "cryptographic verification", "agent supply chain", "OWASP agentic", "prove agent did X", "tamper-proof agent logs". NOT for: application-level SAST scanning (use security-auditor), network firewall rules (use infrastructure), SOC2/HIPAA compliance (organizational), or prompt injection defense (use prompt-engineer).

curiositech/windags-skills

Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Deep comparison of A2A protocol, MCP, OpenAI function calling, and LangChain message types. Teaches when to use rigid schemas vs free-form with validation, typed vs untyped, streaming vs batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).

curiositech/windags-skills

Logic-based agent programming language implementing BDI architecture for practical autonomous agent development

curiositech/windags-skills

>- Design AgentSpeak(L)-style BDI agents with context-guarded plans, selection functions, and intention stacks. Use for interruptible autonomy, agent policy, and multi-agent orchestration in dynamic environments. NOT for simple rule engines, static planners, or centralized workflows.

curiositech/windags-skills

Foundational concurrent computation model where actors communicate exclusively through asynchronous message passing

curiositech/windags-skills

license: Apache-2.0 NOT for unrelated tasks outside this domain.

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