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agent-usage-optimizer-complexity-tier-model-mapping

vamseeachanta
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について

このスキルは、タスクの複雑度に基づいたモデル選択ガイドを提供します。日常的なタスクはHaiku、標準的なタスクはSonnet、複雑なタスクはOpusに割り当てられます。開発者は、単純なタスクをより安価なモデルにルーティングし、要求の厳しい作業には高性能モデルを確保することで、コスト最適化を図ることができます。タスクの性質が明確な場合は、ルートマッピングと併用して、理論的な効率と実際の実装効率のギャップを埋めるためにご活用ください。

クイックインストール

Claude Code

推奨
メイン
npx skills add vamseeachanta/workspace-hub
プラグインコマンド代替
/plugin add https://github.com/vamseeachanta/workspace-hub
Git クローン代替
git clone https://github.com/vamseeachanta/workspace-hub.git ~/.claude/skills/agent-usage-optimizer-complexity-tier-model-mapping

このコマンドをClaude Codeにコピー&ペーストしてスキルをインストールします

ドキュメント

Complexity Tier → Model Mapping

Complexity Tier → Model Mapping

Use this alongside Route mapping when task nature is clear:

Complexity tierKeywordsRecommended model
routineformat, rename, config, scaffold, update-doc, copyClaude Haiku
standardimplement, review, test, fix, migrate, documentClaude Sonnet
complexarchitecture, design, cross-repo, security, compoundClaude Opus

Key insight: the gap between theoretical and observed exposure is an implementation gap — not a capability gap. Routing routine tasks to cheaper models closes this gap for our workflow. See WRK-5002 to automate tier detection in task_classifier.sh.


Use this skill before any multi-item work session or when quota is a concern. Related: ai/optimization/model-selection, ai/optimization/usage-optimization

GitHub リポジトリ

vamseeachanta/workspace-hub
パス: .claude/skills/ai/agent-usage-optimizer/complexity-tier-model-mapping

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