agent-usage-optimizer-complexity-tier-model-mapping
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npx skills add vamseeachanta/workspace-hub/plugin add https://github.com/vamseeachanta/workspace-hubgit 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 tier | Keywords | Recommended model |
|---|---|---|
routine | format, rename, config, scaffold, update-doc, copy | Claude Haiku |
standard | implement, review, test, fix, migrate, document | Claude Sonnet |
complex | architecture, design, cross-repo, security, compound | Claude 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
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