agent-usage-optimizer-complexity-tier-model-mapping
Über
Diese Fähigkeit bietet eine Modellauswahlhilfe basierend auf der Aufgabenkomplexität, die Routineaufgaben Haiku, Standardaufgaben Sonnet und komplexe Aufgaben Opus zuordnet. Sie hilft Entwicklern, Kosten zu optimieren, indem einfachere Aufgaben zu günstigeren Modellen geleitet werden, während leistungsstarke Modelle für anspruchsvolle Arbeiten reserviert bleiben. Nutzen Sie sie zusammen mit Routenzuordnungen, wenn die Aufgabennatur klar ist, um die Lücke zwischen theoretischer und beobachteter Implementierungseffizienz zu schließen.
Schnellinstallation
Claude Code
Empfohlennpx 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-mappingKopieren Sie diesen Befehl und fügen Sie ihn in Claude Code ein, um diese Fähigkeit zu installieren
Dokumentation
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 Repository
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