Optimization Suggest

Generate data-driven optimization recommendations for Claude Code usage.

Input

The user provides: $ARGUMENTS

This may be:

• "all" or empty (default: comprehensive optimization scan)
• "cost" for cost reduction focus
• "speed" for performance/speed focus
• "quality" for error reduction focus
• "efficiency" for workflow efficiency focus

Procedure

1. Gather optimization data from http://localhost:4820:

   • GET /api/sessions?limit=200, session history
   • GET /api/analytics, tool and token analytics
   • GET /api/pricing/cost, cost data
   • GET /api/pricing, pricing rules for model comparison
   • Sample event streams for behavioral analysis

2. Analyze optimization opportunities:

   💰 Cost Optimization

   • Model downgrade opportunities: Tasks completed with expensive models that could use cheaper ones
     • Compare success rates per model per task type
     • Calculate savings from model substitution
   • Cache optimization: Sessions with low cache hit rates
     • Identify sessions that could benefit from better prompt caching
   • Early termination: Sessions that ran longer than needed
     • Detect sessions where useful work completed well before session end
   • Compaction reduction: Sessions hitting context limits
     • Suggest breaking large tasks into smaller sessions

   ⚡ Speed Optimization

   • Tool selection: Faster alternatives for commonly-used tool patterns
   • Subagent parallelization: Tasks that could run in parallel
   • Session planning: Better upfront context to reduce back-and-forth
   • Preemptive context loading: Frequently needed files/context