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

   🛡 Quality Optimization

   • Error prevention: Common error patterns with preventive measures
   • Tool reliability: Tools with high failure rates and alternatives
   • Validation gaps: Sessions lacking verification steps
   • Recovery strategies: Better error handling patterns

   🔄 Workflow Optimization

   • Session sizing: Optimal session scope based on historical success
   • Task decomposition: Complex sessions that should be split
   • Automation candidates: Repetitive workflows to automate
   • Knowledge reuse: Patterns where previous session context could help

3. Quantify each recommendation:

   • Estimated impact (cost savings $, time savings %, error reduction %)
   • Implementation effort (low/medium/high)
   • Confidence level based on data available
   • Priority score = Impact × Confidence / Effort

Output Format

Present as a prioritized optimization plan:

For the top 5 recommendations, include:

• Detailed explanation with supporting data
• Step-by-step implementation guide
• Expected before/after metrics
• How to measure success