Optimization Suggest

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

• 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

• 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

• 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:

| # | Recommendation | Category | Impact | Effort | Priority |

|---|---------------|----------|--------|--------|----------|

| 1 | Specific action | 💰/⚡/🛡/🔄 | High | Low | ★★★★★ |

| 2 | Specific action | ... | ... | ... | ★★★★☆ |

For the top 5 recommendations, include:

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