/optimization-suggest

name: optimization-suggest
description: >
  Suggest concrete optimizations for Claude Code usage based on historical
  session data. Covers cost reduction, speed improvement, error prevention,
  and workflow efficiency. Use for data-driven optimization planning.

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:

| # | 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