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optimization-suggest

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.

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Files included (2)

  • SKILL.md3.3 KB
  • agents/openai.yaml279 B

SKILL.md (original)

Review the instructions the agent will receive before you install.

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:

#RecommendationCategoryImpactEffortPriority
1Specific action💰/⚡/🛡/🔄HighLow★★★★★
2Specific 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

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