Skill456 repo starsupdated 2d ago

optimization-suggest

The optimization-suggest Claude Code skill analyzes session history, analytics, and pricing data from a local monitoring endpoint to generate targeted improvement recommendations across cost, speed, quality, and workflow efficiency. Users specify their optimization priority (all, cost, speed, quality, or efficiency), and the skill identifies actionable opportunities like model downgrades, cache improvements, parallelization options, and error prevention patterns, quantifying each recommendation with estimated impact and implementation effort.

View source Repository: Claude-Code-Agent-Monitor

Install in Claude Code

Copy

git clone --depth 1 https://github.com/hoangsonww/Claude-Code-Agent-Monitor /tmp/optimization-suggest && cp -r /tmp/optimization-suggest/plugins/ccam-insights/skills/optimization-suggest ~/.claude/skills/optimization-suggest

Then start a new Claude Code session; the skill loads automatically.

Definition

SKILL.md

# 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