Optimization suggest
Skill hoangsonww/Claude-Code-Agent-Monitor/plugins/ccam-insights/skills/optimization-suggest
🚀 A real-time monitoring dashboard for Claude Code, built with SQLite3, Node.js, Express, React, Vite, TailwindCSS, and WebSockets. It tracks sessions, agent activity, tool usage, and subagent orchestration, providing live analytics, a Kanban status board, status notifications, a cute buddy, and an interactive web UI/MacOS/Windows native app.
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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.
SKILL.md
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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 historyGET /api/analytics— tool and token analyticsGET /api/pricing/cost— cost dataGET /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
- Model downgrade opportunities: Tasks completed with expensive models that could use cheaper ones
-
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