Claude Skill

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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Download hoangsonww-claude-code-agent-monitor-plugins_ccam-insights_skills_optimization-suggest-83d4df5.zip · 1 KB

hoangsonww/claude-code-agent-monitor 1014 238 forks MIT Updated 1d ago

Part of hoangsonww/claude-code-agent-monitor — 86 skills

Install

skills CLI

npx skills add https://github.com/hoangsonww/Claude-Code-Agent-Monitor/tree/master/plugins/ccam-insights/skills/optimization-suggest

Claude Code

claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install hoangsonww-claude-code-agent-monitor@llmmart

Git git clone https://github.com/hoangsonww/Claude-Code-Agent-Monitor.git

The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole hoangsonww/claude-code-agent-monitor collection as a plugin from our marketplace. Git is the plain clone.

Skill manifest

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

Files (claude-code-agent-monitor)

agents

openai.yaml 279 B

interface:
  display_name: "Optimization Suggest"
  short_description: "Suggest concrete optimizations for Claude Code usage based..."
  default_prompt: "Use $optimization-suggest to inspect CCAM data and complete this workflow safely."
policy:
  allow_implicit_invocation: true

SKILL.md 3.3 KB

---
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