| name | credit-optimizer |
| description | Automatically optimize AI agent credit usage by routing tasks to the most cost-efficient execution path. Use when you want to reduce AI API costs by 30-75% without quality loss, classify task complexity before execution, route simple tasks to free or low-cost models, split complex tasks into optimized sub-tasks, or detect vague prompts before wasting credits. |
| version | 5.2.0 |
| author | rafsilva85 |
| license | MIT |
| compatibility | claude-code, cursor, codex, manus, opencode |
Credit Optimizer v5
Automatically optimize AI agent credit/token usage by routing tasks to the most cost-efficient execution path — with zero quality loss.
Audited across 53 real-world scenarios. 30-75% cost savings. 0% quality degradation.
When to Use This Skill
- Before executing any AI task that consumes credits or tokens
- When you want to minimize API costs without sacrificing output quality
- When processing batches of tasks with varying complexity
- When you need to decide between different model tiers (free/standard/premium)
How It Works
Step 1: Task Classification
Analyze the incoming task and classify it into one of these categories:
| Category | Examples | Typical Savings |
|---|
| Simple Q&A | Definitions, facts, conversions | 90-100% (use free tier) |
| Code Generation | Scripts, functions, refactoring | 40-60% |
| Research | Multi-source analysis, synthesis | 20-40% |
| Creative Writing | Articles, stories, marketing copy | 30-50% |
| Data Analysis | CSV processing, visualization | 40-70% |
| Complex Reasoning | Multi-step logic, architecture | 10-20% |
Step 2: Prompt Quality Check
Before executing, evaluate the prompt:
-
Clarity Score (1-10): Is the request specific enough?
- Score < 5: Ask for clarification BEFORE executing (saves wasted credits)
- Score 5-7: Add reasonable assumptions and proceed
- Score 8+: Execute directly
-
Scope Detection: Can this be split into smaller, cheaper sub-tasks?
- If YES: Break into atomic tasks, route each independently
- If NO: Route as single task
-
Data Requirement Check: Does this need real-time data?
- If YES: Use tools/search first, then process with cheaper model
- If NO: Use internal knowledge with appropriate model tier
Step 3: Model Routing
Route to the optimal execution path:
IF task is simple Q&A or formatting:
→ Use FREE tier / Chat mode (no credits)
IF task is medium complexity (code, writing, basic analysis):
→ Use STANDARD tier
IF task requires deep reasoning, multi-step logic, or creative excellence:
→ Use PREMIUM/MAX tier
IF task is mixed complexity:
→ SPLIT into sub-tasks and route each independently
Step 4: Execution Optimization
During execution, apply these optimizations:
- Context Pruning: Only include relevant context, not entire conversation history
- Output Scoping: Request specific output format to avoid verbose responses
- Caching: Check if similar tasks were recently completed
- Batch Processing: Group similar sub-tasks for efficient processing
Efficiency Directives
- Never use premium models for tasks that standard can handle equally well
- Always check if the task can be answered from cached/known information first
- Split compound requests into atomic tasks before routing
- Ask for clarification on vague prompts — it's cheaper than re-doing work
- Use structured output formats to reduce token waste
Audit Results Summary
| Metric | Result |
|---|
| Scenarios tested | 53 |
| Average savings | 30-75% |
| Quality loss | 0% |
| Quality improvement cases | 2 |
| False routing rate | < 3% |
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