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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:

1. 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

2. 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

3. 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

1. Never use premium models for tasks that standard can handle equally well
2. Always check if the task can be answered from cached/known information first
3. Split compound requests into atomic tasks before routing
4. Ask for clarification on vague prompts — it's cheaper than re-doing work
5. 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% |

Links

• Website: creditopt.ai
• GitHub: github.com/rafsilva85/manus-credit-optimizer
• MCP Server: Available as Python MCP server for programmatic integration
• Full Manus Skill: Available at Gumroad ($29, one-time)