Credit Optimizer v5
AI-powered credit optimization for Manus AI. Reduces credit consumption by 30-75% through intelligent model routing, context hygiene, and smart task processing. Audited across 53 scenarios with zero quality loss.
README
Credit Optimizer v5 for Manus AI
<!-- mcp-name: io.github.rafsilva85/credit-optimizer -->
Save 30-75% on Manus AI credits with zero quality loss. ~55% average savings. Audited across 53 adversarial scenarios, 200+ tasks verified. Works as MCP server (free) or native Manus Skill ($9).
Available on: PyPI · MCP Registry · Smithery · GitHub · Landing Page
The Problem
Manus AI charges credits per task. Most users waste 30-75% of their credits because:
- Simple tasks run in Max mode when Standard would produce identical results
- Prompts contain redundant context that inflates token usage
- Tasks that could be batched are executed one by one
- Output formats are not optimized for the task type
Credit Optimizer fixes all of this automatically.
How It Works
Your Prompt
│
▼
┌──────────────────────────────────────────┐
│ Credit Optimizer v5 │
│ │
│ 1. Intent Classification (12 categories)│
│ 2. Complexity Scoring │
│ 3. Model Routing (Standard vs Max) │
│ 4. Prompt Compression │
│ 5. Batch Detection │
│ 6. Context Hygiene │
│ 7. Output Format Optimization │
│ │
│ Result: Optimized strategy + savings % │
└──────────────────────────────────────────┘
│
▼
Same quality output, fewer credits
Demo
> analyze_prompt("Build me a React dashboard with charts, auth, and database backend")
╔══════════════════════════════════════════════════════════════╗
║ CREDIT OPTIMIZER v5 — Analysis Report ║
╠══════════════════════════════════════════════════════════════╣
║ ║
║ Intent: code_generation (complex, multi-component) ║
║ Model: Max mode ✓ (correct for this complexity) ║
║ Savings: 35-45% estimated ║
║ Quality: 0% loss ║
║ ║
║ Strategy: Split into 3 sequential tasks ║
║ ┌──────────────────────────────────────────────────────┐ ║
║ │ Task 1: Database schema + API routes (Standard) │ ║
║ │ Task 2: Authentication flow (Standard) │ ║
║ │ Task 3: React dashboard + charts (Max) │ ║
║ └──────────────────────────────────────────────────────┘ ║
║ ║
║ Optimizations applied: ║
║ ✓ Model routing: Tasks 1-2 downgraded to Standard ║
║ ✓ Batch detection: 3 focused tasks vs 1 monolithic ║
║ ✓ Context hygiene: Removed redundant specifications ║
║ ✓ Output format: Structured code blocks per component ║
║ ║
╚══════════════════════════════════════════════════════════════╝
> analyze_prompt("Translate this paragraph to Spanish")
╔══════════════════════════════════════════════════════════════╗
║ CREDIT OPTIMIZER v5 — Analysis Report ║
╠══════════════════════════════════════════════════════════════╣
║ ║
║ Intent: translation (simple) ║
║ Model: Standard mode ✓ (Max unnecessary) ║
║ Savings: 60-70% estimated ║
║ Quality: 0% loss ║
║ ║
║ Recommendation: Use Standard mode ║
║ Translation tasks produce identical quality in Standard. ║
║ No splitting needed — single atomic task. ║
║ ║
╚══════════════════════════════════════════════════════════════╝
Real Results
| Metric | Value |
|---|---|
| Credit savings range | 30–75% |
| Average savings (across all task types) | ~55% |
| Quality loss | 0% |
| Real tasks analyzed | 200+ |
| Adversarial test scenarios | 53 (all passing) |
| Vulnerabilities found & fixed | 12 |
Quick Start
Option 1: MCP Server (Free)
Works with Claude Desktop, Cursor, Windsurf, Copilot, and any MCP-compatible client.
# Install from PyPI (recommended)
pip install mcp-credit-optimizer
python -m mcp_credit_optimizer
Or install from source:
git clone https://github.com/rafsilva85/credit-optimizer-v5.git
cd credit-optimizer-v5
pip install -e .
python -m mcp_credit_optimizer
Add to your MCP config (claude_desktop_config.json or equivalent):
{
"mcpServers": {
"credit-optimizer": {
"command": "python",
"args": ["-m", "mcp_credit_optimizer"]
}
}
}
Option 2: Manus Skill (Native Integration)
The Manus Skill runs automatically on every task — no manual prompting needed.
One-time payment. Lifetime updates. 30-day money-back guarantee.
MCP Tools
| Tool | Description |
|---|---|
analyze_prompt |
Analyze a prompt and get optimization recommendations with estimated savings |
get_optimization_strategy |
Get detailed strategy with model routing, prompt compression, and batch detection |
get_golden_rules |
Get the 10 golden rules for credit-efficient Manus usage |
Audit Results
All 53 test scenarios pass with zero quality degradation:
| Category | Scenarios | Quality Loss |
|---|---|---|
| Code generation (Python, JS, React, SQL) | 12 | 0% |
| Creative writing (blog, marketing) | 8 | 0% |
| Data analysis (CSV, JSON, API) | 7 | 0% |
| Research (multi-source synthesis) | 6 | 0% |
| Translation & localization | 5 | 0% |
| Bug fixing & debugging | 5 | 0% |
| Documentation generation | 5 | 0% |
| Mixed-intent tasks | 5 | 0% |
Why Pay When the MCP Server Is Free?
The Manus Skill gives you:
- Auto-activation — runs on every task without you remembering to use it
- Native integration — works inside Manus, not as an external tool
- Priority updates — get new optimization patterns first
- One-time $9 payment — no subscription, yours forever
The MCP server saves you credits when you remember to use it. The Manus Skill saves you credits on every single task automatically.
Community Feedback
"Excellent advice" — u/Business_Cheetah_689 on the optimization strategies
"This is exactly what I needed. Was burning through credits way too fast." — Reddit user
Contributing
Issues and PRs welcome! If you find a scenario where the optimizer reduces quality, please open an issue with the prompt and expected output.
License
MIT License — use it freely in personal and commercial projects.
<p align="center"> <strong>Built by <a href="https://github.com/rafsilva85">Rafael Silva</a></strong><br> <a href="https://creditopt.ai">creditopt.ai</a> · <a href="https://rafaamaral.gumroad.com/l/credit-optimizer-v5">Gumroad</a> · <a href="https://github.com/rafsilva85/credit-optimizer-v5">GitHub</a> </p>
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。