零负面影响。平均节省 47%。约 27 次提示即可回本。 已在 53 个对抗性场景中进行审计。质量否决规则：如果无法在不损失质量的情况下节省额度，它就不会触碰你的任务。
获取方式： 落地页 · Gumroad ($9) · 动力套装 ($12) · SkillFlow 免费 MCP 服务器： PyPI · Smithery
Dev.to · PulseMCP · Smithery · MCPServers.org · Agensi.io · Indie Hackers · Awesome Manus AI
Manus 按任务收取额度。由于以下四种默认行为，大多数用户浪费了 30–75% 的额度：
问题 | 发生情况 | 浪费 |
模型路由错误 | 当标准模式能产生相同结果时，简单任务却在 Max 模式下运行 | 高达 5 倍超额支付 |
浏览器导航缓慢 | 每个页面通过浏览器工具调用需要 8–45 秒，在等待时间上消耗额度 | 每 10 个 URL 浪费 150+ 秒 |
无聊天检测 | 在聊天模式下成本为 $0 的任务（问答、头脑风暴、翻译）却在代理模式下运行 | 100% 超额支付 |
上下文臃肿 | Token 在各步骤间累积 — 后续每一步的成本都比前一步高 | 指数级增长 |
这些不是 Bug，而是默认设置。Power Stack 会覆盖所有这四项设置。
Related MCP server: ComputeGauge MCP
这是最重要的一点：
Power Stack 绝不会让情况变糟。 它内置了硬编码的“质量否决规则”：如果某项优化会导致输出质量下降哪怕 1%，它就会完全跳过该优化。最坏的情况下，它什么都不做。最好的情况下，它能为你节省 75%。
在任何情况下，安装 Power Stack 都不会产生比不安装更差的结果。
在执行前分析每个提示，并确定能提供相同质量的最廉价路径。
意图分类：涵盖 12 个类别（代码、研究、创意、数据、翻译等）
模型路由：标准模式 vs Max 模式 — 仅在复杂性确实需要时才使用 Max 模式
聊天模式检测：将简单问答路由至聊天模式（$0 成本）
批处理检测：对相关任务进行分组以减少开销
上下文清理：压缩累积的上下文以保持 Token 成本线性增长
用程序化工具包取代缓慢的浏览器工具调用。
httpx + selectolax 取代 Playwright 开销（速度快 3–2000 倍）
浏览器 Cookie 桥接：用于已认证站点
异步并行获取：10 个 URL 仅需 1.3 秒，而非 150+ 秒
智能磁盘缓存：带 TTL — 重复访问即刻完成
Credit Optimizer 决定优化什么。Fast Navigation 决定执行速度。两者结合，效果倍增：
指标 | 无 Stack | 有 Stack | 提升 |
简单问答成本 | 全额 | $0 (聊天模式) | 节省 100% |
10 个 URL 研究 | 150+ 秒 | 1.3 秒 | 速度提升 115 倍 |
网页抓取任务 | 全额额度 | 原额度的 33% | 节省 67% |
全栈 Web 应用 | 全额额度 | 原额度的 40% | 节省 60% |
研究报告 | 12 分钟 | 2 分钟 | 速度提升 6 倍 |
所有任务平均值 | 基准 | 减少 47% | 节省 47% |
Power Stack 一次性收费 $9（或 $12 包含 Fast Navigation 的套装）。回本速度如下：
计划 | 月度成本 | 47% 浪费 | 每日浪费 | 回本 |
Plus ($39/月) | 6,500 额度/天 | ~3,055 额度/天 | ~$0.44/天 | ~20 次提示 |
Max ($99/月) | 16,250 额度/天 | ~7,637 额度/天 | ~$0.92/天 | ~10 次提示 |
团队版 ($79/用户/月) | 不定 | ~47% | 不定 | 1–2 天 |
回本后，每次提示都在为你省钱。在你的 Manus 订阅期内。永远有效。
年度节省预估：约 $500–$1,000+，具体取决于使用量。
将技能文件复制到 ~/skills/credit-optimizer/ 和 ~/skills/fast-navigation/
两者会在每个任务上自动激活 — 无需配置
pip install mcp-credit-optimizer
python -m mcp_credit_optimizer添加到你的 MCP 配置中：
{
"mcpServers": {
"credit-optimizer": {
"command": "python",
"args": ["-m", "mcp_credit_optimizer"]
}
}
}适用于 Claude Desktop、Cursor、Windsurf、Copilot 以及任何兼容 MCP 的客户端。
既然 MCP 服务器免费，为什么要付费？ MCP 服务器仅在你记得调用它时才节省额度。而 Manus Skill 会在每一个提示上自动节省额度 — 无需手动调用。该 Skill 还包含 Fast Navigation（115 倍速度提升），这是 MCP 版本所不具备的。
所有 53 个测试场景均通过，且质量零下降：
类别 | 场景数 | 质量损失 |
代码生成 (Python, JS, React, SQL) | 12 | 0% |
创意写作 (博客, 营销) | 8 | 0% |
数据分析 (CSV, JSON, API) | 7 | 0% |
研究 (多源综合) | 6 | 0% |
翻译与本地化 | 5 | 0% |
Bug 修复与调试 | 5 | 0% |
文档生成 | 5 | 0% |
混合意图任务 | 5 | 0% |
“每个人都讨厌 Manus 基于额度的系统。额度消耗的速度简直疯狂。” — Reddit, 90 个赞
“我一周半就用了 100,000 额度。我一个月的配额全没了。” — Reddit
“Manus 贵得离谱。每月 $200，额度还是会用完。” — Reddit
Power Stack 的存在是因为这些挫败感是真实的，而且是可以解决的。
Awesome Manus AI — 精选的 Manus AI 资源、工具和技巧列表
2026 定价指南 — Manus AI 定价完整解析
标准模式 vs Max 模式指南 — 何时使用哪种模式
教程 — 完整新手指南
术语表 — 30+ 个 Manus AI 术语解释
路线图 — 未来规划
渠道 | URL |
落地页 | |
购买单个 ($9) | |
购买套装 ($12) | |
SkillFlow | |
PyPI (免费 MCP) | |
Smithery | |
PulseMCP | |
Awesome 列表 |
MIT — 详情请参阅 LICENSE。
由 Rafael Silva 构建 · creditopt.ai
Analyze an AI agent prompt and return optimization recommendations.
Returns strategy, model recommendation, estimated credit savings, quality impact assessment, and efficiency directives.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | The user's prompt/task description to analyze |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It mentions what it returns but lacks details on side effects, idempotency, or prerequisites.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with action and result. Every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given one parameter and presence of output schema, description covers purpose and what is returned completely.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema coverage, baseline is 3. Description does not add meaning beyond the schema's description of the 'prompt' parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it analyzes an AI agent prompt and returns optimization recommendations, listing specific return fields. Distinguishes from siblings get_golden_rules and get_strategy_for_task.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied but not explicitly stated. No guidance on when to use this tool versus siblings or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the 10 Golden Rules for credit optimization with ZERO quality loss.
Returns: The 10 audited golden rules with explanations
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the full burden of behavioral disclosure. It does not confirm the tool is read-only or safe, nor does it describe any side effects, authorization needs, or rate limits. The claim of 'ZERO quality loss' pertains to the rules, not the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is only two sentences, front-loads the purpose, and contains no extraneous information. Every word serves a purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (no parameters, clear retrieval) and the existence of an output schema, the description adequately explains what the tool returns: 'the 10 audited golden rules with explanations.' No further context seems necessary.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters and 100% coverage, so baseline is 4. The description does not need to add parameter-level detail, and it appropriately confirms no arguments are required.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Get the 10 Golden Rules for credit optimization with ZERO quality loss,' which specifies the exact resource (10 Golden Rules) and the action (Get). This distinguishes it from sibling tools analyze_prompt and get_strategy_for_task.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, nor does it mention any prerequisites or exclusions. It simply states what it returns.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the optimal strategy for a specific task type.
| Name | Required | Description | Default |
|---|---|---|---|
| task_type | Yes | One of: qa, code, research, content, data_analysis, media, automation |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It only states it 'gets' a strategy, but does not disclose any behavioral traits like whether it is read-only, if there are side effects, or what the output entails. Minimal behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, concise and front-loaded. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Simple tool with one param and output schema, but description does not mention output or any constraints. Lacks context about behavior and return value, despite output schema existing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has one parameter with description listing allowed values, coverage is 100%. Description adds no additional meaning beyond schema. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the verb 'get' and resource 'strategy' for a specific task type. Input schema defines task_type with allowed values. It is distinct from sibling tools 'analyze_prompt' and 'get_golden_rules'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied but not explicitly stated. No guidance on when to use this tool versus alternatives, nor any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v5.0.0analyze_prompt1 field changedInput schema / properties / prompt / descriptionAdded value: +"The user's prompt/task description to analyze"get_strategy_for_task1 field changedInput schema / properties / task_type / descriptionAdded value: +"One of: qa, code, research, content, data_analysis, media, automation"v5.2.0analyze_promptget_golden_rulesget_strategy_for_taskScored across 3 tools
Each tool targets a distinct aspect of credit optimization: analyzing prompts, retrieving golden rules, and obtaining task-specific strategies. There is no overlap in functionality.
All tool names follow a consistent verb_noun pattern in snake_case (analyze_prompt, get_golden_rules, get_strategy_for_task), making them predictable and easy to understand.
With 3 tools, the server is slightly minimal but still well-scoped for its advisory purpose. Each tool serves a clear role, and the count is reasonable given the focused domain.
The tools cover the core functionalities of analysis, reference rules, and strategy selection. A minor gap is the lack of an execution tool, but for an advisory server this is acceptable.
Reduces AI Agent token usage by 40% via three-stage SOP workflow.
Cost-optimized LLM model routing recommendations for autonomous AI agents
Enforce AI budgets before the model call and track cost per customer across 10 providers.
AI image, video, audio and text tools for creators. Pay per use with credits.
