agent-expenditure
Monitor and optimize token consumption in multi-agent parallel scheduling, identifying waste.
Install & Use
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This Skill addresses the challenge of tracking and optimizing token consumption in multi-agent parallel scheduling scenarios. In practical development, developers often need to coordinate multiple AI agents to handle tasks simultaneously but lack effective tools to monitor each agent's resource usage, leading to cost waste and inefficiency.
Usage is straightforward: simply integrate this skill into your parallel scheduling framework. You can then use natural language instructions to query each agent's token usage details. The system automatically analyzes consumption patterns and flags inefficient or redundant calls, helping you quickly identify waste points.
It is well-suited for teams or individuals requiring fine-grained management of AI call costs, particularly developers who have deployed multi-agent systems to handle complex workflows (such as code generation, data analysis, content creation) or projects aiming to optimize the efficiency of existing parallel task execution.
It is recommended to introduce this tool early in a project to establish a cost baseline. Note that it primarily relies on the call logs provided by the scheduling framework, so ensure your agent system can output standardized token usage data.
Key Features
Unlike tools that only provide overall API usage statistics, it enables granular tracking at the level of each individual agent and can identify token waste caused by improper scheduling or task overlap within the context of parallel execution.
Limitations
Requires your agent scheduling system to provide detailed logs for each agent call (including model type, input/output token counts). It currently primarily supports runtime environments based on Bun/Node.js.
FAQ
How much token cost can this skill help me save?
The specific savings ratio depends on the waste level in your original workflow. By identifying and optimizing inefficient parallel calls, repeated context loading, etc., it typically can significantly reduce unnecessary overhead.
Which AI models or API providers does it support?
It does not interact directly with model APIs but analyzes token data logged by the scheduling framework. Therefore, as long as your framework can log token usage for mainstream models like Claude, Gemini, etc., it can perform the analysis.
Installation guide for AI assistants
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