token-budget-monitor

What This Skill Does

The token-budget-monitor skill provides essential cost control and usage visibility for users running automated LLM workflows via OpenClaw. In environments where cron jobs trigger frequent AI interactions, monitoring token consumption is critical to avoid unexpected API costs and performance bottlenecks. This utility allows you to track inputs and outputs across various sessions, enforce hard limits for specific tasks, and receive proactive alerts before you burn through your budget. By maintaining a centralized record of token usage, the tool helps developers optimize their AI calls and identify which tasks consume the most resources.

Installation

To integrate this utility into your OpenClaw environment, execute the following command in your terminal:

clawhub install openclaw/skills/skills/aviclaw/token-budget-monitor

Ensure that you have appropriate write permissions for the directory where the usage logs will be stored. After installation, verify the setup by running the status command to ensure the configuration file is correctly parsed.

Use Cases

This skill is perfect for automated content pipelines, such as scheduled social media posting or RSS summarization bots. It is particularly valuable for developers managing multi-agent systems where several scripts run concurrently. You can use it to gate-keep high-cost models, automatically falling back to more efficient or free models when specific budget thresholds are hit, or simply to generate analytical reports on the cost-per-job efficiency of your automation workflows.

Example Prompts

1. "OpenClaw, please run the token-budget-monitor status command to see how close I am to my daily usage limit for the morning news summary job."
2. "Check if the daily-tweet job has exceeded its 5,000 token allowance and provide a recommendation for a cheaper model if it has."
3. "Can you set an alert threshold of 90% for my total monthly budget and report any jobs that are currently nearing that limit?"

Tips & Limitations

To maximize the utility of this skill, ensure your cron jobs are configured to report usage statistics immediately after each LLM call. The current implementation relies on local file storage for logs; therefore, you should ensure that your server-side backup strategies include these data files to maintain persistent history. Note that this tool does not automatically cancel remote API requests; it provides the monitoring logic to trigger your own shutdown or fallback procedures. Always keep your freeModel list updated in the config.json file to take advantage of the latest efficient models provided by your API endpoints.