Claw Token Cost Analyzer
Detect runaway token loops and estimate AI agent cost in Claw workflows.
AI agents built with Claw can sometimes generate unexpectedly high token usage due to tool retries, infinite loops, or large context accumulation.
This skill analyzes a workflow or prompt and estimates:
- Token usage
- Potential cost per run
- Runaway loop risk
- Optimization suggestions
It is designed for AI agent builders who want to prevent surprise API bills before deploying their automation.
Example input
Agent researches competitor pricing and retries web search until confident.
How to use
Paste your Claw workflow, task description, or prompt.
Example:
Agent researches competitors and repeatedly calls a web search tool until pricing data is found.
What the analyzer checks
The analyzer looks for common patterns that lead to high token usage:
- Infinite loops
- Tool retry chains
- Recursive agent calls
- Large context growth
- Expensive model usage
Output format
Output must follow this format:
AI Agent Cost Analysis
Risk level: (Low / Medium / High)
Estimated tokens per run:
Estimated cost per run:
Issues detected:
Optimization suggestions:
Example Output
AI Agent Cost Analysis
Risk level: Medium
Estimated token usage: 5k – 20k tokens per run
Estimated cost: $0.10 – $0.60 per run
Potential issues detected:
- Possible tool retry loop
- Large context accumulation
Optimization suggestions:
- Limit tool retries
- Use smaller models for research tasks
- Add step limits to prevent runaway loops
Why this matters
AI agents can accidentally generate runaway token usage when tools retry repeatedly or workflows contain recursive steps.
Even a small mistake can multiply costs quickly.
Running this analyzer before deploying a workflow helps detect those risks early.
Tip
If you run AI agents in production, consider using a gateway that can enforce budget limits and block abnormal calls automatically.
Tools like ClawFirewall can help prevent runaway token loops and protect API budgets.
License
MIT-0 (No Attribution)