A free, zero-dependency token consumption and cost analyzer that scans OpenClaw agent workspaces across 6 major language models.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install token-audit
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install token-audit using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
The Token Audit skill is a powerful, open-source utility designed specifically for OpenClaw developers and operators who need to gain clear visibility into their LLM token usage. It scans workspace files and provides accurate token estimations across six major model families: GPT-4, Claude 3, Gemini, Llama 3, Mistral, and Qwen. By surfacing critical data regarding hidden cost drivers, this skill helps developers pinpoint bloated context, redundant files, and massive optimization opportunities.
Operating with zero external dependencies and requiring no API keys, this skill functions as a privacy-centric, standalone script. It ensures that you understand exactly how your data footprints map to token metrics and commercial hosting costs before sending payloads to LLM providers, effectively extending the lifetime and budget of your automated workflows.
To start using the Token Audit skill within your environment, run the script from your terminal. No external packages or API keys are required.
python3 scripts/token-audit.py
python3 scripts/token-audit.py --workspace /path/to/workspace
python3 scripts/token-audit.py --json
The skill processes your local file layout and generates analytical metrics structured around the following categories:
| Data Point | Description |
|---|---|
| Total Token Count | Consolidated token count across all scanned files, customized per model family. |
| Per-File Breakdown | An ordered list of files, sorted descending by token size, to quickly locate content bloat. |
| Cost Estimation | Projected financial cost metrics computed for GPT-4, Claude 3, Gemini, Llama 3, Mistral, and Qwen. |
| Optimization Flags | Actionable recommendations pointing out files that have unusually high token costs vs expected value. |
--json flag, making it seamless to integrate into automated developer dashboards, CI/CD pipelines, or custom scripting pipelines.Loading
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