A high-precision utility for estimating and monitoring LLM token consumption across multiple model providers to optimize API costs.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install token-estimator
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install token-estimator using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Token Estimator?
Token Estimator is a professional-grade developer tool designed to provide transparency and cost control for AI-driven applications. As part of the Openclaw Skills ecosystem, it allows developers to accurately predict token usage for both input prompts and expected outputs before executing expensive API calls.
The skill supports a wide range of industry-standard tokenizers, including tiktoken and transformers, ensuring that whether you are using OpenAI, Google Gemini, or Alibaba Qwen, your estimates remain within a tight 3.5% error margin. It bridges the gap between raw text and API costs, enabling a probe-and-act workflow that reduces entropy in AI deployments.
Token Estimator Use Cases
- Pre-calculating API costs for large-scale document processing.
- Monitoring monthly and weekly token quotas through a visual water meter.
- Optimizing long-context prompts by identifying high-consumption segments.
- Deciding when to apply 4D compression to save up to 70% on token costs.
- Debugging tokenizer differences between different LLM providers.
How Token Estimator Works
- The skill detects the specific model identifier (e.g., gpt-4, qwen-plus) provided in the command or configuration.
- It dynamically selects the appropriate tokenizer library, such as tiktoken for OpenAI/Gemini or AutoTokenizer for Qwen.
- It processes the input text, including system prompts and conversation history, to calculate the exact input token count.
- It applies a predictive algorithm to estimate output tokens based on text length categories (Short, Medium, Long).
- A comprehensive report is generated, displaying the total estimated consumption and potential savings if optimization features are enabled.
Token Estimator Setup
To install the skill and its required Python dependencies for Openclaw Skills, run the following commands:
# Install via ClAWHub
clawhub install token-estimator
# Install required Python libraries
pip3 install tiktoken transformers dashscope
Token Estimator Data Schema & Taxonomy
Token Estimator organizes its analytics and metadata using the following structure for clarity and precision:
| Attribute | Description | Data Type |
|---|---|---|
model_name |
The detected or specified LLM identifier | String |
input_tokens |
Calculated tokens for the prompt and context | Integer |
output_range |
Predicted range for model response tokens | String (Range) |
precision |
The estimated error margin based on the tokenizer used | Percentage |
usage_stats |
Periodical tracking (5h, Day, Week, Month) | Object |
Token Estimator Advanced Features
- Multi-Model Tokenizer Support: Native integration with tiktoken (cl100k_base) and HuggingFace Transformers.
- Visual Token Water Meter: Real-time progress bars for monitoring usage against defined quotas.
- 4D Compression Suggestions: Automatic detection of long texts with triggers to suggest 70% token savings.
- Local-First Privacy: All calculations are performed on the local machine without uploading sensitive text to external servers.
- Smart Fallback Mechanism: Uses a character-to-token ratio estimation for unknown models to ensure continuous operation.
SKILL.md
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