Count Dataset Tokens

The count-dataset-tokens skill enables AI agents to calculate the total token count within a specified dataset. This capability allows agents to estimate context window usage and manage input sizes effectively before processing large volumes of text data.

When to use it

• Context Window Management: Determine if a specific document or file fits within the model's maximum context limits.
• Cost Estimation: Calculate potential API costs associated with processing a batch of documents based on token volume.
• Data Sampling: Identify representative subsets of data by analyzing total token distribution across a dataset.
• Prompt Engineering: Verify that combined system instructions and user inputs do not exceed operational thresholds.

Key capabilities

• Accepts file paths or raw text strings as input targets.
• Returns precise integer values representing the total token count.
• Supports various text encodings commonly used in LLM pipelines.
• Integrates seamlessly with Letta AI agent workflows for automated data validation.

Example prompts

"Count the tokens in this customer support log file: [path/to/logs.json]" "Estimate how many tokens are required to process a 50-page PDF report." "Check if the combined token count of my uploaded documents exceeds 128k."

Tips & gotchas

Token counting algorithms may vary slightly depending on the underlying tokenizer model; ensure consistency when comparing counts across different runs. Always verify the specific encoding settings used by your agent configuration to match the counting logic accurately.