Estimate token counts for files using word count multiplied by 1.8.
npx skills add https://github.com/xg-gh-25/SwarmAI --skill estimate-tokens
Please help me install this Agent Skill. Skill: estimate-tokens Source: https://github.com/xg-gh-25/SwarmAI/tree/main/backend/skills/s_estimate-tokens Command: npx skills add https://github.com/xg-gh-25/SwarmAI --skill estimate-tokens
This Skill helps teams estimate token counts for files to plan context window usage and avoid overflows, reducing guesswork and preventing costly failures in AI workflows.
Run the bundled script on a target file to obtain a token estimate.
High-intent search queries and answers about installing and using this skill.
Token count estimation for files is performed by a bundled script that counts words and multiplies by 1.8. This deterministic approach provides formatted output to help you plan context window usage and prevent overflows before AI processing.
The best way to budget context window tokens for large documents is using a deterministic word-count script. It multiplies the word count by 1.8 to estimate tokens, enabling per-project token budgeting and preventing costly AI workflow failures.
You can check if files fit within a 200k token context by running the bundled estimation script. It computes token estimates from file word counts to ensure your content stays within context limits before sending it to an AI agent.
The token estimation mechanism works by counting the total words in a target file and multiplying that number by 1.8. This deterministic calculation provides a quick token estimate formatted for easy consumption during AI preprocessing.
A limitation of using a word count formula for estimating tokens is its reliance on a fixed 1.8 multiplier, which provides an approximation rather than an exact tokenizer count. This means it is best for capacity planning, not precise billing.
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MassimilianoPiliImplement fixed-size, recursive, structure-aware, and semantic chunking for RAG systems.
girijashankarjEstimate token usage for files or directories using a word-count heuristic.
