lirantal/tokenu — A unix-like du command line tool to count token usage per files and directories

Description and use

**Brief Summary (2‑3 sentences)**
lirantal/tokenu is a Unix‑like du utility that scans files and directories and reports the number of AI‑model tokens they contain. Written in TypeScript, it lets developers quickly gauge token usage across codebases, data sets, or prompt libraries, making it easier to size and cost‑estimate downstream LLM workflows. With 59 GitHub stars and recent activity (last updated 2026‑05‑14), it is a lightweight, open‑source tool for prototyping token‑aware AI pipelines.

**Value**

• **Immediate token visibility** – By treating token counts like disk usage, teams can spot hot‑spots, trim oversized prompts, and predict inference costs without writing custom parsers.
• **Accelerates AI feature prototyping** – Token metrics are essential for Retrieval‑Augmented Generation (RAG), prompt engineering, and agent design; tokenu supplies this data out‑of‑the‑box, letting developers focus on model logic rather than token bookkeeping.
• **Low integration friction** – The project ships a CLI, a Node.js SDK, and simple API hooks, so it can be dropped into CI pipelines, VS Code extensions, or runtime scripts with minimal code changes.

**Practical Adoption Path**

1. **Evaluation** – Clone the repo, run npx tokenu <path> on a sample directory to verify token counts align with the model you plan to use (e.g., GPT‑4 tokenizer).
2. **Integration** – Add the npm package (npm i @lirantal/tokenu) to your project, wrap the SDK call in a build step or CI job to generate token‑usage reports for each commit.
3. **Automation** – Use the CLI in pre‑commit hooks or GitHub Actions to enforce token‑size limits, alerting developers when a file exceeds a configurable threshold.
4. **Extension** – Combine the output with cost‑estimation tools or RAG indexing pipelines to automatically prune or chunk data based on token budgets.

**Production Readiness**

• **Maturity** – Medium. The tool is functional and actively maintained (last commit 2026‑05‑14) but has a modest community (59 stars, 1 fork), so extensive enterprise support is limited.
• **Dependencies & Maintenance** – Built in TypeScript with a small runtime footprint; review its dependency tree for known vulnerabilities before deployment.
• **Risk Considerations** – Verify the repository’s license (MIT‑style) and conduct a security audit of the tokenization library it wraps. With those checks, tokenu is suitable for internal prototypes, CI enforcement, and as a building block in production pipelines, but a fallback strategy (e.g., custom tokenizer) should be planned for critical services.

Problem

lirantal/tokenu helps add AI capability without starting from a blank model stack.

Outcome

Medium: useful for prototypes or internal workflows, with dependency and maintenance checks before production.

Workflow

• prototype AI features
• build RAG or agent workflows
• evaluate model tooling

Evidence

a unix-like du command line tool to count token usage per files and directories

Source