tare

tare is a local Claude Code token forensics tool for developers who want to understand where their usage went. The public GitHub repository describes it as a token audit, limit diagnosis, and usage forensics project built from the Claude Code session logs already on the user’s machine. That source-backed positioning is narrow and useful: tare is not a general analytics suite, and it is not a hosted monitoring dashboard. It focuses on the practical question Claude Code users ask when a quota or usage limit arrives sooner than expected. The strongest fit is a builder who uses Claude Code heavily and needs more evidence than a monthly bill or a vague usage total. tare can help turn local session history into a clearer audit trail for token pressure, limit diagnosis, and usage patterns. That matters for solo developers who are trying to stay inside a plan, and it also matters for technical leads who need to understand whether AI coding spend is tied to a few large sessions, repeated retries, or normal day-to-day work. Setup should be treated like a developer workflow. The repository is the primary source, and users should follow the README against their own local Claude Code history. Because the project reads local logs, teams should still review what files are accessed, where any reports are written, and how those reports are shared. The appeal is that the core source says nothing has to leave the machine for the audit use case. Pricing is straightforward at the project layer. GitHub API metadata checked on 2026-08-30 listed tare as an MIT-licensed Python repository with 135 stars and 5 forks. That means the tool itself has no listed SaaS subscription. Claude Code usage, model access, and any separate services remain separate costs. The practical takeaway: try tare when Claude Code usage has become hard to explain. It is most useful before a team changes plans or limits access, because it gives users a local way to inspect evidence first. Adoption should start with a narrow test. Pick one realistic job, run it from the official source, and compare the output with the team's current process. Check permissions, data handling, provider keys, and maintenance signals before using it on sensitive work. A good first test is to run tare after a known heavy Claude Code session, then compare the report with what the developer remembers doing. Look for repeated edits, long context windows, and workflows that could be split into smaller sessions. That turns the tool from a curiosity into a planning aid for future AI coding work.