A few weeks ago, JetBrains independently benchmarked Caveman, a dumb little Claude skill I made to get coding agents to stop yapping and stop my AI from costing so much as a student. I’d been quoting roughly 65% fewer output tokens on prose in-chat style conversations. However across long-running coding tasks where most people were running Caveman, JetBrains measured 8.5%. Fair hit. I should have done this kind of testing too. But, they also found no measurable quality loss, so the grug-brained output style wasn’t hurting task performance. Their benchmark however exposed a bigger source of waste: Claude’s replies are naturally only one slice of a coding session’s context. Files, logs, tool output, browser and MCP responses, and old context all get fed into the model repeatedly. I rebuilt Caveman around that. It now runs as a local proxy under Claude Code, Codex, and other coding agents. It compresses input before each provider call while keeping the original content recoverable byte-for-byte. I pinned a benchmark across 54 runs. Provider-reported input tokens dropped 33.2%, and all 18 exact-answer checks passed. Also to solve the starting token cost of using Caveman, every single skill you have installed can run at 70% less tokens, while remaining 99% accurate by automatically loading it in as an image via Caveman. The command is caveman claude . Your usual Claude Code setup stays the same. Getting publicly dunked on by an independent benchmark turned out to be useful. It killed a weak claim and pushed me toward a much better product. Caveman is open source. If you can break the benchmark or find where this approach falls over, I want to hear about it. We launched this version today: https://www.producthunt.com/products/caveman submitted by /u/VeryVexxy
Originally posted by u/VeryVexxy on r/ClaudeCode