Claude Code announced a tool that can condense any command’s output to roughly 10% of its original token count, promising up to 90% cost savings see tweet.
What the tool does
The utility sits between a shell command and an AI agent, summarizing the raw output into a concise representation. Instead of feeding tens of thousands of tokens into Claude Code, Cursor, Copilot, or Antigravity, the agent receives only the essential facts. The condensation algorithm keeps key metrics, error codes, and actionable lines while dropping verbose logs and decorative text.
Integration points
The tool is language‑agnostic and can be piped into any command line. A typical usage pattern is:
$ my-build.sh | condense-output | copilot --prompt "Analyze build results"
It works out‑of‑the‑box with the four agents mentioned in the announcement. No SDK changes are required; the tool reads stdin and writes the shortened JSON payload to stdout. Existing CI pipelines can adopt it with a single wrapper script.
Cost impact and trade‑offs
Reducing token volume directly lowers API spend, especially for models priced per‑token. Early reports suggest a 70‑80% reduction in monthly bills for heavy‑log use cases. However, the summarization may drop context needed for deep debugging, leading to false‑positive alerts or missed edge‑case information. Teams should validate the condensed output against full logs during a trial period to gauge noise levels.
When to try it
If your startup’s AI‑augmented tooling spends more than $200 /month on token usage for log analysis, add the condenser to a non‑critical pipeline for one sprint and compare costs versus diagnostic fidelity. Watch for any missed error details; if they become a blocker, consider a hybrid approach—send full logs for failures only.
What to watch: upcoming updates may add configurable compression levels, letting you tune the trade‑off between token savings and context depth.