usage-limit-reducer

To reduce Claude Code token usage, you can analyze local JSONL conversation logs to diagnose re-reading patterns and apply targeted fixes like compacting history or clearing context. This approach identifies where your spending actually goes to cut token waste with real usage data.

The best way to track Claude Code token costs is by running a local usage-report script over your JSONL logs in ~/.claude/projects. This generates a token breakdown by model, project, and day, including cache-hit rate, to show exactly where your spending goes without sharing network data.

Yes, you need Python3 to analyze local JSONL logs for token spend patterns, as the diagnostic script relies on standard Python libraries including argparse, json, datetime, and collections. These dependencies parse your local Claude Code conversation history to calculate usage metrics.

You should use /compact or /clear during long chats or rapid token consumption when diagnostics indicate excessive history re-reading. A local log analysis recommends these actions only when they are the most relevant rules for your current session, ensuring you avoid unintended automatic changes.

Switching to a cheaper model helps with Claude Code cost reduction when diagnostics identify model inefficiency for simple edits. By interpreting your model mix from local JSONL logs, you can determine if switching models is a relevant action to lower your overall token spend.

You can diagnose Claude Code usage analytics entirely locally without sharing data externally. The analysis runs a bundled script over your local ~/.claude/projects/*/*.jsonl files, interpreting cache-hit and model mix to recommend next actions without any network data sharing.