Save your main context to raise quality and cut cost — building Context Drop

Описание и применение

Контекстный дроп позволяет изолировать большие данные от основного диалога Claude Code, уменьшая токен‑стоимость и устраняя ошибки вызова инструментов.

Задача

Using Claude Code with heavy context, ultracode fan‑outs, and large logs caused tool‑call corruption, silence, and high token costs.

Результат

Context Drop reduced token costs by up to 100× for large logs, eliminated tool‑call corruption, and restored model responsiveness after marathon sessions.

Рабочий процесс

• 1. Identify heavy context usage (large logs, screenshots, many agents).
• 2. Install Context Drop desktop tool.
• 3. Configure Context Drop to route raw context to a subagent.
• 4. Use /compact only for the main conversation, not for raw data.
• 5. Monitor token usage and model stability.
• 6. Adjust ultracode and effort settings per task.
• 7. Iterate to keep main context under a safe size.

Доказательства

If I had to name the one factor most consistently present, it's context bloat.

Источник

The result: the conversation snapped back toward its original size immediately after the compact

Источник

By the estimate on record, reading a 20k-token log once means paying for about 85k tokens over the next 30 turns.

Источник

Context Drop is a small desktop tool I built to cut exactly one habit at the root: pouring raw context directly into the main conversation.

Источник