context-optimizer

You can analyze agent context window usage by parsing debug logs to profile tokens and detect redundancy, which identifies token waste and produces prioritized optimization reports.

Context optimization improves LLM efficiency by auditing token usage, detecting duplicate file reads, and recommending hand-off points to reduce context waste and lower processing times.

To reduce token waste from duplicate file reads, context optimization audits agent instructions to detect redundancy and suggests narrowing instruction file globs for more efficient loading.

Yes, you need Python 3.10 or higher to run the log parser script that audits debug logs, profiles tokens, and analyzes hand-off gaps for context optimization.

You should extract heavy tasks to subagents when debug logs show long processing times and excessive context loading, which this Skill identifies through token profiling and hand-off gap analysis.

The best way to pinpoint slow agent response times is to audit debug logs for excessive context loading, which identifies token waste and generates prioritized optimization recommendations.