context-engineer

To optimize context window token usage, you can audit workspace files like SKILL.md, SOUL.md, MEMORY.md, AGENTS.md, and TOOLS.md to identify token bloat and redundancy. This process generates a report with efficiency scores and actionable recommendations to reduce overhead.

Context engineering is the process of analyzing workspace state files to identify token bloat and redundancy. It improves AI agent efficiency by generating per-file breakdowns, computing efficiency scores, and providing optimization recommendations to maximize available context utilization.

You can audit workspace context files for token bloat by running an analysis script that parses files like MEMORY.md and AGENTS.md. The audit estimates token counts, identifies redundant content, and outputs a comprehensive report with actionable optimization guidance.

No external dependencies are required to run a context window analysis. The tool operates in a Python 3 standard library-only environment, parsing workspace files to estimate token counts and compute efficiency scores without needing additional packages.

Yes, you can compare before and after snapshots to quantify token savings. By analyzing workspace context files at different points, the tool calculates improved context utilization and generates reports detailing the exact token reductions achieved.

The best way to reduce token overhead in OpenClaw contexts is to analyze key workspace files like SKILL.md and TOOLS.md to find redundancy. The analysis generates per-file breakdowns and actionable recommendations to systematically eliminate unnecessary tokens.