What problem does it solve? AI coding sessions silently accumulate context bloat from oversized include files, bloated CLAUDE.md rules, excessive MCP servers, and missing permission settings, degrading response quality and wasting tokens without any visibility into the cause. ## Core Features & Use Cases - Trend Analysis: Parses runs.log include data to show loading frequency, heaviest skill runs, tier distribution, and week-over-week deltas. - Full Setup Audit: With /context data, audits MCP servers, memory files against five bloat filters, skill description sizes, settings, and permission deny rules, producing a 0-100 health score. - Baseline Snapshots: Saves current metrics to a JSON baseline so future runs can measure growth or improvement. - Use Case: After weeks of adding rules to CLAUDE.md, run a full audit to discover 300 lines of redundant instructions and two MCP servers with 40+ tools each, then apply the suggested fixes to reclaim context space. ## Quick Start Ask the assistant to run a context audit to analyze include loading trends and score the health of your setup.