Sunday audit day rolled around, so I fired up Claude Code to run the 4R audit across my workspace. Everything was cruising along, but if you've run a real audit across a deep folder tree, you know it checks everything—every rule, every "done" line, and every edge case. Mid-run, I hit the usage wall. Audit half-finished, terminal frozen, and I didn't want to wait around for the reset, so I coughed up the extra $3 for more usage just to keep the momentum going. That got me thinking: burning premium tokens just to have Claude read through dozens of raw files and terminal outputs is like hiring a master electrician just to sweep up wire scraps. So I started playing around with running open models through Ollama in Claude Code to act as the "scout," and it completely changed the workflow. The Setup: The Artifact Handoff Instead of letting Claude burn tokens reading the entire workspace, I split the job: 1. The Heavy Lifters (Kimi / DeepSeek) - kimi-k3:cloud: Great for long-horizon agent loops. It crawls the directories, tests edge cases, and logs open findings without dropping the ball mid-pass. - deepseek-v4.1-flash:cloud: With a massive 1M context window and high speed, it chews through massive workspaces in seconds. - The open model runs the discovery phase and dumps the prioritized punch list into a clean audit-report.md file. - The Finisher (Claude) - Exit the session, open a fresh terminal, and bring Claude in only to read audit-report.md. - No raw log bloat: Claude never touches the hundreds of files traversed or raw logs. It only sees a tight 2–3 KB report. - Zero wasted turns: Claude spends its context strictly on making surgical edits, fixing the broken rules, and running the verification test. Fast, cheap, and no hit on your limits. Anyone else playing with hybrid setups or open models for their ICM maintenance? How are you guys saving tokens during heavy workspace runs?