Can a coding agent use 57–85% less fresh model traffic without losing task success? I open-sourced my experiment

I built an open-source execution and context layer for coding agents that moves deterministic repository work outside the model loop, supplies bounded task-relevant context, and independently verifies the resulting patch. So far I have three paired smoke tests on one public reset-token fixture: • GPT-5.6 Luna: 85.64% less fresh input + output and 77.87% lower end-to-end time; 4/4 tests in both arms and a byte-identical patch. • GPT-5.6 Sol: 57.75% less fresh input + output and 51.65% lower end-to-end time; 4/4 in both arms, with matching executable changes. • Claude Opus 5, reproduced by a community operator on another machine: 81.44% less fresh input + output, 82.77% lower provider-reported cost, and 70.83% lower end-to-end time; both pristine verification commands passed. These are three single-task pairs, not independent population-level validation, and I do not claim the percentages generalize. I published the sanitized measurements, limitations, and reproducible drivers. The next useful step is a larger evaluator-selected task set, but I no longer have the budget for provider calls and continued integration work. I am looking for independent evaluators, contributors, compute credits, or sponsorship. I will put the repository and evidence link in a comment to follow this community’s rules. What would you want controlled before treating this as credible evidence: more repositories, randomized task selection, repeated pairs, or something else?