Mingbird is a local-first agent harness designed for small open models to complete real tasks. It addresses issues such as tool prefill overflows, self-correction divergence, and task abandonment by introducing ten mechanisms, including a byte-level net-zero prefill budget and signature-level loop detection. Mingbird outperforms other harnesses on various benchmarks, achieving 0.886 overall on LRAB and 0.856 on τ^2-bench.