Earlier in this series, I wrote about why bio/medical AI repositories need more than benchmarks, what I learned after auditing 10 public repositories, and why an AI auditor itself needs a memory contract. That work led to STEM-AI v1.1.2 and the MICA layer: a memory-contracted initialization step that forces the auditor to load bounded rules before scoring begins. If you have not read that part, the relevant post is here: How Do You Trust the AI Auditor? STEM-AI v1.1.2 and Memory-Contracted Bio-AI Audits For the broader arc, the full series is here: STEM-AI / STEM BIO-AI series But after that, a different engineering problem took over. The audit logic was stricter. The reports were richer. The reasoning was more bounded. But the developer workflow still felt too loose. So the next question was no longer: How do I score trust? It became: How does a bio-AI audit tool become something an engineer can actually run, gate, inspect, and integrate?…