by-failure-diagnosis

BY Failure Diagnosis Skill

Closing the design feedback loop requires understanding why designs fail, not just that they fail. This skill compares the distribution of every continuous feature between PASS and FAIL designs using non-parametric statistics, ranks features by discriminating power, and translates the result into concrete threshold or campaign-parameter changes for the next iteration.

It is the bridge between screening (which produces PASS/FAIL labels) and campaign optimization (which adjusts parameters for the next round).

When to Use This Skill

Use this skill when:

• ✅ Pass rate is below 20% in a screening round and you need to know why before re-spending compute
• ✅ At least 30 designs have been scored with a status field (PASS or FAIL)
• ✅ You have numeric features per design (ipSAE, ipTM, pLDDT, RMSD, liabilities, net_charge, hydrophobic_fraction, cdr3_length)
• ✅ User explicitly asks "why are my designs failing?", "diagnose failures", "what's going wrong?"
• ✅ Before the active-learning step in a multi-round campaign (route diagnosis → optimizer)
• ✅ After a screening regression where pass rate dropped versus a prior round

Don't use this skill when:

• ❌ Fewer than 30 designs total — statistical power is too low; the test will be noisy. Score more designs first.
• ❌ No FAIL designs (100% pass rate) — there is nothing to compare against. Either tighten thresholds or move to lab submission.
• ❌ No PASS designs (0% pass rate) — there is nothing to compare against. Use by-hypothesis-debate to pick a new strategy before spending more compute.
• ❌ You want to redesign individual residues — that is per-design rationale, not population statistics. Use by-epitope-analysis instead.
• ❌ You want to predict structures or score new designs — use protenix or by-scoring instead.
• ❌ The campaign has different scoring criteria across rounds — comparing apples to oranges; run diagnosis within a single round only.