by-experiment-results

BY Experiment Results Skill

A design campaign only learns when lab outcomes are joined back to the in-silico predictions that produced them. This skill is the wet-lab feedback loop closer: it ingests lab readouts (Adaptyv batch CSVs, internal ELISA plate-reader output, BLI/Octet kinetics, expression QC), joins them to the per-design feature table the screener emitted, and runs a calibration analysis that asks one question for every in-silico feature:

Did this feature actually predict whether the design worked in the lab?

The answer for each feature is one of: validated (in-silico PASS correlated with lab PASS), contradicted (predictor pointed the opposite direction), or inconclusive (no signal). The diagnosis goes into the knowledge graph with that confidence label so future campaigns start from real-world-calibrated priors, not silicon-only priors.

This skill sits between by-screening (which produces in-silico PASS/FAIL) and by-campaign-optimizer (which trains the next round). It is the only skill in BY that consumes ground-truth lab data.

---

When to Use This Skill

✅ Use this skill when:

• A lab readout file (CSV or Excel) has arrived from Adaptyv Bio for a previously submitted batch
• An internal ELISA, BLI, or Octet run has produced a tidy results table for designs the BY pipeline scored
• You need to validate round-N in-silico predictions against round-N-1 lab outcomes
• A user asks "did our predictions hold up?", "calibrate the screener", or "did ipSAE correlate with Kd?"
• A round of designs has come back from the lab and you need to update the campaign-optimizer training data with ground truth
• You want to write a calibration report showing precision at top-K and lift over random
• You want to publish findings to the knowledge graph as validated or contradicted with confidence levels

❌ Don't use this skill for:

• A campaign that has not yet been submitted to lab — there is no ground truth to compare. Use by-screening + by-failure-diagnosis first.
• Pre-submission ranking of designs — that is by-screening and by-design-workflow.
• Computing raw in-silico scores from PDB / NPZ — use by-scoring.
• Diagnosing why a campaign produced low in-silico pass rate — use by-failure-diagnosis (no lab data needed).
• Lab submission itself — use the gated /by:approve-lab flow and by-adaptyv MCP tools.
• Curating raw structural files (CIF / PDB) — those stay in the campaign directory.