by-scoring

Compute and interpret BY ipSAE, ipTM, and composite scores from PAE matrices.

104|10|Updated Mar 23, 2026
One-click install
npx skills add https://github.com/001TMF/blatant-why --skill by-scoring
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: by-scoring
Source: https://github.com/001TMF/blatant-why/tree/main/templates/.claude/skills/by-scoring
Command: npx skills add https://github.com/001TMF/blatant-why --skill by-scoring

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, matplotlib, and includes scripts (resource) and references (resource) components.

What problem does it solve?

BY Scoring interprets and applies BY's custom scoring metrics — ipSAE, ipTM, pLDDT, RMSD, liability counts, and the composite ranking formula — to rank antibody, nanobody, and de novo designs based on interface confidence, global topology, and manufacturability.

Core Features & Use Cases

  • Interpret ipSAE_min, ipTM, pLDDT_mean, RMSD, liability counts, and the BY composite score.
  • Use this skill when scoring designs after BoltzGen refolding, ranking panels, troubleshooting metric disagreements, or advising on candidate ranking decisions.
  • Supports single-design scoring, multi-seed scoring, and batch ranking with hard filters by modality; outputs include per-design metrics and a final verdict.

Quick Start

Run the BY scoring workflow on a Protenix output to produce ipsae_min, iptm, and a composite ranking.

Frequently Asked Questions about by-scoring

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I compute ipSAE and ipTM scores from a PAE matrix for antibody design ranking?▼

BY scoring computes ipSAE and ipTM scores from a PAE matrix and chain assignments to evaluate interface confidence, producing per-design metrics and a composite ranking for antibody and nanobody designs.

What is the BY composite score used for when ranking de novo designs?▼

The BY composite score ranks de novo designs by combining ipSAE_min, ipTM, pLDDT_mean, RMSD, and liability counts into a unified metric reflecting interface confidence, global topology, and manufacturability for final verdicts.

How do I run multi-seed scoring and batch ranking with hard filters by modality?▼

Apply multi-seed scoring and batch ranking by processing PAE matrices across seeds with hard modality filters, generating per-design ipSAE, ipTM, composite scores, ranks, and verdicts for candidate panels.

Does BY scoring work with Protenix outputs for evaluating refolded designs?▼

Yes, BY scoring processes Protenix outputs by interpreting PAE matrices to score designs after refolding, calculating ipsae_min, iptm, pLDDT_mean, and composite rankings to guide candidate selection decisions.

Why do my ipSAE and ipTM metric results disagree when troubleshooting design rankings?▼

Metric disagreements arise because ipSAE measures interface confidence while ipTM captures global topology; BY scoring resolves this by computing a liability-based composite score and final verdict to rank designs consistently.