Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add 001TMF/blatant-why --skill by-scoringgit clone --depth 1 https://github.com/001TMF/blatant-whyWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/001tmf/blatant-why/by-scoring)<a href="https://agentmods.dev/skills/001tmf/blatant-why/by-scoring"><img src="https://agentmods.dev/badge/skills/001tmf/blatant-why/by-scoring/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/001tmf/blatant-why/by-scoring"><img src="https://agentmods.dev/badge/skills/001tmf/blatant-why/by-scoring.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00003 | $0.10441 |
| Opus 5.5 | $0.00001 | $0.04176 |
| Sonnet 5.5 | $0.00001 | $0.02088 |
| Haiku 4.5 | $0.00000 | $0.01044 |
Grade A, and why
by-scoring scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 813 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BY Scoring Skill
Interpret and apply BY custom scoring metrics for protein and antibody design. This skill covers ipSAE (interface Predicted Structural Accuracy Error) — the primary custom metric that differentiates BY from generic structure prediction tools — along with ipTM, pLDDT, RMSD, liability scoring, and the BY composite ranking formula.
ipSAE uses the open-source DunbrackLab formula (Dunbrack et al. 2025) with no proprietary dependencies. Use this skill whenever you need to score designs, interpret scoring output, troubleshoot disagreements between metrics, or advise on candidate ranking.
When to Use This Skill
Use this skill when:
- ✅ Scoring designs after Protenix refolding — you have NPZ or confidence JSON output
- ✅ Computing the BY composite score on a panel of screened designs
- ✅ Explaining why two metrics (ipSAE vs ipTM) disagree on a candidate
- ✅ Setting modality-specific thresholds (antibody vs nanobody vs de novo)
- ✅ Diagnosing why an entire panel failed screening (zero LAB-READY)
- ✅ Auditing whether a multi-seed result is stable or driven by one lucky seed
- ✅ Interpreting asymmetric ipSAE (
dt >> tdortd >> dt) - ✅ Selecting the right PAE cutoff (10 A Protenix/AF3 vs 15 A AF2)
Don't use this skill for:
- ❌ Generating designs — use the by-design-workflow skill instead
- ❌ Running liability or developability screening directly — use the by-screening skill
- ❌ Choosing which target to design against — use the by-research skill
- ❌ Submitting candidates to a lab — use the by-lab agent (triple-gated)
- ❌ Predicting raw structures — use the protenix skill
Quick Start
Compute ipSAE for a single Protenix output:
# JSON confidence file (Protenix /summary_confidence.json)
python scripts/calc_ipsae.py \
--pae confidence.json \
--chains confidence.json \
--design A --target B \
--pae-cutoff 10.0
# Or run the worked numerical example from references/
python scripts/calc_ipsae.py --example
# Expected: ipsae_min = 0.0396
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 813 lines · 3 tokens per session scan A 8698b24c6a5a
by-scoring is a skill published in the GitHub repository 001TMF/blatant-why (119 stars, last pushed 9d ago), licensed MIT. It adds 3 tokens to every session and 10,441 once invoked, about $0.0000 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
binding-affinity
Empirical affinity estimates, ligand energy inspection, docking-score consensus, and batch virtual screening. Full MM/GBSA requires a validated external workflow.
statistical-modeling
Statistical modeling and machine learning for biomarker discovery, survival analysis, classification, regression, and model interpretation.
chromatin-regulation
Chromatin regulation analysis from called peaks and count matrices — differential binding, signal summarisation, peak annotation, and scATAC-seq.
genomic-variants
Analysis of called genomic variants — filtering, annotation, GWAS, and population-genetics summaries from VCF and PLINK-format data.
bulk-transcriptomics
Bulk RNA-seq and microarray differential expression analysis including method selection, batch correction, and complex experimental designs.
spatial-omics
Spatial transcriptomics and spatial proteomics analysis covering technology-specific workflows, spatial statistics, deconvolution, and niche analysis.