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 GPTomics/bioSkills --skill pose-validationgit clone --depth 1 https://github.com/GPTomics/bioSkillsWrote 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/gptomics/bioskills/pose-validation)<a href="https://agentmods.dev/skills/gptomics/bioskills/pose-validation"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/pose-validation/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/gptomics/bioskills/pose-validation"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/pose-validation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00074 | $0.03776 |
| Opus 5.5 | $0.00030 | $0.01510 |
| Sonnet 5.5 | $0.00015 | $0.00755 |
| Haiku 4.5 | $0.00007 | $0.00378 |
Grade A, and why
bio-pose-validation 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 2mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- bio-pose-validation — 94% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 294 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: PoseBusters 0.6+, RDKit 2024.09+, pandas 2.2+, posecheck 0.5+ (optional).
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Pose Validation
Test docked or AI-generated protein-ligand poses for physical plausibility. PoseBusters (Buttenschoen et al. 2024) provides geometric, chemical, and energetic checks that flag implausible poses, including non-planar aromatic rings, van der Waals clashes, broken bonds, altered stereochemistry, and unfavorable internal energies. On the Astex Diverse Set, DiffDock achieved 72% RMSD success but only 47% combined RMSD-and-PB-valid success; the size of this gap is dataset- and method-dependent. PB-valid status complements RMSD for downstream SAR, FEP setup, or generative-model training.
For docking, see chemoinformatics/virtual-screening. For ML docking specifically, see chemoinformatics/ml-docking-rescoring.
PoseBusters Test Suite
PoseBusters runs ~20 individual checks grouped into:
The thresholds below are the benchmark criteria reported by Buttenschoen et al. (2024). Installed PoseBusters defaults may differ by version and configuration, so record the package version and resolved configuration.
| Check group | What it tests | 2024 benchmark criterion |
|---|---|---|
| Sanity | Ligand chemical sanity | RDKit sanitization passes |
| Bond lengths | Bond lengths within reference | 0.75–1.25 times RDKit distance-geometry bounds |
| Bond angles | 1–3 distances within reference | 0.75–1.25 times RDKit distance-geometry bounds |
| Internal steric | No intra-ligand clash | Pair distance > 0.70 times the RDKit lower bound |
| Aromatic ring planarity | Aromatic rings planar | Maximum deviation from fitted plane <= 0.25 Å |
| Double-bond stereo | Z/E preserved | Match input SMILES |
| Internal energy | Energy relative to generated conformers | UFF energy ratio <= 100 versus the mean of 50 generated, relaxed conformers |
| Volume overlap | vdW overlap with protein | < 7.5% of ligand vdW volume |
| Minimum distance | No severe protein-ligand clash | Distance >= 0.75 times the sum of vdW radii |
| Chirality | R/S preserved from input | Match input SMILES |
What ships with it
2 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.
- 2mo ago First seen · 294 lines · 74 tokens per session scan A 2f2b158805db
bio-pose-validation is a skill published in the GitHub repository GPTomics/bioSkills (1,228 stars, last pushed 2mo ago), licensed MIT. It adds 74 tokens to every session and 3,776 once invoked, about $0.0003 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.
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