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 virtual-screeninggit 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/virtual-screening)<a href="https://agentmods.dev/skills/gptomics/bioskills/virtual-screening"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/virtual-screening/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/virtual-screening"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/virtual-screening.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 | $0.00129 | $0.06108 |
| Opus 5 | $0.00064 | $0.03054 |
| Sonnet 5 | $0.00026 | $0.01222 |
| Haiku 4.5 | $0.00013 | $0.00611 |
Grade A, and why
bio-virtual-screening scanned grade A with 1 finding 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 6d 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
subprocess.run(['pdb2pqr', '--ff=AMBER', f'--with-ph={pH}', Copies of this mod
1 near-identical copy found in the catalogue:
- bio-virtual-screening — 98% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 371 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: AutoDock Vina 1.2.5+, SMINA 2020-12+, GNINA 1.1+ for rescore (GNINA 1.3+ for the six-mode interface documented below), RDKit 2024.09+, meeko 0.5+, P2Rank 2.4+, ProDy 2.4+, pdb2pqr 3.6+.
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures - CLI:
vina --version;gnina --version;smina --version
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Virtual Screening
Screen chemical libraries against protein targets via molecular docking. Vina is the de-facto default, SMINA adds flexibility (Vinardo scoring, custom scoring), and GNINA adds CNN-based pose scoring (Top-1 redock 58%->73% over Vina, cross-dock 27%->37%). Deep-learning docking (DiffDock-L, EquiBind, NeuralPLexer) competes in pose accuracy, but physical validity is method- and dataset-dependent; the workflow therefore combines ML pose sampling with classical scoring and explicit geometry checks. For ultralarge libraries (>1M), library preparation, hierarchical filtering, and HPC orchestration become the limiting steps.
For pose physical-validity QC, see chemoinformatics/pose-validation. For ML-driven docking + rescoring, see chemoinformatics/ml-docking-rescoring. For covalent docking, see chemoinformatics/covalent-design. For affinity calculations (FEP), see chemoinformatics/free-energy-calculations.
Docking Tool Taxonomy
| Tool | Scoring | Speed (sec/lig) | Best at | Fails when |
|---|---|---|---|---|
| AutoDock Vina 1.2 | Vina (empirical) | Hardware- and settings-dependent | Open, well-characterized baseline | Cross-dock; cryptic pockets; metal centers |
| SMINA | Vina + flexible + custom | Hardware- and settings-dependent | Custom scoring; flexible side chains | Same Vina-scoring caveats |
| Vinardo | Modified Vina scoring | Hardware- and settings-dependent | Alternative empirical score | Validate on target-relevant controls |
| GNINA 1.1 | CNN or Vina scoring | GPU- and settings-dependent | CNN-assisted pose ranking | Validate transfer to the target and chemotype |
| AutoDock 4 | AD4 + grid maps | Hardware- and settings-dependent | Legacy reference | More setup than Vina |
| DOCK 6/7 | DOCK + Amber | Hardware- and settings-dependent | UCSF DOCK ecosystem | Steep learning curve |
| Glide (Schrodinger) | GlideScore | License and hardware-dependent | Commercial docking workflow | License cost |
| GOLD (CCDC) | GOLDScore / ChemScore | License and hardware-dependent | Commercial workflow; metal options | License cost |
| FlexX (BioSolveIT) | FlexX | License and hardware-dependent | Fragment-based placement | License cost |
| rDock | rDock | Hardware- and settings-dependent | Open-source alternative | Validate maintenance and target fit |
| DiffDock-L | Diffusion-generative | GPU- and settings-dependent | Pose sampling for cross-docking | Validate geometry with PoseBusters; see ml-docking-rescoring |
| EquiBind | Equivariant NN | GPU- and settings-dependent | Single-shot pose generation | Requires independent geometry and ranking checks |
| Boltz-2 + GNINA rescore | Foundation model + CNN | GPU- and settings-dependent | Experimental multi-model workflow | Benchmark each evidence stream independently |
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.
- 6d ago First seen · 371 lines · 129 tokens per session scan A 6cccbbd96b9e
bio-virtual-screening is a skill published in the GitHub repository GPTomics/bioSkills (1,199 stars, last pushed 25d ago), licensed MIT. It adds 129 tokens to every session and 6,108 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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