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 molecular-standardizationgit 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/molecular-standardization)<a href="https://agentmods.dev/skills/gptomics/bioskills/molecular-standardization"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/molecular-standardization.svg" alt="Measured on agentmods" 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 | $0.00108 | $0.04177 |
| Opus 5 | $0.00054 | $0.02089 |
| Sonnet 5 | $0.00022 | $0.00835 |
| Haiku 4.5 | $0.00011 | $0.00418 |
Grade A, and why
bio-molecular-standardization 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 8d 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-molecular-standardization — 95% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 307 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: RDKit 2024.09+ and chembl_structure_pipeline 1.2+. MolVS 0.1.1 is a legacy package; use RDKit's maintained rdMolStandardize module for custom pipelines.
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.
Molecular Standardization
Convert raw molecular structures into a consistent form for ML training data, deduplication, registry, and cross-database joining. Skipping standardization can create data leakage when alternate representations of one compound enter different splits, distort QSAR inputs, and cause database join misses. The ChEMBL structure pipeline (Bento et al. 2020) is built on RDKit and applies ChEMBL-specific normalization and parent-selection rules. canSARchem (Dolciami et al. 2022) adds canonical-tautomer selection before parent extraction. RDKit's maintained rdMolStandardize module provides primitives for building an explicit custom pipeline.
For format-level I/O and aromaticity perception, see chemoinformatics/molecular-io. For descriptor calculation after standardization, see chemoinformatics/molecular-descriptors.
Standardization Pipeline Stages
| Stage | RDKit Tool | Operation | Common errors caught |
|---|---|---|---|
| 1. Sanitization | Chem.SanitizeMol |
Kekulize, assign aromaticity, fix valences | Wrong valence on N/O |
| 2. Salt stripping | rdMolStandardize.FragmentRemover or LargestFragmentChooser |
Remove counterions | Cl-, Na+, K+, OH- |
| 3. Mixture choice | LargestFragmentChooser |
Pick parent fragment | Co-crystals, hydrates |
| 4. Charge neutralization | Uncharger |
Neutralize while preserving net charge | Permanent charges preserved (quaternary N+) |
| 5. Tautomer canonicalization | TautomerEnumerator.Canonicalize |
Pick canonical tautomer | Keto/enol; amide/imidate |
| 6. Stereo standardization | Chem.AssignStereochemistry |
Consistent stereo descriptors | Lost wedges, ambiguous R/S |
| 7. Isotope normalization | Explicitly set selected atom isotope labels to 0 | Remove 13C, 2H labels | Tracer studies; preserve labels when scientifically meaningful |
| 8. Output canonicalization | Chem.MolToSmiles(canonical=True) |
Canonical SMILES + InChIKey | Round-trip stability |
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.
- 8d ago First seen · 307 lines · 108 tokens per session scan A 520752e73708
bio-molecular-standardization is a skill published in the GitHub repository GPTomics/bioSkills (1,199 stars, last pushed 23d ago), licensed MIT. It adds 108 tokens to every session and 4,177 once invoked, about $0.0005 per session on Opus 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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