Individual skill
datamol
A Pythonic wrapper around RDKit with simplified interfaces and sensible defaults. Preferred for standard drug discovery workflows including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformer generation, and parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.
scientific-skills/Other/datamol/SKILL.md
Source commit 686e09dfdb45d9fa1a08664f06bb93769b3c9d91
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- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- No
- Repository scan complete
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- Skill scan complete
- No
- Observed behavior
- Not checked yet
- Last checked
- Nov 9, 2026
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Repository context
- Repository stars (not skill ratings)
- 2,042
- Repository last updated
- Sep 17, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- bioinformatics Repository topic
- computational-biology Repository topic
- data-analysis Repository topic
- ai Repository topic
- biology Repository topic
- cancer-genomics Repository topic
- data-visualization Repository topic
- differential-expression Repository topic
- genomics Repository topic
- medical Repository topic
- engineering Derived category
Files and excerpt
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Browse files
SKILL.mdcore · 23,943 bytesSKILL.mdcore · 23,943 bytesSKILL.mdcore · 23,943 bytesSKILL.mdcore · 23,943 bytesSKILL.mdcore · 23,943 bytesSKILL.mdcore · 23,943 bytesSKILL.mdcore · 23,943 bytesdatamol_audit_result_v2.jsonasset · 12,705 bytesreferences/conformers_module.mddoc · 5,305 bytesreferences/core_api.mddoc · 4,492 bytesreferences/descriptors_viz.mddoc · 7,686 bytesreferences/fragments_scaffolds.mddoc · 5,999 bytesreferences/io_module.mddoc · 4,312 bytesreferences/reactions_data.mddoc · 6,667 bytesscripts/validate_skill.pyruns · 679 bytes
Read excerpt
> **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills)
## When to Use
- Use this skill when the request matches its documented task boundary.
- Use it when the user can provide the required inputs and expects a structured deliverable.
- Prefer this skill for repeatable, checklist-driven execution rather than open-ended brainstorming.
## Key Features
- Scope-focused workflow aligned to: A Pythonic wrapper around RDKit with simplified interfaces and sensible defaults. Preferred for standard drug discovery workflows including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformer generation, and parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.
- Packaged executable path(s): `scripts/validate_skill.py`.
- Reference material available in `references/` for task-specific guidance.
- Structured execution path designed to keep outputs consistent and reviewable.
