Borrowing it
Nothing to install: this file belongs to omar-A-hassan/medsci-agent. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/omar-A-hassan/medsci-agent/main/.opencode/skills/datamol/SKILL.mdgit clone --depth 1 https://github.com/omar-A-hassan/medsci-agentWrote 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/omar-a-hassan/medsci-agent/datamol)<a href="https://agentmods.dev/skills/omar-a-hassan/medsci-agent/datamol"><img src="https://agentmods.dev/badge/skills/omar-a-hassan/medsci-agent/datamol/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/omar-a-hassan/medsci-agent/datamol"><img src="https://agentmods.dev/badge/skills/omar-a-hassan/medsci-agent/datamol.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 | $0.00019 | $0.00428 |
| Opus 5 | $0.00010 | $0.00214 |
| Sonnet 5 | $0.00004 | $0.00086 |
| Haiku 4.5 | $0.00002 | $0.00043 |
Grade A, and why
datamol 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 10d 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.
What it actually says
Datamol
Overview
Datamol is a lightweight Python library built on top of RDKit that simplifies molecular manipulation. It provides a clean API for SMILES parsing, standardization, fingerprints, scaffolds, and visualization.
Core Operations
import datamol as dm
# Parse and standardize SMILES
mol = dm.to_mol("CC(=O)Oc1ccccc1C(=O)O")
std_mol = dm.standardize_mol(mol)
smiles = dm.to_smiles(std_mol, canonical=True)
# Fix and sanitize
mol = dm.to_mol("bad_smiles", ordered=True) # returns None if invalid
fixed = dm.fix_mol(mol)
sanitized = dm.sanitize_mol(fixed)
Descriptors and Fingerprints
# Molecular properties
dm.descriptors.mw(mol) # molecular weight
dm.descriptors.logp(mol) # cLogP
dm.descriptors.tpsa(mol) # topological polar surface area
dm.descriptors.n_hba(mol) # H-bond acceptors
dm.descriptors.n_hbd(mol) # H-bond donors
# Fingerprints
fp = dm.to_fp(mol, fp_type="ecfp", n_bits=2048) # numpy array
Key Details
- Scaffolds:
dm.to_scaffold_murcko(mol),dm.fragment.brics(mol). - All functions gracefully handle
Noneinputs (returnNone). dm.to_smilesreturns canonical SMILES by default.- Batch:
dm.to_mol(["CCO", "c1ccccc1"])accepts lists. - Clustering:
dm.cluster.cluster_mols(mols, cutoff=0.7). - Install:
pip install datamol.
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.
- 10d ago First seen · 46 lines · 19 tokens per session scan A e6a48a4fae59
datamol is a skill published in the GitHub repository omar-A-hassan/medsci-agent (18 stars, last pushed 3d ago), licensed MIT. It adds 19 tokens to every session and 428 once invoked, about $0.0001 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.
Other skills, from other repositories
build-expression-tree
For symbolic computation: ASTs, mathematical expressions, code that manipulates code structure, expression transformations.
find-convex-hull
For computational geometry: convex hull, point enclosure, polygon operations. Uses monotone chain algorithm with stack-based turn detection.
dfam-check
Measure mesh files against Design for Additive Manufacturing (DfAM) rules and report printability findings per process (FDM, SLS, SLA/DLP, metal PBF, MJF). Use when the user asks whether a part is printable, wants overhang/wall-thickness/support analysis of an .stl, .obj, .ply, or .3mf mesh, wants a build-orientation…
peer-review
Structured manuscript/grant review with checklist-based evaluation. Use when writing formal peer reviews with specific criteria methodology assessment, statistical validity, reporting standards compliance (CONSORT/STROBE), and constructive feedback. Best for actual review writing, manuscript revision. For evaluating…
scientific-critical-thinking
Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review…
tooluniverse-gene-enrichment
Gene-set enrichment analysis — GO (Biological Process, Molecular Function, Cellular Component), KEGG, Reactome pathway enrichment via clusterProfiler, gseapy, ORA, GSEA. Use for interpreting DEG lists, screen hit lists, or any gene-list-to-pathways query. Includes simplify-cutoff handling and union-vs-total…