medsci-agent: Skill for OpenCode

.opencode/skills/datamol/SKILL.md

datamol is a skill for OpenCode from omar-A-hassan/medsci-agent. It costs 19 tokens per session (428 once invoked), scanned A, original, MIT.

A Python toolkit for working with molecules and chemical structures. It can read SMILES, a text format for describing molecules, then standardize, inspect, split, and visualize them.

In plain words
What is it for?
Use it to parse SMILES, calculate properties such as molecular weight and logP, create fingerprints, find molecular scaffolds, and break molecules into fragments.
Why use it?
It removes repetitive low-level chemistry data handling and provides one consistent way to clean and compare molecular structures.

Skill for OpenCode

Written for OpenCode: installed under .opencode/.

This is omar-A-hassan/medsci-agent's own configuration. It tells OpenCode how to work on medsci-agent itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything medsci-agent configures →

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/omar-A-hassan/medsci-agent/main/.opencode/skills/datamol/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/omar-A-hassan/medsci-agent

Made for: OpenCode.

Wrote 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.

agentmods badge for datamol

README.md
[![agentmods](https://agentmods.dev/badge/skills/omar-a-hassan/medsci-agent/datamol/github.svg)](https://agentmods.dev/skills/omar-a-hassan/medsci-agent/datamol)
Your own site
<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.

agentmods 80×15 button for datamol

Your own site · 80×15
<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>
Per session 19 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 428 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00019 $0.00428
Opus 5.5 $0.00008 $0.00171
Sonnet 5.5 $0.00004 $0.00086
Haiku 4.5 $0.00002 $0.00043

Measured 2mo ago against content hash e6a48a4fae59, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 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.

.opencode/skills/datamol/SKILL.md · 46 lines

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 None inputs (return None).
  • dm.to_smiles returns 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.
Changes

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.

  1. 2mo ago First seen · 46 lines · 19 tokens per session scan A e6a48a4fae59

Subscribe to this mod's changes

datamol is a skill published in the GitHub repository omar-A-hassan/medsci-agent (17 stars, last pushed 2mo ago), licensed MIT. It adds 19 tokens to every session and 428 once invoked, about $0.0001 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.

Related

datamol compared with skills from other repositories

datamol next to 6 skills from other repositories
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