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deepchem-molgraph-featurization

deepchem-molgraph-featurization is a skill for Claude Code, Codex from ma-compbio-lab/SkillFoundry. It costs 39 tokens per session (409 once invoked), scanned A, original, Apache-2.0.

A preprocessing example that turns SMILES strings, a text notation for molecular structures, into DeepChem molecular graph objects. DeepChem is a machine-learning toolkit for chemistry, and the output summarizes each graph's size and feature dimensions.

In plain words
What is it for?
Use it with a TSV containing molecule IDs and SMILES to count nodes and edges and inspect node-feature dimensions before a molecular machine-learning experiment.
Why use it?
It lets you inspect molecular graph data before training models, without requiring model training or dataset downloads. The process is deterministic and uses the project's managed environment.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

not rated 38repo +1 4mo ago A scan 39 tokens original Apache-2.0

Good fit Use it with a TSV containing molecule IDs and SMILES to count nodes and edges and inspect node-feature dimensions before a molecular machine-learning experiment.

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Install with agentmods
npx agentmods add skills/ma-compbio-lab/skillfoundry/deepchem-molgraph-featurization
View source ↗ ma-compbio-lab/SkillFoundry ma-compbio-lab.github.io
Install

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.

Any agent
npx skills add ma-compbio-lab/SkillFoundry --skill deepchem-molgraph-featurization
Clone the repo
git clone --depth 1 https://github.com/ma-compbio-lab/SkillFoundry

Made for: Claude Code, Codex.

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.

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README.md
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Your own site
<a href="https://agentmods.dev/skills/ma-compbio-lab/skillfoundry/deepchem-molgraph-featurization"><img src="https://agentmods.dev/badge/skills/ma-compbio-lab/skillfoundry/deepchem-molgraph-featurization/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 deepchem-molgraph-featurization

Your own site · 80×15
<a href="https://agentmods.dev/skills/ma-compbio-lab/skillfoundry/deepchem-molgraph-featurization"><img src="https://agentmods.dev/badge/skills/ma-compbio-lab/skillfoundry/deepchem-molgraph-featurization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 39 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 409 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 $0.00039 $0.00409
Opus 5 $0.00019 $0.00204
Sonnet 5 $0.00008 $0.00082
Haiku 4.5 $0.00004 $0.00041

Measured 9d ago against content hash 057fe9b90393, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

deepchem-molgraph-featurization 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 9d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/featurize_molecules.py, tests/test_featurize_molecules.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/computational-chemistry-and-molecular-simulation/deepchem-molgraph-featurization/SKILL.md · 46 lines

What it actually says

Purpose

Create deterministic DeepChem molecular graph features from a small SMILES table using the repo-managed chemtools prefix.

When to use

  • You need a lightweight DeepChem starter without training a model.
  • You want to inspect graph sizes and node-feature dimensions before modeling.

When not to use

  • You need trained DeepChem models requiring TensorFlow or PyTorch.
  • You need dataset download or benchmark automation.

Inputs

  • A TSV file with molecule_id and smiles columns.

Outputs

  • A JSON summary with graph dimensions for each molecule.

Requirements

  • slurm/envs/chemtools
  • DeepChem and RDKit in that prefix

Procedure

  1. Run slurm/envs/chemtools/bin/python skills/computational-chemistry-and-molecular-simulation/deepchem-molgraph-featurization/scripts/featurize_molecules.py --input skills/computational-chemistry-and-molecular-simulation/deepchem-molgraph-featurization/examples/molecules.tsv --out scratch/deepchem/featurization.json.
  2. Inspect node counts, edge counts, and feature dimensions.
  3. Use the feature summary as a preflight step before larger molecular ML runs.

Validation

  • The script exits successfully.
  • Each molecule yields graph metadata.
  • Node and edge counts are positive for valid molecules.

Failure modes and fixes

  • Missing optional ML backends: this skill only requires the featurizer path, not torch or tensorflow.
  • Invalid SMILES: correct the input row before featurization.

Provenance

  • rdkit-molecular-descriptors
Files

What ships with it

9 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.

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. 9d ago First seen · 46 lines · 39 tokens per session scan A 057fe9b90393

Subscribe to this mod's changes

deepchem-molgraph-featurization is a skill published in the GitHub repository ma-compbio-lab/SkillFoundry (38 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 39 tokens to every session and 409 once invoked, about $0.0002 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.

Data and AIScientificagent-skillsai-for-scienceautomationcomputational-biologygenomicsllm-agents
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