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 AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-deepchemgit clone --depth 1 https://github.com/AlterLab-IEU/AlterLab-Academic-SkillsWrote 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/alterlab-ieu/alterlab-academic-skills/alterlab-deepchem)<a href="https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-deepchem"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-deepchem/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/alterlab-ieu/alterlab-academic-skills/alterlab-deepchem"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-deepchem.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00127 | $0.04283 |
| Opus 5.5 | $0.00051 | $0.01713 |
| Sonnet 5.5 | $0.00025 | $0.00857 |
| Haiku 4.5 | $0.00013 | $0.00428 |
Grade A, and why
alterlab-deepchem 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 452 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DeepChem
Overview
DeepChem is a comprehensive Python library for applying machine learning to chemistry, materials science, and biology. Enable molecular property prediction, drug discovery, materials design, and biomolecule analysis through specialized neural networks, molecular featurization methods, and pretrained models.
When to Use This Skill
This skill should be used when:
- Loading and processing molecular data (SMILES strings, SDF files, protein sequences)
- Predicting molecular properties (solubility, toxicity, binding affinity, ADMET properties)
- Training models on chemical/biological datasets
- Using MoleculeNet benchmark datasets (Tox21, BBBP, Delaney, etc.)
- Converting molecules to ML-ready features (fingerprints, graph representations, descriptors)
- Implementing graph neural networks for molecules (GCN, GAT, MPNN, AttentiveFP)
- Applying transfer learning with pretrained models (ChemBERTa, GROVER; MoLFormer in nightly builds)
- Predicting crystal/materials properties (bandgap, formation energy)
- Analyzing protein or DNA sequences
Does NOT Trigger
| Scenario | Use Instead |
|---|---|
| Designing a custom GNN architecture (own layers / message passing) in PyTorch | alterlab-torch-geometric (or alterlab-torchdrug for TorchDrug's task abstractions) |
| Only fetching standardized ADMET/DTI benchmark datasets and official splits, no modeling | alterlab-pytdc |
| Only turning molecules into feature matrices or pretrained embeddings for your own models | alterlab-molfeat |
| Classical ML on an existing numeric feature table (pipelines, CV, tuning) | alterlab-scikit-learn |
Core Capabilities
1. Molecular Data Loading and Processing
DeepChem provides specialized loaders for various chemical data formats:
import deepchem as dc
# Load CSV with SMILES
featurizer = dc.feat.CircularFingerprint(radius=2, size=2048)
loader = dc.data.CSVLoader(
tasks=['solubility', 'toxicity'],
feature_field='smiles',
featurizer=featurizer
)
dataset = loader.create_dataset('molecules.csv')
# Load SDF files
loader = dc.data.SDFLoader(tasks=['activity'], featurizer=featurizer)
dataset = loader.create_dataset('compounds.sdf')
# Load protein sequences
loader = dc.data.FASTALoader()
dataset = loader.create_dataset('proteins.fasta')
What ships with it
8 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.
- evals/evals.json 4.8 KB
- references/api_reference.md 12 KB
- references/best_practices.md 2.8 KB
- references/end_to_end_recipes.md 2.5 KB
- references/workflows.md 13 KB
- scripts/graph_neural_network.py 9.5 KB runs code
- scripts/predict_solubility.py 6.5 KB runs code
- scripts/transfer_learning.py 11 KB runs code
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.
- 1mo ago Changed · +17 lines · +1 tokens per session 125d8ff32ea5
- 1mo ago First seen · 435 lines · 126 tokens per session scan A 53553963fd69
alterlab-deepchem is a skill published in the GitHub repository AlterLab-IEU/AlterLab-Academic-Skills (78 stars, last pushed 2d ago), licensed MIT. It adds 127 tokens to every session and 4,283 once invoked, about $0.0005 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.
Other skills, from other repositories
r-spss-syntax-architect
A guide for turning research hypotheses into repeatable R or SPSS code for statistical analysis. It covers panel data, where the same companies or other units are observed over time, as well as interaction effects, curves, and mediation.
management-figure
A chart-making toolkit for evidence-based management, finance, and strategy research. It creates publication-ready plots from regression results and tracking data, including coefficient, interaction, group-comparison, trend, and curved-relationship charts.
ob-hrm-scale-adaptor
A guide for adapting organisational behaviour and human-resources survey scales across languages and cultures. It covers permission checks, translation and back-translation, expert review, participant interviews, and tests of whether groups interpret the scale comparably.
thesis-consistency-audit
A consistency audit for quantitative master’s and doctoral theses in management, finance, or strategy. It checks whether numbers, tables, analyses, and claims agree, and can inspect hidden author information in office documents.
reproducibility-architect
A guide for packaging research so another person can rerun its data processing and analysis. A replication package is the project files, instructions, code, data guidance, and software details needed to reproduce published results.
journal-submission-scout
A research tool for choosing a journal for a completed paper. It searches for journals that publish similar work, compares public information such as citation data, fees, open-access listing, and review practices, and screens for warning signs of predatory journals, which charge authors without providing trustworthy publishing services.