Agent skill

molfeat

Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.

FreedomIntelligencegithub.com/FreedomIntelligenceGitHub ↗
claude-code
Install
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill molfeat --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 4
SKILL.md size: 14 KB
Bundled scripts: none
Path: skills/molfeat/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 2,909
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Molfeat - Molecular Featurization Hub ## Overview Molfeat is a comprehensive Python library for molecular featurization that unifies 100+ pre-trained embeddings and hand-crafted featurizers. Convert chemical structures (SMILES strings or RDKit molecules) into numerical representations for machine learning tasks including QSAR modeling, virtual screening, similarity searching, and deep learning applications. Features fast parallel processing, scikit-learn compatible transformers, and built-in caching. ## When to Use This Skill This skill should be used when working with: - **Molecular machine learning**: Building QSAR/QSPR models, property prediction - **Virtual screening**: Ranking compound libraries for biological activity - **Similarity searching**: Finding structurally similar molecules - **Chemical space analysis**: Clustering, visualization, dimensionality reduction - **Deep learning**: Training neural networks on molecular data - **Featurization pipelines**: Converting SMILES to ML-ready representations - **Cheminformatics**: Any task requiring molecular feature extraction ## Installation ```bash uv pip install molfeat # With all optional dependencies uv pip install "molfea

What's inside
Steps it walks through
  1. Overview
  2. When to Use This Skill
  3. Installation
  4. Core Concepts
  5. 1. Calculators (molfeat.calc)
  6. 2. Transformers (molfeat.trans)
  7. 3. Pretrained Transformers (molfeat.trans.pretrained)
  8. Quick Start Workflow
  9. Basic Featurization
  10. Save and Load Configuration
  11. Handle Errors Gracefully
  12. Choosing the Right Featurizer
  13. For Traditional Machine Learning (RF, SVM, XGBoost)
  14. For Deep Learning
Ships with 3 files
  • references/api_reference.md
  • references/available_featurizers.md
  • references/examples.md
Commands it runs
uv pip install molfeat
With all optional dependencies
uv pip install "molfeat[all]"
grep -i "chembert" references/available_featurizers.md
grep -i "pharmacophore" references/available_featurizers.md
uv pip install "molfeat[transformer]"  # For ChemBERTa/ChemGPT
uv pip install "molfeat[dgl]"          # For GIN models
More from OpenClaw-Medical-Skills
All skills →
About this skill
What does the molfeat skill do?

Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.

How do I install it?

Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill molfeat --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going