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 Zaoqu-Liu/ScienceClaw --skill molfeatgit clone --depth 1 https://github.com/Zaoqu-Liu/ScienceClawWrote 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/zaoqu-liu/scienceclaw/molfeat)<a href="https://agentmods.dev/skills/zaoqu-liu/scienceclaw/molfeat"><img src="https://agentmods.dev/badge/skills/zaoqu-liu/scienceclaw/molfeat/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/zaoqu-liu/scienceclaw/molfeat"><img src="https://agentmods.dev/badge/skills/zaoqu-liu/scienceclaw/molfeat.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 | $0.00047 | $0.03778 |
| Opus 5.5 | $0.00019 | $0.01511 |
| Sonnet 5.5 | $0.00009 | $0.00756 |
| Haiku 4.5 | $0.00005 | $0.00378 |
Grade A, and why
molfeat 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.
This is a copy
89% identical to molfeat — 10 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 511 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
uv pip install molfeat
# With all optional dependencies
uv pip install "molfeat[all]"
Optional dependencies for specific featurizers:
molfeat[dgl]- GNN models (GIN variants)molfeat[graphormer]- Graphormer modelsmolfeat[transformer]- ChemBERTa, ChemGPT, MolT5molfeat[fcd]- FCD descriptorsmolfeat[map4]- MAP4 fingerprints
Core Concepts
Molfeat organizes featurization into three hierarchical classes:
1. Calculators (molfeat.calc)
Callable objects that convert individual molecules into feature vectors. Accept RDKit Chem.Mol objects or SMILES strings.
Use calculators for:
- Single molecule featurization
- Custom processing loops
- Direct feature computation
Example:
from molfeat.calc import FPCalculator
calc = FPCalculator("ecfp", radius=3, fpSize=2048)
features = calc("CCO") # Returns numpy array (2048,)
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 First seen · 511 lines · 47 tokens per session scan A c32661864533
molfeat is a skill published in the GitHub repository Zaoqu-Liu/ScienceClaw (60 stars, last pushed 7mo ago), licensed MIT. It adds 47 tokens to every session and 3,778 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to molfeat, differing in 10 lines, and is treated as a copy.
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