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 dralkh/iktinah --skill molfeatgit clone --depth 1 https://github.com/dralkh/iktinahWrote 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/dralkh/iktinah/molfeat)<a href="https://agentmods.dev/skills/dralkh/iktinah/molfeat"><img src="https://agentmods.dev/badge/skills/dralkh/iktinah/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/dralkh/iktinah/molfeat"><img src="https://agentmods.dev/badge/skills/dralkh/iktinah/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 | $0.04083 |
| Opus 5 | $0.00023 | $0.02041 |
| Sonnet 5 | $0.00009 | $0.00817 |
| Haiku 4.5 | $0.00005 | $0.00408 |
Grade B, and why
molfeat scanned grade B with 1 finding 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 6d 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.
Unrestricted tool accessmediumExcessive agency
A wildcard tool grant or "run any command" leaves no least-privilege boundary at all.
Prefer molfeat's built-in pretrained-model cache when possible. For custom embedding caches, use NumPy arrays instead of pickle (pickle can execute arbitrary code when loading untrusted files): Copies of this mod
1 near-identical copy found in the catalogue:
- molfeat — 100% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 522 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.
Version note: Examples target molfeat 0.11.0 (PyPI stable, May 2025). Requires Python 3.9–3.10 (requires-python caps below 3.11). Depends on datamol ≥0.8.0 and PyTorch ≥1.13. Since 0.8.7, prefer datamol Mol objects over raw rdkit.Chem.Mol. Since 0.10.1, fingerprint calculators use RDKit's rdFingerprintGenerator API internally. Since 0.11.0, pretrained models load in memory and base models are set to PyTorch evaluation mode automatically.
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
Use a Python 3.9 or 3.10 environment (molfeat does not install on 3.11+ as of 0.11.0):
uv pip install "molfeat==0.11.0"
# With all pip-installable optional dependencies
uv pip install "molfeat[all]==0.11.0"
Optional dependency extras (PyPI):
molfeat[dgl]— GNN models (GIN variants); upstream recommendsdgl<=2.0(graphbolt issues in newer DGL)molfeat[graphormer]— Graphormer modelsmolfeat[transformer]— ChemBERTa, ChemGPT, MolT5molfeat[fcd]— FCD descriptorsmolfeat[pyg]— PyTorch Geometric featurizersmolfeat[viz]— NGLView visualization widgets
What ships with it
3 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.
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.
- 6d ago First seen · 522 lines · 47 tokens per session scan B 3d3f41c769c0
molfeat is a skill published in the GitHub repository dralkh/iktinah (77 stars, last pushed 2mo ago), licensed MIT. It adds 47 tokens to every session and 4,083 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 1 finding (unrestricted tool access). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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