deepchem-circular-featurization — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited deepchem-circular-featurization (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Turn one or more SMILES strings into deterministic DeepChem CircularFingerprint summaries without requiring TensorFlow or PyTorch.
--smiles arguments, or no arguments to use the bundled aspirin/caffeine example--size, --radius, and --outcanonical_smiles, bit_vector, on_bits, and on_bit_count for each moleculeslurm/envs/deepchemslurm/envs/deepchem/bin/python skills/drug-discovery-and-cheminformatics/deepchem-circular-featurization/scripts/compute_circular_fingerprints.py --out skills/drug-discovery-and-cheminformatics/deepchem-circular-featurization/assets/aspirin_caffeine_fingerprints.json.size, radius, and each molecule's canonical_smiles, bit_vector, and on_bits.slurm/envs/deepchem/bin/python.slurm/envs/deepchem/bin/python.rdkit-molecular-descriptors~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.