Pharmaclaw Cheminformatics
v1.0.0Advanced cheminformatics agent for 3D molecular analysis, pharmacophore mapping, format conversion, RECAP fragmentation, and stereoisomer enumeration. The "s...
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MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name/description match the actual code. All modules implement the cheminformatics features described (conformers, pharmacophores, format conversion, RECAP fragmentation, stereoisomer enumeration) and rely on RDKit and standard scientific libs; no unrelated credentials, binaries, or services are requested.
Instruction Scope
SKILL.md and chain_entry.py confine operations to molecule inputs, local file outputs, and RDKit processing. Modules may write SDF/PDB/PNG/text files when an output path is provided. Two minor notes: (1) format_converter applies basic path sanitization for output file paths, but chain_entry.py writes into a user-supplied output_dir without additional sanitization, so outputs will be created wherever the caller points the skill; (2) some operations (conformer generation) use all CPU cores and can be resource-intensive.
Install Mechanism
There is no install spec (no downloads or installers). The code depends on RDKit, numpy, and optionally Pillow; missing dependencies cause the scripts to exit with a clear error. No remote URLs, extract operations, or package installs are embedded in the skill.
Credentials
The skill declares no required environment variables, credentials, or config paths. It uses RDKit internals (RDConfig.RDDataDir) to load feature definitions, which is expected. No secrets are requested or accessed.
Persistence & Privilege
always is false and the skill does not modify other skills or global agent settings. It writes outputs only to paths you supply; it does not attempt to persist credentials or alter runtime configuration beyond its own outputs.
Assessment
This appears to be a straightforward local cheminformatics toolkit. Before installing/using: (1) ensure RDKit (rdkit-pypi) and other dependencies (numpy, Pillow for images) are installed in a controlled environment; (2) be aware CPU/memory can be heavy for large conformer enumerations (the conformer generator uses all cores by default); (3) outputs are written to any output_dir you provide — chain_entry.py does not further sanitize output_dir, so choose directories you trust and have appropriate permissions; (4) no network calls or credentials are requested by the skill, so it won't exfiltrate data unless you run it in an environment that already exposes files or secrets; (5) as always, run untrusted code in an isolated environment (container/VM) if you are concerned about unexpected behavior.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.