AutoMD-Viz
v1.0.0Generate publication-quality molecular dynamics visualizations including structures, data plots, trajectory projections, and full reports with journal-specif...
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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 (MD visualization, journal styles) align with the provided SKILL.md, README, and the included automd-viz.sh. Required tools (Python, PyMOL, VMD, Matplotlib/Seaborn) are exactly what a visualization tool needs. The repository reference and examples consistently target MD workflows and AutoMD-GROMACS integration.
Instruction Scope
SKILL.md and automd-viz.sh instruct the agent to run local command-line tools and Python code to read local structure/trajectory/data files and produce images/reports — all within the stated scope. The only scope notes: it expects precomputed projection files (projection_pca.xvg) and integration with external analysis tools (AutoMD-GROMACS/advanced-analysis); it does not attempt to read unrelated system files or exfiltrate data. However, the shell script embeds user-supplied filenames/parameters directly into here-docs and inline Python code without explicit sanitization, which is a robustness/security consideration if used with untrusted filenames/inputs.
Install Mechanism
No install specification is present; this is instruction-only plus a shell entrypoint. SKILL.md suggests installing Python packages via pip (a standard public registry) or cloning from GitHub. No downloads from obscure URLs or archive extraction instructions are present.
Credentials
The skill declares no required environment variables, credentials, or config paths. Runtime behavior uses only local files and standard tool invocation; there are no requests for unrelated secrets or cloud credentials.
Persistence & Privilege
Flags: always is false and the skill is user-invocable; it does not request persistent elevated privileges or modify other skills' configuration. Autonomous invocation is allowed by default but is not combined with other concerning factors.
Assessment
This skill looks coherent for MD visualization and doesn't ask for secrets. Before installing/running: 1) Verify the upstream GitHub repo and author if you need provenance (SKILL.md points at a GitHub URL but the package source was listed as unknown). 2) Inspect automd-viz.sh yourself (included) — it writes and executes PyMOL scripts and runs inline Python; filenames and parameters are interpolated into scripts, so avoid passing untrusted filenames (a malicious filename could break quoting). 3) Install Python dependencies in a virtualenv/conda environment to avoid affecting system packages. 4) Run first on sample or sandboxed data to ensure behavior matches expectations. If you need higher assurance, request the upstream repo and compare the packaged files to its canonical source and check for any additional commits or network calls not present in these files.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.