PyMOL (Claude Skill)

Render and analyze 3D protein and small-molecule structures with PyMOL from inside Claude, producing publication-quality images, alignments, and interaction measurements without a display server or GPU.

   
Type Claude Skill
Supplier Google DeepMind
Availability GA
Pricing Free / OSS skill (Apache-2.0 code, CC-BY-4.0 docs); PyMOL itself is licensed separately — review pymol.org
Capabilities Read/Write — Claude writes and runs PyMOL Python scripts locally (uv run) over structure files in your project, writing PNG/.pse/stdout outputs
Verified degraded · 2026-07-20 — removed stale scienceskillscommon copy line; pymol skill dir resolves
Security cleared · 2026-07-20 — provenance matches google-deepmind, Apache-2.0, maintained, no OSV advisories

How to install

The google-deepmind/science-skills collection follows the Agent Skills SKILL.md spec. The repo’s primary npx skills add path targets Gemini/Antigravity; for Claude the followable path is a manual copy of the skill directory.

  • Claude Code / Claude Desktop — clone and copy the skill into your skills directory: 
    git clone https://github.com/google-deepmind/science-skills
cp -r science-skills/skills/pymol ~/.claude/skills/
  • Prerequisite — the skill runs PyMOL via uv run with PEP 723 dependency headers; install uv first if absent: curl -LsSf https://astral.sh/uv/install.sh | sh. PyMOL (open-source build, headless via OSMesa) installs into an isolated environment on first run — no GPU, X11, or display server required.

What it does

Generates and executes PyMOL Python scripts headlessly, then interprets the results:

  • Visualization — renders publication-quality PNG images of structures with custom representations and coloring.
  • Alignment / superposition — overlays structures and reports RMSD.
  • Measurements — distances, atom counts, and other stdout metrics.
  • Coloring schemes — by B-factor / pLDDT confidence, binding-site highlighting, and protein–ligand interaction views.
  • Outputs — PNG renders, editable .pse session files (openable in a local PyMOL), and stdout numeric metrics.

Inputs are structure files (.pdb, .cif, etc.) that already exist locally in your project directory.

Primary use cases: publication figures of protein structures, structure superposition and RMSD, pLDDT/B-factor coloring of predicted models, binding-site and protein–ligand interaction inspection.

Notes

The skill operates on local coordinate files — pair it with AlphaFold or the RCSB PDB servers to obtain structures first. It is not for AlphaFold prediction, docking, molecular dynamics, or sequence-only analysis. Rendering is headless via OSMesa, so it works on machines without a GPU or display. PyMOL is open-source under its own license (Schrödinger maintains both open-source and commercial Incentive builds) — review the PyMOL license before use; the skill itself is Apache-2.0 (code) / CC-BY-4.0 (docs). The upstream npx skills add google-deepmind/science-skills/ command is oriented at Gemini/Antigravity (it writes to ~/.gemini/config/skills/); for Claude, the manual copy into ~/.claude/skills/ shown above is the equivalent path.

Sources

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