Visualize, analyze, and render protein and molecular structures using PyMOL.

Apache-2.0Auto-check passedResearch & Science

Install Pymol

skills CLI
$ npx skills add google-deepmind/science-skills --skill pymol -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install google-deepmind/science-skills pymol --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/google-deepmind/science-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/pymol .claude/skills/pymol && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
pymol
GitHub stars
3.2k
Used in
2 other repos
Token cost
~1.6k tokens
SKILL.md length
694 words
Files
4 (incl. references)
Skills in repo
40
Repo updated
First seen
Licence
Apache-2.0

At a glance

Visualize, analyze, and render protein and molecular structures using PyMOL.

  • Works in 2 steps: uv: Read the uv skill and follow its… → User Notification: If…
  • The user wants to create images of protein structures
  • SKILL.md covers Prerequisites, Overview, Setup (Agent Instructions) and Core Rules, plus 3 more sections
  • Calls uv and bash

What it does

Pymol is an agent skill from google-deepmind/science-skills. Visualize, analyze, and render protein and molecular structures using PyMOL. Use when the user wants to create images of protein structures, perform structural alignments or superposition, measure distances or contacts, highlight binding sites or active site residues, color by B-factor/pLDDT, or analyze protein-ligand interactions. Do not use for docking, molecular dynamics, or sequence-only analysis.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `references/PYMOL_REFERENCE.md` and `references/RECIPES.md`).

It sits in Research & Science, covering Protein structure and design and Physical and earth sciences. It works with AlphaFold. The repository describes itself as: GDM Science Skills to speed up agentic scientific workflows with better grounding and higher token efficiency. Integrate insights from AlphaGenome, AFDB, UniProt and 30+ other… The licence is Apache-2.0.

When your agent uses it

  • The user wants to create images of protein structures
  • Perform structural alignments
  • Measure distances
  • Highlight binding sites

Example prompts

  • “/pymol”

Requirements

  • Python 3

Workflow steps

2 steps, taken from the first numbered list in SKILL.md.

  1. uv: Read the uv skill and follow its Setup instructions to ensure
  2. User Notification: If .licenses/pymol_LICENSE.txt does not already exist

What it can do on your machine

Read from SKILL.md and the folder at commit 6883275. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • uv
    • bash

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • pymol.org

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Pymol loads about 1.6k tokens when it runs, and up to ~5.9k if it reads all its reference files. Until then it costs about 103 tokens; SKILL.md has 694 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~103
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from google-deepmind/science-skills at commit 6883275, republished under its Apache-2.0 licence (© google-deepmind). 694 words, ~1,646 tokens.

Download SKILL.mdSave it as .claude/skills/pymol/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
pymol
description
Visualize, analyze, and render protein and molecular structures using PyMOL. Use when the user wants to create images of protein structures, perform structural alignments or superposition, measure distances or contacts, highlight binding sites or active site residues, color by B-factor/pLDDT, or analyze protein-ligand interactions. Do not use for docking, molecular dynamics, or sequence-only analysis.

PyMOL

Prerequisites

  1. uv: Read the uv skill and follow its Setup instructions to ensure uv is installed and on PATH.
  2. User Notification: If .licenses/pymol_LICENSE.txt does not already exist in the workspace root directory then (1) prominently notify the user to check the license at https://www.pymol.org/, then (2) create the file recording the notification text and timestamp.

Overview

All PyMOL commands run on the host via uv with OSMesa software rendering — no GPU, display, or X server is needed. Structure files must be downloaded to the host before running PyMOL.

Do NOT use when:

  • The user wants to run AlphaFold predictions.
  • The user wants docking or molecular dynamics simulations.
  • The user only has a sequence and no structure file — fetch the structure first. Check if any other installed skills can retrieve structures from the PDB or AlphaFold Database before proceeding.

Setup (Agent Instructions)

Ensure that uv is installed on the host system. The PyMOL scripts use PEP 0723 headers to declare their dependencies, and uv run will automatically handle installing them (including pymol-open-source-whl) when the script is executed.

Core Rules

  • Output paths must be absolute or relative to the user's project root. Always run PyMOL scripts from the user's project directory.
  • Software rendering only. Use cmd.png() for output. Never use cmd.draw() or cmd.ray() with hardware acceleration — OSMesa does not support it. Set environment variable PYOPENGL_PLATFORM=osmesa for headless rendering.
  • Always save a .pse session file alongside any PNG output. This lets the user open the session in their local PyMOL for further inspection.
  • Always call cmd.quit() at the end of every PyMOL script. Omitting it causes the process to stop responding.
  • Init boilerplate is mandatory. Every PyMOL script must begin with the initialization sequence. from pymol import cmd must come after finish_launching(), not before.
  • See references/PYMOL_REFERENCE.md for selection syntax, common commands, and gotchas.
  • Pre-Flight File Check: Before writing the PyMOL script or running it, you MUST verify that the requested structure file actually exists on the host machine.
  • Verify Structure Load: After loading a structure with cmd.load(), always verify it succeeded by checking cmd.count_atoms("all"). If the result is 0, print an error to stdout and call cmd.quit() immediately.
  • Auto-detect Alpha-Carbon Trace: For a cartoon representation your PyMOL scripts should automatically detect if the structure is an alpha-carbon trace (cmd.count_atoms("name CA") == cmd.count_atoms("all")), then you MUST follow the Alpha carbon trace cartoon recipe.
  • Notification: If this skill is used, ensure this is mentioned in the output.
Show full SKILL.md (290 more words)Show less

Quick Start

  • Ensure structure files are downloaded to a directory in the user's project.
  • Write a PyMOL Python script (e.g., render.py) with the required init boilerplate and PEP 0723 header.
  • Run it via uv run: bash uv run render.py
Minimal example script (render.py)
python
# /// script
# requires-python = ">=3.10, <3.13"
# dependencies = [
#     "pymol-open-source-whl",
# ]
# ///

import os
import sys

# Set environment variable for headless rendering
os.environ["PYOPENGL_PLATFORM"] = "osmesa"

import pymol # pytype: disable=import-error
pymol.pymol_argv = ["pymol", "-cq"]
pymol.finish_launching()

from pymol import cmd # pytype: disable=import-error

cmd.load("AF-P00520-F1-model_v4.cif", "structure")
cmd.show("cartoon")
cmd.color("green", "ss h")
cmd.color("yellow", "ss s")
cmd.color("gray", "ss l+''")
cmd.orient()
cmd.set("ray_opaque_background", 1)
cmd.png("output/render.png", width=1200, height=900, dpi=150)
cmd.save("output/session.pse")
cmd.quit()

Common Recipes

See references/RECIPES.md for complete, copy-paste ready recipes. Available recipes:

  • Cartoon with secondary structure coloring — basic helix/sheet/loop coloring
  • Alpha carbon trace cartoon — force cartoon representation for CA-only structures
  • B-factor (pLDDT) coloring — continuous spectrum coloring by B-factor
  • AlphaFold pLDDT coloring — canonical threshold-based confidence colors
  • Highlight specific residues — show active site or key residues as sticks
  • Surface rendering — transparent surface over cartoon
  • Electrostatic surface rendering — vacuum electrostatics (qualitative)
  • Multi-chain complex colors — automatic per-chain coloring
  • B-factor putty analysis — tube width proportional to flexibility
  • Cavity and pocket visualization — surface cavity detection with ligand focus
  • Multi-structure batch rendering — render a directory of structures
  • Measure distance between residues — CA–CA distance with labels
  • Zoom into binding pocket — simple pocket focus
  • Protein-ligand interaction — ligand isolation, styled rendering, polar contacts
  • Two-structure superposition with RMSD — align/cealign with auto-fallback
  • In silico mutagenesis — mutate residues with the mutagenesis wizard
  • Load and modify an existing session — re-open a .pse file

Interpreting Output

  • The output/ directory contains PNG images and a .pse session file.
  • Any measurements or metrics (distances, RMSD, atom counts) are printed to stdout by the PyMOL script. Report these values to the user.
  • Present PNG images to the user and describe the visualization.
  • Tell the user they can open the .pse file in their local PyMOL to further explore, rotate, or modify the visualization.
  • If the user wants modifications, load the saved .pse in a new script and re-run.
  • Large sessions with surfaces can exceed the --max_output_mb limit (default 500 MB). Increase it with --max_output_mb=1000 if needed.

© google-deepmind, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (references) in skills/pymol of google-deepmind/science-skills.

  • SKILL.md
  • references/PYMOL_REFERENCE.md
  • references/RECIPES.md
  • references/citation.bib

Open the folder on GitHubat commit 6883275

Used in 2 other repositories

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in google-deepmind/science-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Pymol next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Pymol compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Pymol this skillgoogle-deepmind/science-skills3.2k2 repos~1.6kAutomated safety check: PassApache-2.0
TamarindK-Dense-AI/scientific-agent-skills48k1 repos~3.4kAutomated safety check: PassMIT
Database Lookupmajiayu000/claude-skill-registry6661 repos~7kAutomated safety check: NotesMIT
Alphafoldadaptyvbio/protein-design-skills1634 repos~1.2kAutomated safety check: PassMIT
Pymol VisualizationChatMol/ChatMol372—~1.2kAutomated safety check: PassMIT
Chaiadaptyvbio/protein-design-skills1634 repos~1.5kAutomated safety check: PassMIT

Similar skills

  • Tamarind

    K-Dense-AI/scientific-agent-skills

    Provides access to a collection of open-source molecular design and structural biology tools on the Tamarind Bio platform, via its REST API or MCP server — no local GPUs required.

    48k GitHub starsUsed in 1 repo~3.4k tokens
    Research & ScienceAuto-check passed
  • Database Lookup

    majiayu000/claude-skill-registry

    Search 78 public scientific, biomedical, materials science, and economic databases via REST APIs.

    666 GitHub starsUsed in 1 repo~7k tokens
    Research & ScienceAuto-check: notes
  • Alphafold

    adaptyvbio/protein-design-skills

    Validate protein designs using AlphaFold2 structure prediction.

    163 GitHub starsUsed in 4 repos~1.2k tokens
    Research & ScienceAuto-check passed
  • Pymol Visualization

    ChatMol/ChatMol

    Generate publication-quality molecular visualization images using PyMOL.

    372 GitHub stars~1.2k tokensUpdated 6 mo ago
    Research & ScienceAuto-check passed
  • Chai

    adaptyvbio/protein-design-skills

    Structure prediction using Chai-1, a foundation model for molecular structure.

    163 GitHub starsUsed in 4 repos~1.5k tokens
    Research & ScienceAuto-check passed
  • Biopipelines

    locbp-uzh/biopipelines

    Design and run computational protein and ligand workflows on a GPU: binder and enzyme design, de novo backbone generation, inverse folding and sequence redesign, structure prediction, protein-ligand…

    109 GitHub stars~2.4k tokensUpdated 7 days ago
    Research & ScienceAuto-check passed

More from google-deepmind/science-skills

All 40 skills in this repo
  • Alphafold Database Fetch And Analyze

    google-deepmind/science-skills

    Retrieve and analyze AlphaFold predicted structures for a protein.

    3.2k GitHub starsUsed in 2 repos~1.2k tokens
    Auto-check passed
  • Alphagenome Single Variant Analysis

    google-deepmind/science-skills

    Analyzes genetic variant effects on gene expression (RNA-seq), chromatin accessibility (DNASE), histone marks (ChIP), and transcription factors using the AlphaGenome API.

    3.2k GitHub starsUsed in 2 repos~3k tokens
    Auto-check: notes
  • Chembl Database

    google-deepmind/science-skills

    Query the ChEMBL database for bioactive molecules, drug targets, bioactivity data, approved drugs, and chemical structures.

    3.2k GitHub starsUsed in 2 repos~2.9k tokens
    Auto-check passed
  • Clinical Trials Database

    google-deepmind/science-skills

    Query ClinicalTrials.gov via APIv2. An agent skill from google-deepmind/science-skills.

    3.2k GitHub starsUsed in 2 repos~3.2k tokens
    Auto-check passed
  • Clinvar Database

    google-deepmind/science-skills

    A skill your agent uses when needing clinical significance, pathogenicity classifications (e.g., Pathogenic, Benign, VUS), clinical evidence rationales, or finding "hard positive" benchmark controls…

    3.2k GitHub starsUsed in 2 repos~3.9k tokens
    Auto-check: notes
  • Dbsnp Database

    google-deepmind/science-skills

    A skill your agent uses when you want to look up, map, and search for short genetic variants (SNPs, indels) in NCBI's dbSNP database.

    3.2k GitHub starsUsed in 2 repos~3.4k tokens
    Auto-check: notes

Works with

Questions about Pymol

What does Pymol do?

Visualize, analyze, and render protein and molecular structures using PyMOL. Pymol is an agent skill from google-deepmind/science-skills. Visualize, analyze, and render protein and molecular structures using PyMOL.

When should I use Pymol?

Pymol fits situations like: the user wants to create images of protein structures; perform structural alignments; measure distances; highlight binding sites.

How do I install Pymol in Claude Code?

Run `npx skills add google-deepmind/science-skills --skill pymol -a claude-code`. Or copy the skill folder (skills/pymol in google-deepmind/science-skills) into .claude/skills/pymol in your project. Claude Code loads it when a task matches its description.

How do I install Pymol in Codex?

Run `npx skills add google-deepmind/science-skills --skill pymol -a codex`. Or copy the skill folder (skills/pymol in google-deepmind/science-skills) into .agents/skills/pymol in your project. Codex loads it when a task matches its description.

Can I use Pymol in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add google-deepmind/science-skills --skill pymol -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/pymol, .gemini/skills/pymol, .github/skills/pymol and .opencode/skills/pymol in your project.

What does Pymol need to run?

Going by SKILL.md and its folder, Pymol needs the command-line tools its instructions call (uv and bash). Our summary lists: Python 3.

Does Pymol access the network?

SKILL.md names 1 domain. As links in the text: pymol.org. This is read from the text; nothing was executed.

Is Pymol safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Pymol use?

Pymol is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Pymol use?

About 1.6k tokens (SKILL.md is roughly 6.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.2k tokens, read only when the agent opens those files.

What are the alternatives to Pymol?

Skills that share tags, products or a category with Pymol: Tamarind (K-Dense-AI/scientific-agent-skills, 48k stars), Database Lookup (majiayu000/claude-skill-registry, 666 stars), Alphafold (adaptyvbio/protein-design-skills, 163 stars) and Pymol Visualization (ChatMol/ChatMol, 372 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Pymol?

google-deepmind (a GitHub organization) maintains it in google-deepmind/science-skills, which has 3,216 GitHub stars. The repository holds 40 skills in this directory. The repository was last updated on September 15, 2026.

Source: google-deepmind/science-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.