Agent skill

Pymol Visualization

by ChatMol in ChatMol/ChatMol

Generate publication-quality molecular visualization images using PyMOL.

MITAuto-check passedResearch & Science

Install Pymol Visualization

skills CLI
$ npx skills add ChatMol/ChatMol --skill pymol-visualization -a claude-code

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

GitHub CLI
$ gh skill install ChatMol/ChatMol pymol-visualization --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/ChatMol/ChatMol.git skills-src && mkdir -p .claude/skills && cp -r skills-src/pymol_skill .claude/skills/pymol-visualization && 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-visualization
GitHub stars
372
Token cost
~1.2k tokens
SKILL.md length
274 words
Files
2 (incl. references)
Skills in repo
1
Repo updated
First seen
Licence
MIT

At a glance

Generate publication-quality molecular visualization images using PyMOL.

  • Works in 4 steps: Ask the User → Write a .pml Script and Run It → Read the Reference Before Writing → …
  • The user mentions PyMOL
  • SKILL.md covers Prerequisites, Workflow, Script Template and Essential Patterns, plus 1 more section
  • Calls conda

What it does

Pymol Visualization is an agent skill from ChatMol/ChatMol. Generate publication-quality molecular visualization images using PyMOL. Use this skill whenever the user mentions PyMOL, molecular visualization, protein rendering, structure figures, PDB visualization, ray tracing molecules, or wants to create images of proteins, ligands, binding sites, protein-protein interactions, or any biomolecular structure. Also trigger when the user wants to: make a figure of a crystal structure for a paper or presentation; render a protein surface; show binding pockets; visualize…

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/recipes.md`).

It sits in Research & Science, covering Protein structure and design, Data visualization and Physical and earth sciences. The licence is MIT.

When your agent uses it

  • The user mentions PyMOL
  • Molecular visualization
  • Protein rendering
  • Structure figures

Example prompts

  • “t mention”
  • “/pymol-visualization”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Ask the User
  2. Write a .pml Script and Run It
  3. Read the Reference Before Writing
  4. Deliver Output

What it can do on your machine

Read from SKILL.md and the folder at commit d376ccd. 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:

    • conda

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

  • Network

    No URLs in SKILL.md.

    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 Visualization loads about 1.2k tokens when it runs, and up to ~2.8k if it reads all its reference files. Until then it costs about 233 tokens; SKILL.md has 274 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~233
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.8k

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 ChatMol/ChatMol at commit d376ccd, republished under its MIT licence (© ChatMol). 274 words, ~1,154 tokens.

Download SKILL.mdSave it as .claude/skills/pymol-visualization/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
pymol-visualization
description
Generate publication-quality molecular visualization images using PyMOL. Use this skill whenever the user mentions PyMOL, molecular visualization, protein rendering, structure figures, PDB visualization, ray tracing molecules, or wants to create images of proteins, ligands, binding sites, protein-protein interactions, or any biomolecular structure. Also trigger when the user wants to: make a figure of a crystal structure for a paper or presentation; render a protein surface; show binding pockets; visualize protein-ligand interactions; create Goodsell-style illustrations; produce cartoon representations with highlighted residues; compare multiple structures side by side; or generate any molecular graphics output — even if they don't mention "PyMOL" by name. Trigger for any request involving PDB files, molecular surfaces, cartoon ribbons, stick models, electron density, or structural biology figures.

PyMOL Visualization Skill

Generate publication-quality molecular structure images using PyMOL.

Prerequisites

PyMOL must be installed. Check with:

bash
pymol -c -q -e "print('ok')" 2>/dev/null && echo "PyMOL available" || echo "PyMOL not found"

If missing: conda install -c conda-forge pymol-open-source.

Workflow

1. Ask the User

Before writing any script, clarify:

  • Structure: PDB ID, uploaded file path, or AlphaFold model?
  • Goal: What does the figure show? (e.g. protein overview, binding site, PPI interface, active site, mutation, surface, alignment)
  • Style preference: Any preferred colors or theme? Journal figure vs. presentation vs. artistic?
2. Write a .pml Script and Run It
bash
pymol -c -q script.pml

-c = no GUI (headless), -q = quiet. For Python API logic, use pymol -c -q -r script.py.

3. Read the Reference Before Writing

Read references/recipes.md before writing — it contains scene-specific recipes and essential PyMOL commands organized by visualization goal.

4. Deliver Output

Always deliver three files:

  1. PNG image — the rendered figure
  2. PML script — so the user can reproduce or tweak
  3. PSE session — so the user can open in PyMOL GUI and adjust interactively

Save all to user's desktop and use present_files.

Script Template

Every script should follow this structure:

pml
reinitialize

# --- Load ---
fetch 4HHB, async=0
# or: load /path/to/structure.pdb, myprotein

# --- Clean ---
remove solvent
remove elem H
set valence, 0

# --- Base look ---
bg_color white
space cmyk
set ray_shadow, 0
set ray_trace_mode, 1
set antialias, 3
set ambient, 0.5
set spec_count, 5
set shininess, 50
set specular, 1
set reflect, 0.1
set orthoscopic, on
set opaque_background, off
set cartoon_oval_length, 1
set cartoon_rect_length, 1
set cartoon_discrete_colors, on
dss

# --- Representation (scene-specific) ---
hide everything
show cartoon
# ...

# --- Color ---
util.color_chains("(all) and elem C", _self=cmd)
util.cnc("all", _self=cmd)

# --- Camera ---
orient
# zoom sele, 8

# --- Save session BEFORE ray tracing ---
save /mnt/user-data/outputs/structure.pse

# --- Render ---
ray 2400, 1800
png /mnt/user-data/outputs/structure.png, dpi=150
quit

Essential Patterns

Show sidechains cleanly:

pml
cmd.show("sticks", "((byres (sele)) & (sc. | (n. CA) | (n. N & r. PRO)))")

Molecule-agnostic coloring:

pml
util.color_chains("(sele) and elem C", _self=cmd)
util.cnc("sele", _self=cmd)

Surface + cartoon as separate objects:

pml
create surf_obj, sele, zoom=0
show surface, surf_obj
set transparency, 0.5, surf_obj
cmd.color_deep("white", "surf_obj", 0)

Ball-and-stick for ligands:

pml
show sticks, ligand
show spheres, ligand
set sphere_scale, 0.25, ligand
set stick_radius, 0.15, ligand

Goodsell style (flat, illustrative):

pml
set ray_trace_mode, 3
set ray_trace_color, black
unset specular
set ray_trace_gain, 0
unset depth_cue
set ambient, 1.0
set direct, 0.0
set reflect, 0.0

Key Rules

  1. Always space cmyk for print colors
  2. Always remove elem H unless user needs hydrogens
  3. Always save .pse before ray tracing — this is the user's editable session
  4. set valence, 0 unless showing ligand bond orders
  5. Create separate objects for surface overlays (transparency is per-object)
  6. Use async=0 with fetch — otherwise structure isn't loaded when next command runs
  7. End script with quit — otherwise PyMOL hangs in batch mode
  8. Render large (1200x900+) — downscale later for quality

© ChatMol, MIT. 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 1 other file (references) in pymol_skill of ChatMol/ChatMol.

  • SKILL.md
  • references/recipes.md

Open the folder on GitHubat commit d376ccd

Compare with similar skills

Pymol Visualization 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 Visualization compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Pymol Visualization this skillChatMol/ChatMol372—~1.2kAutomated safety check: PassMIT
Pymolgoogle-deepmind/science-skills3.2k2 repos~1.6kAutomated safety check: PassApache-2.0
Hugging ScienceK-Dense-AI/scientific-agent-skills48k1 repos~2.9kAutomated safety check: NotesMIT
TamarindK-Dense-AI/scientific-agent-skills48k1 repos~3.4kAutomated safety check: PassMIT
Molecular DynamicsK-Dense-AI/scientific-agent-skills48k1 repos~4.7kAutomated safety check: PassMIT
Bio Structural Biology Structure PreparationGPTomics/bioSkills1.2k1 repos~4.9kAutomated safety check: PassMIT

Similar skills

  • Pymol

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    Research & ScienceAuto-check passed
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  • Molecular Dynamics

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  • Hugging Science

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Questions about Pymol Visualization

What does Pymol Visualization do?

Generate publication-quality molecular visualization images using PyMOL. Pymol Visualization is an agent skill from ChatMol/ChatMol. Generate publication-quality molecular visualization images using PyMOL.

When should I use Pymol Visualization?

Pymol Visualization fits situations like: the user mentions PyMOL; molecular visualization; protein rendering; structure figures.

How do I install Pymol Visualization in Claude Code?

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

How do I install Pymol Visualization in Codex?

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

Can I use Pymol Visualization 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 ChatMol/ChatMol --skill pymol-visualization -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-visualization, .gemini/skills/pymol-visualization, .github/skills/pymol-visualization and .opencode/skills/pymol-visualization in your project.

What does Pymol Visualization need to run?

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

Does Pymol Visualization access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Pymol Visualization 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 Visualization use?

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

How many tokens does Pymol Visualization use?

About 1.2k tokens (SKILL.md is roughly 4.6k 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 1.6k tokens, read only when the agent opens those files.

What are the alternatives to Pymol Visualization?

Skills that share tags, products or a category with Pymol Visualization: Pymol (google-deepmind/science-skills, 3.2k stars), Hugging Science (K-Dense-AI/scientific-agent-skills, 48k stars), Tamarind (K-Dense-AI/scientific-agent-skills, 48k stars) and Molecular Dynamics (K-Dense-AI/scientific-agent-skills, 48k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Pymol Visualization?

ChatMol (a GitHub organization) maintains it in ChatMol/ChatMol, which has 372 GitHub stars. The repository was last updated on March 16, 2026.

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