Pymol
google-deepmind/science-skills
Visualize, analyze, and render protein and molecular structures using PyMOL.
Generate publication-quality molecular visualization images using PyMOL.
$ npx skills add ChatMol/ChatMol --skill pymol-visualization -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install ChatMol/ChatMol pymol-visualization --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "pymol-visualization" agent skill from https://github.com/ChatMol/ChatMol/tree/main/pymol_skill into .claude/skills/pymol-visualization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pymol-visualization", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/ChatMol/ChatMol/tree/main/pymol_skillType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add ChatMol/ChatMol --skill pymol-visualization -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install ChatMol/ChatMol pymol-visualization --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ChatMol/ChatMol.git skills-src && mkdir -p .agents/skills && cp -r skills-src/pymol_skill .agents/skills/pymol-visualization && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "pymol-visualization" agent skill from https://github.com/ChatMol/ChatMol/tree/main/pymol_skill into .agents/skills/pymol-visualization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pymol-visualization", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ChatMol/ChatMol --skill pymol-visualization -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install ChatMol/ChatMol pymol-visualization --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ChatMol/ChatMol.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/pymol_skill .cursor/skills/pymol-visualization && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "pymol-visualization" agent skill from https://github.com/ChatMol/ChatMol/tree/main/pymol_skill into .cursor/skills/pymol-visualization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pymol-visualization", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/ChatMol/ChatMol.git --path pymol_skill--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add ChatMol/ChatMol --skill pymol-visualization -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install ChatMol/ChatMol pymol-visualization --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ChatMol/ChatMol.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/pymol_skill .gemini/skills/pymol-visualization && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "pymol-visualization" agent skill from https://github.com/ChatMol/ChatMol/tree/main/pymol_skill into .gemini/skills/pymol-visualization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pymol-visualization", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install ChatMol/ChatMol pymol-visualizationInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add ChatMol/ChatMol --skill pymol-visualization -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/ChatMol/ChatMol.git skills-src && mkdir -p .github/skills && cp -r skills-src/pymol_skill .github/skills/pymol-visualization && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "pymol-visualization" agent skill from https://github.com/ChatMol/ChatMol/tree/main/pymol_skill into .github/skills/pymol-visualization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pymol-visualization", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ChatMol/ChatMol --skill pymol-visualization -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install ChatMol/ChatMol pymol-visualization --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ChatMol/ChatMol.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/pymol_skill .opencode/skills/pymol-visualization && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "pymol-visualization" agent skill from https://github.com/ChatMol/ChatMol/tree/main/pymol_skill into .opencode/skills/pymol-visualization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pymol-visualization", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
pymol-visualizationGenerate publication-quality molecular visualization images using PyMOL.
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.
4 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit d376ccd. It shows what the files ask for, not the result of running them.
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.
Shell commands in SKILL.md call:
condaFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from ChatMol/ChatMol at commit d376ccd, republished under its MIT licence (© ChatMol). 274 words, ~1,154 tokens.
.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.Generate publication-quality molecular structure images using PyMOL.
PyMOL must be installed. Check with:
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.
Before writing any script, clarify:
pymol -c -q script.pml-c = no GUI (headless), -q = quiet. For Python API logic, use pymol -c -q -r script.py.
Read references/recipes.md before writing — it contains scene-specific recipes and
essential PyMOL commands organized by visualization goal.
Always deliver three files:
Save all to user's desktop and use present_files.
Every script should follow this structure:
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
quitShow sidechains cleanly:
cmd.show("sticks", "((byres (sele)) & (sc. | (n. CA) | (n. N & r. PRO)))")Molecule-agnostic coloring:
util.color_chains("(sele) and elem C", _self=cmd)
util.cnc("sele", _self=cmd)Surface + cartoon as separate objects:
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:
show sticks, ligand
show spheres, ligand
set sphere_scale, 0.25, ligand
set stick_radius, 0.15, ligandGoodsell style (flat, illustrative):
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, 0.0space cmyk for print colorsremove elem H unless user needs hydrogens.pse before ray tracing — this is the user's editable sessionset valence, 0 unless showing ligand bond ordersasync=0 with fetch — otherwise structure isn't loaded when next command runsquit — otherwise PyMOL hangs in batch mode© ChatMol, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 1 other file (references) in pymol_skill of ChatMol/ChatMol.
Open the folder on GitHubat commit d376ccd
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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Pymol Visualization this skillChatMol/ChatMol | 372 | — | ~1.2k | Automated safety check: Pass | MIT | |
| Pymolgoogle-deepmind/science-skills | 3.2k | 2 repos | ~1.6k | Automated safety check: Pass | Apache-2.0 | |
| Hugging ScienceK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~2.9k | Automated safety check: Notes | MIT | |
| TamarindK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~3.4k | Automated safety check: Pass | MIT | |
| Molecular DynamicsK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~4.7k | Automated safety check: Pass | MIT | |
| Bio Structural Biology Structure PreparationGPTomics/bioSkills | 1.2k | 1 repos | ~4.9k | Automated safety check: Pass | MIT |
google-deepmind/science-skills
Visualize, analyze, and render protein and molecular structures using PyMOL.
K-Dense-AI/scientific-agent-skills
Discovers and evaluates scientific datasets, models, methodology posts, and Spaces through the Hugging Science catalog.
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.
K-Dense-AI/scientific-agent-skills
Runs and analyzes molecular dynamics simulations with OpenMM and MDAnalysis.
GPTomics/bioSkills
Prepares a deposited or predicted structure for docking, molecular dynamics, or electrostatics by adding hydrogens, assigning protonation and tautomer states, and filling missing atoms and short…
majiayu000/claude-skill-registry
A skill your agent uses when the user is doing AI/ML work in a scientific domain such as biology, chemistry, physics, astronomy, climate, genomics, materials, medicine, ecology, energy, engineering…
Categories
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.
Pymol Visualization fits situations like: the user mentions PyMOL; molecular visualization; protein rendering; structure figures.
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.
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.
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.
Going by SKILL.md and its folder, Pymol Visualization needs the command-line tools its instructions call (conda). Our summary lists: Python 3.
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.
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.
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.
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.
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.
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.