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.
Visualize, analyze, and render protein and molecular structures using PyMOL.
$ npx skills add google-deepmind/science-skills --skill pymol -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install google-deepmind/science-skills pymol --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/google-deepmind/science-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/pymol .claude/skills/pymol && 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" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/pymol into .claude/skills/pymol/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pymol", 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/google-deepmind/science-skills/tree/main/skills/pymolType 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 google-deepmind/science-skills --skill pymol -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install google-deepmind/science-skills pymol --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google-deepmind/science-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/pymol .agents/skills/pymol && 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" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/pymol into .agents/skills/pymol/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pymol", 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 google-deepmind/science-skills --skill pymol -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install google-deepmind/science-skills pymol --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google-deepmind/science-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/pymol .cursor/skills/pymol && 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" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/pymol into .cursor/skills/pymol/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pymol", 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/google-deepmind/science-skills.git --path skills/pymol--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 google-deepmind/science-skills --skill pymol -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install google-deepmind/science-skills pymol --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google-deepmind/science-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/pymol .gemini/skills/pymol && 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" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/pymol into .gemini/skills/pymol/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pymol", 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 google-deepmind/science-skills pymolInstalls 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 google-deepmind/science-skills --skill pymol -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/google-deepmind/science-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/pymol .github/skills/pymol && 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" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/pymol into .github/skills/pymol/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pymol", 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 google-deepmind/science-skills --skill pymol -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install google-deepmind/science-skills pymol --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google-deepmind/science-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/pymol .opencode/skills/pymol && 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" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/pymol into .opencode/skills/pymol/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pymol", 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.
pymolVisualize, 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. 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.
2 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 6883275. 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:
uvbashFrom the folder's file list and the shell code blocks in SKILL.md.
Links to these hosts (documentation or services it may open):
pymol.orgFrom 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 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.
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 google-deepmind/science-skills at commit 6883275, republished under its Apache-2.0 licence (© google-deepmind). 694 words, ~1,646 tokens.
.claude/skills/pymol/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.uv: Read the uv skill and follow its Setup instructions to ensure
uv is installed and on PATH.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:
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.
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..pse session file alongside any PNG output. This lets
the user open the session in their local PyMOL for further inspection.cmd.quit() at the end of every PyMOL script. Omitting it
causes the process to stop responding.from pymol import cmd must come after
finish_launching(), not before.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.cmd.count_atoms("name CA") == cmd.count_atoms("all")),
then you MUST follow the Alpha carbon trace cartoon recipe.render.py) with the required init
boilerplate and PEP 0723 header.uv run: bash uv run render.pyrender.py)# /// 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()See references/RECIPES.md for complete, copy-paste ready recipes. Available recipes:
.pse fileoutput/ directory contains PNG images and a .pse session file..pse file in their local PyMOL to further
explore, rotate, or modify the visualization..pse in a new script and
re-run.--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
SKILL.md and 3 other files (references) in skills/pymol of google-deepmind/science-skills.
Open the folder on GitHubat commit 6883275
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.
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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Pymol this skillgoogle-deepmind/science-skills | 3.2k | 2 repos | ~1.6k | Automated safety check: Pass | Apache-2.0 | |
| TamarindK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~3.4k | Automated safety check: Pass | MIT | |
| Database Lookupmajiayu000/claude-skill-registry | 666 | 1 repos | ~7k | Automated safety check: Notes | MIT | |
| Alphafoldadaptyvbio/protein-design-skills | 163 | 4 repos | ~1.2k | Automated safety check: Pass | MIT | |
| Pymol VisualizationChatMol/ChatMol | 372 | — | ~1.2k | Automated safety check: Pass | MIT | |
| Chaiadaptyvbio/protein-design-skills | 163 | 4 repos | ~1.5k | Automated safety check: Pass | MIT |
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.
majiayu000/claude-skill-registry
Search 78 public scientific, biomedical, materials science, and economic databases via REST APIs.
adaptyvbio/protein-design-skills
Validate protein designs using AlphaFold2 structure prediction.
ChatMol/ChatMol
Generate publication-quality molecular visualization images using PyMOL.
adaptyvbio/protein-design-skills
Structure prediction using Chai-1, a foundation model for molecular structure.
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…
google-deepmind/science-skills
Retrieve and analyze AlphaFold predicted structures for a protein.
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.
google-deepmind/science-skills
Query the ChEMBL database for bioactive molecules, drug targets, bioactivity data, approved drugs, and chemical structures.
google-deepmind/science-skills
Query ClinicalTrials.gov via APIv2. An agent skill from google-deepmind/science-skills.
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…
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.
Works with
Categories
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.
Pymol fits situations like: the user wants to create images of protein structures; perform structural alignments; measure distances; highlight binding sites.
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.
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.
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.
Going by SKILL.md and its folder, Pymol needs the command-line tools its instructions call (uv and bash). Our summary lists: Python 3.
SKILL.md names 1 domain. As links in the text: pymol.org. 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 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.
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.
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.
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.