Science Skills is a collection of add-ons that give AI agents structured instructions, scripts, and references for scientific research, including genomics, structural biology, cheminformatics, and literature search. Researchers use it to guide agents through specialized scientific tasks with information from databases and tools such as AlphaGenome, AFDB, and UniProt. The catalogue entries are individual skills from this collection.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add google-deepmind/science-skills --skill pymolgit clone --depth 1 https://github.com/google-deepmind/science-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/google-deepmind/science-skills/pymol)<a href="https://agentmods.dev/skills/google-deepmind/science-skills/pymol"><img src="https://agentmods.dev/badge/skills/google-deepmind/science-skills/pymol.svg" alt="Measured on agentmods" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00084 | $0.01537 |
| Opus 5 | $0.00042 | $0.00768 |
| Sonnet 5 | $0.00017 | $0.00307 |
| Haiku 4.5 | $0.00008 | $0.00154 |
Grade A, and why
pymol scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PyMOL
Prerequisites
uv: Read theuvskill and follow its Setup instructions to ensureuvis installed and on PATH.- 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 usecmd.draw()orcmd.ray()with hardware acceleration — OSMesa does not support it. Set environment variablePYOPENGL_PLATFORM=osmesafor headless rendering. - Always save a
.psesession 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 cmdmust come afterfinish_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 checkingcmd.count_atoms("all"). If the result is 0, print an error to stdout and callcmd.quit()immediately. - Notification: If this skill is used, ensure this is mentioned in the output.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 147 lines · 84 tokens per session scan A a9e65d014645
pymol is a skill published in the GitHub repository google-deepmind/science-skills (2,863 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 84 tokens to every session and 1,537 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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