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 protein_sequence_msagit 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/protein_sequence_msa)<a href="https://agentmods.dev/skills/google-deepmind/science-skills/protein_sequence_msa"><img src="https://agentmods.dev/badge/skills/google-deepmind/science-skills/protein_sequence_msa/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/google-deepmind/science-skills/protein_sequence_msa"><img src="https://agentmods.dev/badge/skills/google-deepmind/science-skills/protein_sequence_msa.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00111 | $0.01361 |
| Opus 5.5 | $0.00044 | $0.00544 |
| Sonnet 5.5 | $0.00022 | $0.00272 |
| Haiku 4.5 | $0.00011 | $0.00136 |
Grade A, and why
protein-sequence-msa scanned grade A with 1 finding 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 1mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
`scripts/msa_align.py` rather than writing your own curl or custom Python How it starts
The opening of the file, as written. The whole thing — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prerequisites
uv: Read theuvskill and follow its Setup instructions to ensureuvis installed and on PATH.- User Notification: If .licenses/protein_sequence_msa_LICENSE.txt does not already exist in the workspace root directory then (1) prominently notify the user to check the terms at https://www.ebi.ac.uk/jdispatcher/msa/clustalo and https://www.ebi.ac.uk/about/terms-of-use/, then (2) create the file recording the notification text and timestamp.
.envfile: Make sure the.envfile exists in your home directory. Create one if it does not exist.USER_EMAIL: Required by the wrapper script for Clustal Omega job tracking (recommended by the EBI). You MUST use the safe credentials protocol in thecredentialsskill to check for and request this credential if this skill looks relevant to the user's request.
Core Rules
- Use the Wrapper: ALWAYS execute the alignment using
scripts/msa_align.pyrather than writing your own curl or custom Python requests. The script automatically enforces the required rate limit to respect EBI's Terms of Use. - Notification: If this skill is used, ensure this is mentioned in the output.
- Always state the method: Every report must clearly state that the alignment was performed using EBI Clustal Omega.
- No Hallucinations: Do NOT invent alignments or conservation metrics. Report only what is present in the alignment file.
Goal
Take a file containing multiple protein sequences in FASTA format, perform multiple sequence alignment using the EBI Clustal Omega API, save the resulting alignment locally for future programmatic analysis, and interpret the results towards addressing the user's specific research objective (e.g., assessing similarity, identifying conserved domains, or analyzing key residues).
Instructions
- Prepare Input File: The input must be a plain text file containing two
or more protein sequences in FASTA format. Each sequence header must start
with a
>symbol. Example:
What ships with it
2 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.
- 1mo ago First seen · 123 lines · 111 tokens per session scan A 899c45684b07
protein-sequence-msa is a skill published in the GitHub repository google-deepmind/science-skills (3,315 stars, last pushed 7d ago), licensed Apache-2.0. It adds 111 tokens to every session and 1,361 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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