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
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/google-deepmind/science-skillsnpx agentmods add skills/google-deepmind/science-skills/alphafold_database_fetch_and_analyzeWrote 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/alphafold_database_fetch_and_analyze)<a href="https://agentmods.dev/skills/google-deepmind/science-skills/alphafold_database_fetch_and_analyze"><img src="https://agentmods.dev/badge/skills/google-deepmind/science-skills/alphafold_database_fetch_and_analyze/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/alphafold_database_fetch_and_analyze"><img src="https://agentmods.dev/badge/skills/google-deepmind/science-skills/alphafold_database_fetch_and_analyze.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 | $0.00080 | $0.01110 |
| Opus 5 | $0.00040 | $0.00555 |
| Sonnet 5 | $0.00016 | $0.00222 |
| Haiku 4.5 | $0.00008 | $0.00111 |
Grade A, and why
alphafold-database-fetch-and-analyze 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AlphaFold Database: Fetch and Analyze
Prerequisites
uv: Read theuvskill and follow its Setup instructions to ensureuvis installed and on PATH.- User Notification: If .licenses/alphafold_database_fetch_and_analyze_LICENSE.txt does not already exist in the workspace root directory then (1) prominently notify the user to check the terms at https://alphafold.ebi.ac.uk/, then (2) create the file recording the notification text and timestamp.
Overview
Downloads AlphaFold predicted structures (mmCIF) and Predicted Aligned Error (PAE) matrices from the AlphaFold Database for a given UniProt ID, then performs automated heuristic analysis on structural confidence (pLDDT), intrinsically disordered regions, rigid domain boundaries, and inter-domain flexibility.
Do NOT use when:
- The user only has a protein name, gene name, or amino acid sequence (no UniProt ID) — ask them to look up the ID on UniProt.
- The user wants to search for structural homologs (use Foldseek).
- The user wants to run AlphaFold predictions on a custom sequence.
- The user needs experimental PDB structures (use RCSB PDB).
Core Rules
- Use the Wrapper: ALWAYS execute the provided helper scripts to query the database rather than accessing the database directly. The scripts automatically enforce the required rate limit gracefully.
- Do not attempt to calculate domain boundaries or assess structural disorder yourself; always rely on the output provided by the script.
- If this skill is used, ensure this is mentioned in the output.
Utility Scripts
1. Fetch Structure Files
Downloads the .cif structure file, _predicted_aligned_error.json, and API
metadata JSON (-metadata.json) for a UniProt ID. Handles fragment fallback for
very large proteins.
Examples:
uv run scripts/fetch_structure.py P00520 -o /path/to/output/
uv run scripts/fetch_structure.py P04637 -o /path/to/custom_results/
What ships with it
4 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 · 117 lines · 80 tokens per session scan A a16c4defe16c
alphafold-database-fetch-and-analyze 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 80 tokens to every session and 1,110 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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