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 alphagenome_variant_impact_scoregit 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/alphagenome_variant_impact_score)<a href="https://agentmods.dev/skills/google-deepmind/science-skills/alphagenome_variant_impact_score"><img src="https://agentmods.dev/badge/skills/google-deepmind/science-skills/alphagenome_variant_impact_score/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/alphagenome_variant_impact_score"><img src="https://agentmods.dev/badge/skills/google-deepmind/science-skills/alphagenome_variant_impact_score.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00088 | $0.04566 |
| Opus 5 | $0.00044 | $0.02283 |
| Sonnet 5 | $0.00018 | $0.00913 |
| Haiku 4.5 | $0.00009 | $0.00457 |
Grade A, and why
alphagenome-variant-impact-score 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 yesterday.
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 — 350 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AlphaGenome Variant Impact (AVI) Analysis
Score and prioritize genetic variants using AlphaGenome Variant Impact (AVI)
models via scripts/alphagenome_atlas_avi.py.
[!IMPORTANT] Research Use Only & Clinical Safety Rules: The AlphaGenome AVI Skill and the underlying AlphaGenome model/Atlas are strictly research tools. Access to outputs requires an AlphaGenome API key subject to terms of service prohibiting clinical use.
- No Medical Advice or Clinical Diagnosis: You MUST NOT provide medical advice, clinical diagnoses, disease management strategies, or treatment recommendations based on outputs from this skill or the AlphaGenome Atlas.
- Strict Molecular & Functional Framing: A high AVI score reflects predicted molecular/functional impact (e.g., disruption of splicing, alteration of transcription factor binding, chromatin accessibility changes, or coding consequences). Frame all findings in terms of molecular mechanisms and biological annotations—never as clinical diagnoses or medical conclusions.
- No Diagnostic Leaps: Never extrapolate high functional impact to clinical disease causation, penetrance, or patient prognosis. If a user asks a clinical or diagnostic question, explicitly clarify that AlphaGenome is a research tool and restrict your answer to the predicted molecular and functional effects.
[!IMPORTANT] Always Use AlphaGenome GENCODE v46 GTF (
scripts/alphagenome_atlas_avi.py gtf) for Gene Annotations: When retrieving gene models, transcript IDs, exon coordinates, CDS/UTR regions, or splice junction donor/acceptor boundaries, always use the built-inscripts/alphagenome_atlas_avi.py gtfcommand. Do NOT query external sources (e.g., Ensembl REST API, UCSC, external GTF databases, or NCBI) for gene annotations or transcript coordinates. This ensures that the annotations match the scores and website.
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.
- yesterday First seen · 350 lines · 88 tokens per session scan A 047e0b38f3eb
alphagenome-variant-impact-score is a skill published in the GitHub repository google-deepmind/science-skills (2,990 stars, last pushed 2d ago), licensed Apache-2.0. It adds 88 tokens to every session and 4,566 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 2026-09-09.
Other skills, from other repositories
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
phylogenetics
Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
mapping-to-snomed
Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…