agentmods.dev

alphagenome-single-variant-analysis

alphagenome-single-variant-analysis is a skill for Claude Code, Codex from google-deepmind/science-skills. It costs 118 tokens per session (2,925 once invoked), scanned A, original, Apache-2.0.

A tool for estimating how a genetic variant may affect the activity of DNA regions. It uses the AlphaGenome API to analyse gene expression, chromatin accessibility, histone marks, and transcription-factor signals.

In plain words
What is it for?
Use it to study non-coding variants, including their possible effects on genes, regulatory DNA, disease-related biology, and functional or clinical interpretation.
Why use it?
It helps investigate possible effects of variants outside protein-coding regions, where the consequences are often harder to assess directly. The service requires an API key and acceptance of its terms.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents.

not rated 3.0krepo +125 changed today A scan SkillSpector: warn 118 tokens original Apache-2.0

Good fit Use it to study non-coding variants, including their possible effects on genes, regulatory DNA, disease-related biology, and functional or clinical interpretation.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/google-deepmind/science-skills/alphagenome_single_variant_analysis
View source ↗ google-deepmind/science-skills antigravity.google
About the project

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.

google-deepmind/science-skills · 2,960 stars · on GitHub · antigravity.google

Install

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.

Any agent
npx skills add google-deepmind/science-skills --skill alphagenome_single_variant_analysis
Clone the repo
git clone --depth 1 https://github.com/google-deepmind/science-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for alphagenome-single-variant-analysis

README.md
[![agentmods](https://agentmods.dev/badge/skills/google-deepmind/science-skills/alphagenome_single_variant_analysis/github.svg)](https://agentmods.dev/skills/google-deepmind/science-skills/alphagenome_single_variant_analysis)
Your own site
<a href="https://agentmods.dev/skills/google-deepmind/science-skills/alphagenome_single_variant_analysis"><img src="https://agentmods.dev/badge/skills/google-deepmind/science-skills/alphagenome_single_variant_analysis/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.

agentmods 80×15 button for alphagenome-single-variant-analysis

Your own site · 80×15
<a href="https://agentmods.dev/skills/google-deepmind/science-skills/alphagenome_single_variant_analysis"><img src="https://agentmods.dev/badge/skills/google-deepmind/science-skills/alphagenome_single_variant_analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 118 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,925 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 3 findings, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • high Privilege Escalation · line 161
    Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.
    Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
  • high Privilege Escalation · line 162
    Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.
    Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
  • medium Rogue Agent · line 24
    Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.
    Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00118 $0.02925
Opus 5 $0.00059 $0.01463
Sonnet 5 $0.00024 $0.00585
Haiku 4.5 $0.00012 $0.00293

Measured today against content hash bd663b06a6b0, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

alphagenome-single-variant-analysis 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 today.

The scan reads SKILL.md. This mod also ships 7 executable files (scripts/analyze_ism.py, scripts/generate_ontology_mapping.py, scripts/interpret_splicing.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/alphagenome_single_variant_analysis/SKILL.md · 260 lines

How it starts

The opening of the file, as written. The whole thing — 260 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Variant Analysis using AlphaGenome

Prerequisites

  1. uv: Read the uv skill and follow its Setup instructions to ensure uv is installed and on PATH.

  2. User Notification: If .licenses/alphagenome_single_variant_analysis_LICENSE.txt does not already exist in the workspace root directory then (1) prominently notify the user to check the terms at https://deepmind.google.com/science/alphagenome/, then (2) create the file recording the notification text and timestamp.

  3. .env file: Make sure the .env file exists in your home directory. Create one if it does not exist.

  4. ALPHAGENOME_API_KEY: This skill requires an API key to function.

    You can register for a key at https://deepmind.google.com/science/alphagenome/. You MUST use the safe credentials protocol in the credentials skill to check for and request this key if this skill looks relevant to the user's request.

  5. ALPHAGENOME_GTF_PATH (Optional): Accelerate gene/transcript lookup by pointing to a local copy of the GTF feather file instead of downloading from GCS:

    echo "ALPHAGENOME_GTF_PATH=/path/to/local/gencode.v46.annotation.gtf.gz.feather" >> ~/.env

Core Rules

  • NEVER run python3 or python3 -c directly. The system Python does not necessarily have pandas, numpy, and other key dependencies. ALWAYS use uv run to run ALL Python code — including scripts, ad-hoc analysis files, and one-liners. Do not attempt to pip install or create new venvs — uv manages an isolated environment automatically.
  • Offline Only: NEVER use external APIs (e.g., MyGene.info, Ensembl REST) for gene/transcript lookup. Use lookup_gene_info.py with the local GTF. If it fails, fix the environment/paths, do not switch to external APIs.
  • API Key is required: ALPHAGENOME_API_KEY must be set before running any script.
  • Notification: If this skill is used, ensure this is mentioned in the output.
  • Report Format: Always use the templates in docs/report-templates.md for generating analysis reports, and ensure to include the table of top hits from the discovery scan.

Read the full file on GitHub · 260 lines

Files

What ships with it

47 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.

Changes

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.

  1. today Changed · +8 lines bd663b06a6b0
  2. 10d ago First seen · 252 lines · 118 tokens per session scan A 92646a203073

Subscribe to this mod's changes

alphagenome-single-variant-analysis is a skill published in the GitHub repository google-deepmind/science-skills (2,960 stars, last pushed yesterday), licensed Apache-2.0. It adds 118 tokens to every session and 2,925 once invoked, about $0.0006 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.

Scientific
Related

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…

anthropics/knowledge-work-plugins · 123 tokens

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.

K-Dense-AI/scientific-agent-skills · 42 tokens

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…

K-Dense-AI/scientific-agent-skills · 83 tokens

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.

K-Dense-AI/scientific-agent-skills · 68 tokens

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.

davila7/claude-code-templates · 43 tokens

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…

maziyarpanahi/openmed · 205 tokens