13C Metabolic Flux Analysis
K-Dense-AI/scientific-agent-skills
Estimates reaction fluxes inside cells from steady-state carbon-13 labeling data with a bundled mfapy-based solver, and reports which fluxes the data pin down.
Constructs deep-links and URLs for the AlphaGenome Atlas website.
$ npx skills add google-deepmind/science-skills --skill alphagenome-atlas-website-links -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install google-deepmind/science-skills alphagenome-atlas-website-links --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/google-deepmind/science-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/alphagenome_atlas_website_links .claude/skills/alphagenome-atlas-website-links && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "alphagenome-atlas-website-links" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/alphagenome_atlas_website_links into .claude/skills/alphagenome-atlas-website-links/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "alphagenome-atlas-website-links", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/google-deepmind/science-skills/tree/main/skills/alphagenome_atlas_website_linksType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add google-deepmind/science-skills --skill alphagenome-atlas-website-links -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install google-deepmind/science-skills alphagenome-atlas-website-links --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google-deepmind/science-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/alphagenome_atlas_website_links .agents/skills/alphagenome-atlas-website-links && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "alphagenome-atlas-website-links" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/alphagenome_atlas_website_links into .agents/skills/alphagenome-atlas-website-links/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "alphagenome-atlas-website-links", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add google-deepmind/science-skills --skill alphagenome-atlas-website-links -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install google-deepmind/science-skills alphagenome-atlas-website-links --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google-deepmind/science-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/alphagenome_atlas_website_links .cursor/skills/alphagenome-atlas-website-links && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "alphagenome-atlas-website-links" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/alphagenome_atlas_website_links into .cursor/skills/alphagenome-atlas-website-links/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "alphagenome-atlas-website-links", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/google-deepmind/science-skills.git --path skills/alphagenome_atlas_website_links--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add google-deepmind/science-skills --skill alphagenome-atlas-website-links -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install google-deepmind/science-skills alphagenome-atlas-website-links --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google-deepmind/science-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/alphagenome_atlas_website_links .gemini/skills/alphagenome-atlas-website-links && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "alphagenome-atlas-website-links" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/alphagenome_atlas_website_links into .gemini/skills/alphagenome-atlas-website-links/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "alphagenome-atlas-website-links", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install google-deepmind/science-skills alphagenome-atlas-website-linksInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add google-deepmind/science-skills --skill alphagenome-atlas-website-links -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/google-deepmind/science-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/alphagenome_atlas_website_links .github/skills/alphagenome-atlas-website-links && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "alphagenome-atlas-website-links" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/alphagenome_atlas_website_links into .github/skills/alphagenome-atlas-website-links/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "alphagenome-atlas-website-links", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add google-deepmind/science-skills --skill alphagenome-atlas-website-links -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install google-deepmind/science-skills alphagenome-atlas-website-links --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google-deepmind/science-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/alphagenome_atlas_website_links .opencode/skills/alphagenome-atlas-website-links && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "alphagenome-atlas-website-links" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/alphagenome_atlas_website_links into .opencode/skills/alphagenome-atlas-website-links/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "alphagenome-atlas-website-links", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
alphagenome-atlas-website-linksConstructs deep-links and URLs for the AlphaGenome Atlas website.
Alphagenome Atlas Website Links is an agent skill from google-deepmind/science-skills. Constructs deep-links and URLs for the AlphaGenome Atlas website. Supports generating single-variant exploration links (1-based chr:pos:refalt), genomic locus views (1-based closed chr:start-end), candidate summary tables, and AlphaGenome reference vs. alternate predictions. Use whenever visualizing, exploring, charting, or linking genetic variants and genomic loci on the AlphaGenome Atlas, or when asked to inspect, view, or link predictions for a genomic variant.
Its SKILL.md is about 2.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including scripts (for example `scripts/alphagenome_atlas_links.py`).
It sits in Research & Science, covering Bioinformatics. The repository describes itself as: GDM Science Skills to speed up agentic scientific workflows with better grounding and higher token efficiency. Integrate insights from AlphaGenome, AFDB, UniProt and 30+ other… The licence is Apache-2.0.
4 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 8ab7672. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
uvFrom the folder's file list and the shell code blocks in SKILL.md.
Hosts in commands or code, which the agent is likely to contact:
deepmind.google.comFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Alphagenome Atlas Website Links loads about 2.9k tokens when it runs. Until then it costs about 125 tokens; SKILL.md has 932 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
The full file from google-deepmind/science-skills at commit 8ab7672, republished under its Apache-2.0 licence (© google-deepmind). 932 words, ~2,884 tokens.
.claude/skills/alphagenome-atlas-website-links/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.Construct and validate deep-links for the AlphaGenome Atlas web application
(https://deepmind.google.com/science/alphagenome/atlas).
Base URL: https://deepmind.google.com/science/alphagenome/atlas
[!IMPORTANT] Mandatory Atlas Deep-Linking with Variant Scores: Whenever presenting, discussing, or scoring genetic variants, you MUST always provide clickable deep-links to the AlphaGenome Atlas. Use
scripts/alphagenome_atlas_links.pyto automate link and table generation.
# 1. Single Variant Exploration Link:
uv run scripts/alphagenome_atlas_links.py variant "chr9:128225994:G>A" \
--biosample K562 \
--modalities RNA_SEQ,DNASE,CHIP_TF
# 2. Genomic Locus / Interval Link:
uv run scripts/alphagenome_atlas_links.py locus "chr11:5288500-5290500" \
--biosample K562 \
--modalities RNA_SEQ,DNASE,CHIP_TF
# 3. Format Candidate Variant Records Table (with embedded clickable links):
uv run scripts/alphagenome_atlas_links.py table --input top_variants.json --biosample K562
# 4. Construct Ref vs. Alt Track Predictions Link (/atlas/track-predictions):
uv run scripts/alphagenome_atlas_links.py track-predictions \
--variant "chr15:42387805:C>G" \
--gene CAPN3 \
--biosample "Muscle_Skeletal"q (string, Required): Primary search target. Supports 1-based
closed intervals (chr11:5288500-5290500), gene symbols (BRCA1), Ensembl
IDs (ENSG00000012048), or 1-based variants (chr7:27170000:A>G).m (enum, Optional): View mode. Defaults to entity for
genes/variants and locus for coordinate intervals. Use variant for
variant queries. (Allowed: locus, entity, variant, motifs).i (string, Optional): Centered viewport zoom interval in 1-based
closed chr:start-end format (e.g. chr11:5289310-5289690). Required for
automatic motif rendering.f (string, Optional): Comma-separated filter predicates in
KEY:VALUE format (e.g.
BIOSAMPLE_NAME:K562,SCORER_MODALITY:RNA-seq,ASSAY_TRANSCRIPTOR_FACTOR:GATA1).
Controls visible heatmap rows.lItems (string, Optional): Layout item sequence, AVI score track
toggle (avi), section heatmaps, and pinned tracks list (e.g.
avi,section:RNA_SEQ,section:DNASE,pinned:<TrackKey>).scores (string, Optional): Comma-separated list of ScoreId tokens
for the /atlas/track-predictions page comparison (e.g.
<ScoreId1>,<ScoreId2>).md (enum, Optional): Active modality tab selector on the track
predictions view (RNA_SEQ, SPLICE_JUNCTIONS, SPLICE_SITE_USAGE,
DNASE).tpRenames (string, Optional): Custom title overrides for specific
score predictions (ScoreId:CustomTitle).tpLegendTitle (string, Optional): Custom legend title for the track
predictions chart card (e.g. Predicted Gene Expression).[!IMPORTANT] Variant Query Format: Variants in
qmust strictly usechr:pos_1_based:ref>altformat (e.g.chr7:27170000:A>Gor URL-encodedchr7:27170000:A%3EG, where the position is 1-based). Do not use colon-separated alleles (A:G) or dbSNP rsIDs (rsIDs are unsupported).
f)Filters in f map to three primary evaluation groups:
Biosample Group (BIOSAMPLE_NAME, BIOSAMPLE_TYPE): Evaluated with
AND logic.Assay Group (SCORER_MODALITY, ASSAY_TRANSCRIPTOR_FACTOR,
ASSAY_HISTONE_MARK): Evaluated with OR logic.Gene Group (GENE_NAME): Evaluated with OR logic.RNA-seq and DNase tracks have no transcription factor code
(transcriptionFactorCode === ""). If f contains only
ASSAY_TRANSCRIPTOR_FACTOR filters under the Assay group, RNA-seq and DNase
tracks fail the Assay evaluation and are hidden from the heatmap.
To display RNA-seq and DNase tracks alongside specific ChIP-seq
transcription factors, explicitly include SCORER_MODALITY:RNA-seq and
SCORER_MODALITY:DNase in f (handled automatically by
scripts/alphagenome_atlas_links.py):
f=BIOSAMPLE_NAME:<CellLine>,SCORER_MODALITY:RNA-seq,SCORER_MODALITY:DNase,ASSAY_TRANSCRIPTOR_FACTOR:<TF1>,ASSAY_TRANSCRIPTOR_FACTOR:<TF2>lItems)avi): Including avi in lItems renders
the top-level AlphaGenome Variant Impact score track for the interval or
variant.section:<MODALITY>): Sections render full
unpinned heatmaps across all matching tracks for that modality (e.g.
section:RNA_SEQ, section:DNASE, section:CHIP_TF, section:ATAC,
section:CAGE).[!NOTE] Track-Specific Motif Guideline: Pinned Active-ISM tracks with motif instances and Contribution Weight Matrix (CWM) logos should only be added when specifically requested for individual tracks. Only a limited subset of tracks (such as key ChIP-TF or RNA-seq tracks relevant to the locus) support and benefit from pinned motif overlays. For standard exploration links, default section heatmaps (
avi,section:RNA_SEQ,section:DNASE,section:CHIP_TF) without pinned tracks are preferred.
Motif instances and CWM logos render exclusively on pinned tracks at base-pair resolution. General section heatmaps do not trigger motif footprint rendering.
pinned:<TrackMetadataName>:<StrandNumber>:<ScorerShortName>:heatmap:HEATMAP_TILESET_SOURCE_ACTIVE_ISM_SCORES:<TilesetId><TrackMetadataName>: Exact track name from production metadata proto,
URL-encoded (%20 for spaces).<StrandNumber>: 1 (STRAND_POSITIVE), 2 (STRAND_NEGATIVE), 3
(STRAND_UNSTRANDED).<ScorerShortName>: RNA_SEQ, CHIP_TF, DNASE, ATAC, CAGE,
PROCAP, CHIP_HISTONE.HEATMAP_TILESET_SOURCE_ACTIVE_ISM_SCORES: Required source identifier for
Active-ISM motif layers.<TilesetId>: Server-assigned tileset identifier (17354278441953531756
for current production).pinned:<PinnedKey> entries to lItems for the specific target
tracks only.i to base-pair resolution ($\le 1\text{ bp/px}$,
window $\le 380\text{ bp}$).f with cell line and transcription factors./atlas/track-predictions)The dedicated /atlas/track-predictions page compares predicted functional
profiles between the Reference and Alternate alleles for selected scores across
genomic windows:
https://deepmind.google.com/science/alphagenome/atlas/track-predictionsscripts/alphagenome_atlas_links.py track-predictions)Always construct track prediction URLs using scripts/alphagenome_atlas_links.py track-predictions. Manual ScoreId string formatting is error-prone due to
donor/acceptor skipping coordinates, strand orientation (+/-), and genic vs.
non-genic suffix rules. The script automatically handles coordinate extraction
from GENCODE v46, track catalog resolution, and URL synthesis.
# Variant & Gene:
uv run scripts/alphagenome_atlas_links.py track-predictions \
--variant "chr15:42387805:C>G" \
--gene CAPN3 \
--biosample "Muscle_Skeletal" \
--modalities SPLICE_JUNCTIONS,RNA_SEQ,DNASE,CHIP_TF \
--tf CTCF
# Interval/Locus query:
uv run scripts/alphagenome_atlas_links.py track-predictions \
--variant "chr15:42387805:C>G" \
--interval "chr15:41869312-42917888" \
--biosample "Muscle_Skeletal" \
--modalities SPLICE_JUNCTIONS,RNA_SEQ,DNASE,CHIP_TFtrack-predictions--variant, -v (string, default: None): Variant string in
chr:pos_1_based:ref>alt format.--gene, -g (string, default: None): Target gene symbol (bounds
i= viewport and computes splice junctions).--gene_id (string, default: None): Target Ensembl gene ID (e.g.
ENSG00000092529.26).--interval, -i (string, default: None): Genomic interval
viewport in chr:start-end format.--biosample, -b (string, default: Muscle_Skeletal): Target
biosample or tissue query (e.g. Muscle_Skeletal, K562, Whole_Blood).--modalities, -m (string, default:
SPLICE_JUNCTIONS,RNA_SEQ,DNASE,CHIP_TF): Comma-separated list of
modalities (SPLICE_JUNCTIONS, RNA_SEQ, DNASE, ATAC, CHIP_TF).--tf (string, default: CTCF): Transcription factor name for
ChIP-TF tracks (e.g. CTCF, GATA1).--rename (string, default: None): Custom track rename overrides in
the chart card.--legend_title (string, default: None): Custom legend header for
the chart card.--format (enum, default: table): Output format (table, url,
json).[!IMPORTANT] Mandatory Splicing & RNA-seq Co-Plotting Rule: When generating
/atlas/track-predictionsdeep-links, plotting, or visualizing variant impact data for splicing variants, always plot continuous RNA-seq expression alongside splicing tracks (SPLICE_JUNCTIONS,SPLICE_SITE_USAGE,SPLICE_SITES). Splicing mutations frequently activate cryptic splice junctions and trigger nonsense-mediated decay (NMD) or alter total transcript output; assessing splice junctions (sashimi arcs) together with continuous RNA-seq read coverage is required to observe both the structural splice defect and the resulting change in overall transcript abundance.
[!IMPORTANT] Always Provide Bounded
i=in Track Prediction URLs: Omittingscores=or leaving the genomic interval (i=) unbounded causes the web application to attempt querying all matching tracks across the broader locus, leading to severe latency or page hanging.alphagenome_atlas_links.py track-predictionsautomatically boundsi=to the target gene or requested interval.
© google-deepmind, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 2 other files (scripts) in skills/alphagenome_atlas_website_links of google-deepmind/science-skills.
Open the folder on GitHubat commit 8ab7672
Alphagenome Atlas Website Links next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Alphagenome Atlas Website Links this skillgoogle-deepmind/science-skills | 3.2k | — | ~2.9k | Automated safety check: Pass | Apache-2.0 | |
| 13C Metabolic Flux AnalysisK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~3.2k | Automated safety check: Pass | MIT | |
| Metabolic Study Planneraiming-lab/AutoResearchClaw | 15k | — | ~1.9k | Automated safety check: Pass | MIT | |
| MFA Pipeline Orchestratoraiming-lab/AutoResearchClaw | 15k | — | ~923 | Automated safety check: Pass | MIT | |
| Singlecell Qcxuzhougeng/wisp-science | 1k | — | ~1.6k | Automated safety check: Pass | AGPL-3.0 | |
| Single Cell Rna QcFreedomIntelligence/OpenClaw-Medical-Skills | 3.1k | 2 repos | ~2k | Automated safety check: Pass | Apache-2.0 |
K-Dense-AI/scientific-agent-skills
Estimates reaction fluxes inside cells from steady-state carbon-13 labeling data with a bundled mfapy-based solver, and reports which fluxes the data pin down.
aiming-lab/AutoResearchClaw
Turns a broad metabolic modelling topic into a concrete, paper-shaped plan with organism, model, perturbations, metrics and figures before any FBA code is written.
aiming-lab/AutoResearchClaw
Runs a metabolic flux analysis from model loading to phenotype prediction and figures by handing work to four sub-agents in sequence.
xuzhougeng/wisp-science
A skill your agent uses when designing, reviewing, or implementing single-cell RNA-seq QC in Python or R with a human-in-the-loop, data-driven approach.
FreedomIntelligence/OpenClaw-Medical-Skills
Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations.
guhaohao0991/PaperClaw
Generate a specialized domain-expert research agent modeled on PaperClaw architecture.
google-deepmind/science-skills
Retrieve and analyze AlphaFold predicted structures for a protein.
google-deepmind/science-skills
Analyzes genetic variant effects on gene expression (RNA-seq), chromatin accessibility (DNASE), histone marks (ChIP), and transcription factors using the AlphaGenome API.
google-deepmind/science-skills
Query the ChEMBL database for bioactive molecules, drug targets, bioactivity data, approved drugs, and chemical structures.
google-deepmind/science-skills
Query ClinicalTrials.gov via APIv2. An agent skill from google-deepmind/science-skills.
google-deepmind/science-skills
A skill your agent uses when needing clinical significance, pathogenicity classifications (e.g., Pathogenic, Benign, VUS), clinical evidence rationales, or finding "hard positive" benchmark controls…
google-deepmind/science-skills
A skill your agent uses when you want to look up, map, and search for short genetic variants (SNPs, indels) in NCBI's dbSNP database.
Categories
Constructs deep-links and URLs for the AlphaGenome Atlas website. Alphagenome Atlas Website Links is an agent skill from google-deepmind/science-skills. Constructs deep-links and URLs for the AlphaGenome Atlas website.
Alphagenome Atlas Website Links fits situations like: linking genetic variants and genomic loci on the AlphaGenome Atlas; asked to inspect; link predictions for a genomic variant.
Run `npx skills add google-deepmind/science-skills --skill alphagenome-atlas-website-links -a claude-code`. Or copy the skill folder (skills/alphagenome_atlas_website_links in google-deepmind/science-skills) into .claude/skills/alphagenome-atlas-website-links in your project. Claude Code loads it when a task matches its description.
Run `npx skills add google-deepmind/science-skills --skill alphagenome-atlas-website-links -a codex`. Or copy the skill folder (skills/alphagenome_atlas_website_links in google-deepmind/science-skills) into .agents/skills/alphagenome-atlas-website-links in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add google-deepmind/science-skills --skill alphagenome-atlas-website-links -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/alphagenome-atlas-website-links, .gemini/skills/alphagenome-atlas-website-links, .github/skills/alphagenome-atlas-website-links and .opencode/skills/alphagenome-atlas-website-links in your project.
Going by SKILL.md and its folder, Alphagenome Atlas Website Links needs Python for the scripts in its folder and the command-line tools its instructions call (uv). Our summary lists: Python 3.
SKILL.md names 1 domain. In commands or code: deepmind.google.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Alphagenome Atlas Website Links is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.9k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Alphagenome Atlas Website Links: 13C Metabolic Flux Analysis (K-Dense-AI/scientific-agent-skills, 48k stars), Metabolic Study Planner (aiming-lab/AutoResearchClaw, 15k stars), MFA Pipeline Orchestrator (aiming-lab/AutoResearchClaw, 15k stars) and Singlecell Qc (xuzhougeng/wisp-science, 1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
google-deepmind (a GitHub organization) maintains it in google-deepmind/science-skills, which has 3,233 GitHub stars. The repository holds 40 skills in this directory. The repository was last updated on October 9, 2026.
Source: google-deepmind/science-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.