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Get Started Free →Vendor of the google-deepmind/science-skills bundle (37 skills for scientific research). Use when a user asks about any of: AlphaGenome single-variant effect analysis (RNA-seq / DNase / ChIP / TF effects, splicing disruption, UBERON/CL ontology resolution for non-coding variants), AlphaFold DB fetch and analyze, ChEMBL bioactivity queries, ClinicalTrials.gov lookups, ClinVar variant interpretation, dbSNP, EMBL-EBI Ontology Lookup Service (OLS4), ENCODE cCREs, Ensembl REST, Foldseek structural se
.claude/skills/mkurman-gdm-science-bundle/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-05 | ✗→✓ | ▲ Improved | 66% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 82% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 38% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 130% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 163% | 0% |
A vendored copy of the google-deepmind/science-skills bundle (pin: see UPSTREAM_PIN.txt). This directory is the long-tail fallback: for the 5 sub-plugins with first-class zorai support (alphagenome, alphafold, uniprot, clinvar, chembl) prefer the matching plugins/zorai-plugin-science/ sub-plugin for typed settings and structured responses. For every other sub-skill in this bundle, follow the workflow below.
SKILL.md first. Layout:skills/<skill_dir>/SKILL.md — the file's YAML frontmatter is the routing contract; the markdown body is the full workflow with steps, error fixes, and report templates. Never skip this read.
uv run. The deepmind scripts use PEP 723inline # /// script ... # /// blocks, so uv run will resolve and install the right deps in an isolated cache (~/.cache/uv/). Never use bare python3 or pip install.
uv on PATH (zorai runtime has it), a ~/.env file with the relevant API key, and possibly a notification step that records the skill was used.
cat, echo, printenv, or os.environ.get the .envfile or its keys. Deepmind scripts load credentials via dotenv inside the script — they pull keys from disk but do not surface them in the agent's context. Keep it that way.
the plugin settings system (see zorai_plugin_science sub-plugins); for the long-tail skills here, use dotenv's normal on-disk load.
| Sub-skill (kebab-case) | Path under this bundle | Notes | |---|---|---| | alphafold-database-fetch-and-analyze | skills/alphafold_database_fetch_and_analyze/ | Compute, also has a zorai sub-plugin. | | alphagenome-single-variant-analysis | skills/alphagenome_single_variant_analysis/ | Compute + API key, also has a zorai sub-plugin. | | chembl-database | skills/chembl_database/ | REST, also has a zorai sub-plugin. | | clinical-trials-database | skills/clinical_trials_database/ | REST. | | clinvar-database | skills/clinvar_database/ | REST, also has a zorai sub-plugin. | | dbsnp-database | skills/dbsnp_database/ | REST (NCBI EFetch). | | embl-ebi-ols | skills/embl_ebi_ols/ | REST (OLS4). | | encode-ccres-database | skills/encode_ccres_database/ | REST. | | ensembl-database | skills/ensembl_database/ | REST. | | foldseek-structural-search | skills/foldseek_structural_search/ | Compute (Foldseek binary). | | gnomad-database | skills/gnomad_database/ | REST. | | gtex-database | skills/gtex_database/ | REST. | | human-protein-atlas-database | skills/human_protein_atlas_database/ | REST. | | interpro-database | skills/interpro_database/ | REST. | | jaspar-database | skills/jaspar_database/ | REST. | | literature-search-arxiv | skills/literature_search_arxiv/ | REST. | | literature-search-biorxiv | skills/literature_search_biorxiv/ | REST. | | literature-search-europepmc | skills/literature_search_europepmc/ | REST. | | literature-search-openalex | skills/literature_search_openalex/ | REST (key optional). | | ncbi-sequence-fetch | skills/ncbi_sequence_fetch/ | REST (EFetch). | | openfda-database | skills/openfda_database/ | REST. | | opentargets-database | skills/opentargets_database/ | REST (GraphQL). | | pdb-database | skills/pdb_database/ | REST. | | protein-sequence-msa | skills/protein_sequence_msa/ | Compute. | | protein-sequence-similarity-search | skills/protein_sequence_similarity_search/ | Compute. | | pubchem-database | skills/pubchem_database/ | REST. | | pubmed-database | skills/pubmed_database/ | REST. | | pymol | skills/pymol/ | Compute (PyMOL binary). | | quickgo-database | skills/quickgo_database/ | REST. | | reactome-database | skills/reactome_database/ | REST. | | string-database | skills/string_database/ | REST. | | ucsc-conservation-and-tfbs | skills/ucsc_conservation_and_tfbs/ | REST. | | unibind-database | skills/unibind_database/ | REST. | | uniprot-database | skills/uniprot_database/ | REST, also has a zorai sub-plugin. | | uv | skills/uv/ | Internal. Sets up uv. zorai runtime already provides it. | | scienceskillscommon | skills/scienceskillscommon/ | Internal. Shared helpers for other skills. Do not invoke directly. | | workflow-skill-creator | skills/workflow_skill_creator/ | Meta-skill. Skip; the agent does not need to author new skills mid-task. |
--project $SKILL_DIR for ad-hoc). The skill's own SKILL.md will tell you which.skills/.(AlphaGenome API calls, OpenAlex bulk pulls, Foldseek server queries, etc.).
SKILL.md says to record a LICENSE_NOTIFICATION.txt inthe skill directory, skip the file write when running through zorai (the bundle is read-only inside the repo); instead, mention the upstream license URL to the user once per session.
LICENSE).International (CC-BY-4.0).
SKILL_LICENSES.md for the full list. You are responsible for ensuring that any data retrieved through these skills is used in compliance with the upstream provider's terms.
UPSTREAM_PIN.txt for the exact commit hash this bundle wasvendored at. To refresh, re-vendor at a newer commit and update the pin.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-22 | pass→pass | 8,385 | 6,093 | -27% | 1 | 1 | 0% | 1,572 | 2,812 | +79% | 0 | 0 | — |
case-01 | fail→fail | 8,185 | 4,957 | -39% | 1 | 1 | 0% | 1,414 | 2,226 | +57% | 0 | 0 | — |
case-02 | fail→fail | 13,566 | 8,118 | -40% | 1 | 1 | 0% | 2,343 | 2,723 | +16% | 0 | 0 | — |
case-03 | fail→fail | 15,731 | 5,430 | -65% | 1 | 1 | 0% | 3,027 | 2,134 | -30% | 0 | 0 | — |
case-04 | pass→pass | 6,873 | 16,609 | +142% | 1 | 1 | 0% | 1,126 | 2,883 | +156% | 0 | 0 | — |
case-05 | fail→pass | 8,563 | 3,948 | -54% | 1 | 1 | 0% | 1,556 | 2,584 | +66% | 0 | 0 | — |
case-06 | fail→pass | 7,930 | 2,638 | -67% | 1 | 1 | 0% | 1,242 | 2,258 | +82% | 0 | 0 | — |
case-07 | fail→pass | 12,512 | 5,534 | -56% | 1 | 1 | 0% | 2,072 | 2,865 | +38% | 0 | 0 | — |
case-08 | pass→pass | 4,951 | 2,200 | -56% | 1 | 1 | 0% | 559 | 2,186 | +291% | 0 | 0 | — |
case-09 | pass→pass | 6,798 | 3,752 | -45% | 1 | 1 | 0% | 1,181 | 2,479 | +110% | 0 | 0 | — |
case-10 | fail→pass | 6,667 | 4,142 | -38% | 1 | 1 | 0% | 1,122 | 2,577 | +130% | 0 | 0 | — |
case-11 | fail→pass | 4,786 | 1,682 | -65% | 1 | 1 | 0% | 809 | 2,124 | +163% | 0 | 0 | — |
case-12 | fail→pass | 6,676 | 2,893 | -57% | 1 | 1 | 0% | 1,090 | 2,307 | +112% | 0 | 0 | — |
case-13 | fail→pass | 6,080 | 2,092 | -66% | 1 | 1 | 0% | 1,088 | 2,138 | +97% | 0 | 0 | — |
case-14 | fail→pass | 7,506 | 1,861 | -75% | 1 | 1 | 0% | 1,168 | 2,176 | +86% | 0 | 0 | — |
case-15 | fail→pass | 7,270 | 1,533 | -79% | 1 | 1 | 0% | 1,177 | 2,097 | +78% | 0 | 0 | — |
case-16 | fail→pass | 6,845 | 2,169 | -68% | 1 | 1 | 0% | 984 | 2,208 | +124% | 0 | 0 | — |
case-17 | fail→pass | 8,572 | 2,266 | -74% | 1 | 1 | 0% | 1,439 | 2,196 | +53% | 0 | 0 | — |
case-18 | fail→pass | 7,952 | 1,952 | -75% | 1 | 1 | 0% | 1,282 | 2,192 | +71% | 0 | 0 | — |
case-19 | fail→pass | 8,294 | 1,983 | -76% | 1 | 1 | 0% | 1,254 | 2,124 | +69% | 0 | 0 | — |
case-20 | pass→pass | 15,115 | 12,179 | -19% | 1 | 1 | 0% | 2,741 | 4,291 | +57% | 0 | 0 | — |
case-21 | pass→pass | 10,673 | 7,578 | -29% | 1 | 1 | 0% | 2,055 | 3,301 | +61% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 19 counted toward the lift figure. The other 3 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +59 percentage points is the difference between those two pass rates over the 19 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.