alphafold_database_fet…
Retrieve and analyze AlphaFold predicted structures for a protein. Use when the user provides…
Query the STRING database for protein-protein interactions (PPIs), functional enrichment, and homology. Use when the user asks about interactions between specific proteins, interaction evidence, confidence scores, protein interaction partners, or pathway enrichments.
$ npx -y skills add google-deepmind/science-skills --skill string_database --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/string_databaseContext preview
The summary Claude sees to decide when to auto-load this skill.
Query the STRING database for protein-protein interactions (PPIs), functional enrichment, and homology. Use when the user asks about interactions between specific proteins, interaction evidence, confidence scores, protein interaction partners, or pathway enrichments.
name: string-database description: > Query the STRING database for protein-protein interactions (PPIs), functional enrichment, and homology. Use when the user asks about interactions between specific proteins, interaction evidence, confidence scores, protein interaction partners, or pathway enrichments.
This skill allows you to query the STRING database programmatically using a bundled Python CLI wrapper.
1. **`uv`**: Read the `uv` skill and follow its Setup instructions to ensure `uv` is installed and on PATH. 2. **User Notification**: If .licenses/string_database_LICENSE.txt does not already exist in the workspace root directory then (1) prominently notify the user to check the terms at https://string-db.org/cgi/access, then (2) create the file recording the notification text and timestamp.
1. **MANDATORY: Ask for Species First:** The STRING API requires NCBI Taxon IDs. **You MUST NOT guess or assume a species.** If the user does not explicitly state a species or Taxon ID, you MUST stop and ask: "Which species are you interested in? I need the NCBI Taxon ID to proceed." Even for well-known proteins like TP53, BRCA1, or MDM2 that are commonly associated with human studies, you MUST still ask — do not default to Human. 2. **Never print output to stdout:** The `--output <file.tsv>` is required. Never read large outputs into context. Instead use jq, python or file operations (`grep`, `head`) to process large output. 3. **Map Identifiers first:** If you only have common gene names (e.g., 'TP53'), map them to STRING IDs first as this guarantees much faster server responses. Use the `map` command for this. 4. **Notification**: If this skill is used, ensure this is mentioned in the output.
The CLI is at `scripts/string_cli.py` and should be run using `uv run`:
uv run scripts/string_cli.py <command> [options] --output /tmp/out.tsv
Read the following reference files based on the user's request:
to STRING IDs.
proteins, network topologies, mediators, homology, and visual network images.
Analyze pathway enrichment (GO, KEGG, Pfam), PPI significance, or find all proteins associated with a specific term (e.g. Melanoma).
experimental datasets (e.g., logFC, p-values) for rank-based enrichment analysis using the async background API.
To begin, read the reference file most appropriate to the current task to discover the correct CLI command.
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