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string-database

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.

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In the catalog since
1 Oct 2026
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google-deepmind/science-skills
Research & Science
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Skill instructions

Rendered from the published SKILL.md, 58 lines.

STRING Database Skill

This skill allows you to query the STRING database programmatically using a bundled Python CLI wrapper.

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/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.

Core Rules

  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.

Tool Execution

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

Feature Domains (Progressive Disclosure)

Read the following reference files based on the user's request:

  • Mapping Identifiers - Map common protein names to STRING IDs.
  • Interactions & Network - Find interacting proteins, network topologies, mediators, homology, and visual network images.
  • Enrichment & Functional Annotations - Analyze pathway enrichment (GO, KEGG, Pfam), PPI significance, or find all proteins associated with a specific term (e.g. Melanoma).
  • Values/Ranks Enrichment - Submit full 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.