STRING Database Skill

Prerequisites

1. uv

   : Read the

   uv

   skill and follow its Setup instructions to ensure

   uv

   is installed and on PATH.
2. User Notification: If LICENSE_NOTIFICATION.txt does not already exist in this skill 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

scripts/string_cli.py

uv run

uv run scripts/string_cli.py <command> [options] --output /tmp/out.tsv

Feature Domains (Progressive Disclosure)

• 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.