| 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.
|
STRING Database Skill
This skill allows you to query the STRING database programmatically using a
bundled Python CLI wrapper.
Prerequisites
uv: Read the uv skill and follow its Setup instructions to ensure
uv is installed and on PATH.- 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
- 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.
- 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.
- 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.
- 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.
