# String Database

Query the STRING database for protein-protein interactions (PPIs), functional...

- Repository: https://github.com/google-deepmind/science-skills (3377 stars)
- Category: Data and ML
- License: Apache-2.0
- Last updated: 2026-10-23
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add google-deepmind/science-skills --skill string-database -g -a claude-code -y`
Codex: `npx skills add google-deepmind/science-skills --skill string-database -g -a codex -y`
Cursor: `npx skills add google-deepmind/science-skills --skill string-database -g -a cursor -y`
Gemini CLI: `npx skills add google-deepmind/science-skills --skill string-database -g -a gemini-cli -y`

## 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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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`:

```bash
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:

The rest: https://github.com/google-deepmind/science-skills/blob/HEAD/skills/string_database/SKILL.md