How do I install the String Database skill?

Run npx skillmds@latest add fridrichmethod/string-database in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is String Database safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with String Database?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is String Database free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published String Database?

FridrichMethod (@fridrichmethod) published this skill. Their other Agent Skills are listed on their SkillMD profile.

String Database by fridrichmethod — Agent Skill

Name: String Database
Author: FridrichMethod

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.

FridrichMethod 942f651 7 files · 21.2 KB Updated Sep 17, 2026

File contents

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 .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

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.

---name: string-databasedescription: 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 SkillThis skill allows you to query the STRING database programmatically using abundled Python CLI wrapper.## Prerequisites1. **`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 Rules1. **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 ExecutionThe CLI is at `scripts/string_cli.py` and should be run using `uv run`:```bashuv 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](references/mapping.md)** - Map common protein names to STRING IDs.* **[Interactions & Network](references/interactions.md)** - Find interacting proteins, network topologies, mediators, homology, and visual network images.* **[Enrichment & Functional Annotations](references/enrichment.md)** - Analyze pathway enrichment (GO, KEGG, Pfam), PPI significance, or find all proteins associated with a specific term (e.g. Melanoma).* **[Values/Ranks Enrichment](references/valuesranks.md)** - 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 todiscover the correct CLI command.

Frequently asked questions