Skill-Details
string-database
Useful for biological network analysis.
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SKILL.md
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---
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
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:
* **[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 to
discover the correct CLI command.
Vollständige Quelle auf GitHub lesen (öffnet externe Seite)