Skill 詳細
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.
GitHub で全文を読む (外部ページ)