本文へ移動
cccskills
無料GitHub で公開

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.

インストール方法を見る

含まれるファイル(7)

  • SKILL.md2.9 KB
  • references/citation.bib567 B
  • references/enrichment.md1.6 KB
  • references/interactions.md2.0 KB
  • references/mapping.md735 B
  • references/valuesranks.md1.6 KB
  • scripts/string_cli.py11.8 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

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:

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.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Retrieve and analyze AlphaFold predicted structures for a protein. Use when the user provides a specific UniProt Accession ID and wants structural confidence metrics (pLDDT), domain boundary analysis, or disorder assessment. Do not use if the user only has a protein name, gene name, or amino acid sequence — ask for a UniProt ID first.

日本語の概要は準備中です。原文の説明を表示しています。

google-deepmind/science-skills3,2382026年10月10日 更新

Constructs deep-links and URLs for the AlphaGenome Atlas website. Supports generating single-variant exploration links (1-based chr:pos:ref>alt), genomic locus views (1-based closed chr:start-end), candidate summary tables, and AlphaGenome reference vs. alternate predictions. Use whenever visualizing, exploring, charting, or linking genetic variants and genomic loci on the AlphaGenome Atlas, or when asked to inspect, view, or link predictions for a genomic variant.

日本語の概要は準備中です。原文の説明を表示しています。

google-deepmind/science-skills3,2382026年10月10日 更新

Analyzes genetic variant effects on gene expression (RNA-seq), chromatin accessibility (DNASE), histone marks (ChIP), and transcription factors using the AlphaGenome API. Use when the user asks about non-coding variant effects, pathogenicity, clinical significance, disease associations, functional effects, gene expression changes, splicing disruption, or regulatory effects in promoters and enhancers. Also use for resolving biological terms to tissue/cell-type ontologies (UBERON/CL) or analyzing variants in chr:pos:ref>alt format.

日本語の概要は準備中です。原文の説明を表示しています。

google-deepmind/science-skills3,2382026年10月10日 更新

Score, annotate, and analyze the functional impact of genetic variants using AlphaGenome Variant Impact (AVI) scores. Query variants in chr:pos:ref>alt format, annotate VCF/tabular callsets, perform saturation mutagenesis window scans (1-based closed chr:start-end), and extract GENCODE v46 GTF gene/exon/junction coordinates all via the AlphaGenome Atlas API.

日本語の概要は準備中です。原文の説明を表示しています。

google-deepmind/science-skills3,2382026年10月10日 更新

Query the ChEMBL database for bioactive molecules, drug targets, bioactivity data, approved drugs, and chemical structures. Use when the user asks about compounds, targets, IC50/Ki values, drug mechanisms, or structure searches.

日本語の概要は準備中です。原文の説明を表示しています。

google-deepmind/science-skills3,2382026年10月10日 更新

Query ClinicalTrials.gov via APIv2. Use when you want to search for trials by condition, drug, location, status, or phase; retrieve trial details by NCT ID; check eligibility/inclusion criteria; count trials across conditions or time periods; identify a sponsor's trial portfolio; find recruiting trials for patient matching.

日本語の概要は準備中です。原文の説明を表示しています。

google-deepmind/science-skills3,2382026年10月10日 更新

google-deepmind のスキルをすべて見る

このスキルの問題を報告する