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1stANUNNAKI1stANUNNAKI/majlis/skills/string-database/SKILL.md

string-database

Skill2 starsChanged 3 months agoMITView on GitHub

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.

11 other projects use a version of this, 4 of them edited.

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Used with

Projects with it also use ena-database, gwas-database and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
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  • +8 more from garri333

The file

We split this skill into the rules it gives an agent

6 rules. 0 are common, 2 are distinctive, and 4 are very specific to this project.

  • 0

    Common

    Most of the 11 edited versions we read keep these.

  • 2

    Distinctive

    This author's own choices. Worth a look.

  • 4

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    uv: Read the uv skill and follow its Setup instructions to ensure uv is installed and on PATH.

    line 22
  2. Very specific:

    User Notification: If LICENSE_NOTIFICATION.txt does not already exist in this skill directory then (1) prominently notify the user to check …

    line 24
  3. Very specific:

    Never read large outputs into context.

    line 38
  4. Very specific:

    Map Identifiers first: If you only have common gene names (e.g., 'TP53'), map them to STRING IDs first as this guarantees much faster …

    line 40
  5. Distinctive:

    Notification: If this skill is used, ensure this is mentioned in the output.

    line 43
  6. Distinctive:

    Read the following reference files based on the user's request:

    line 56

“Common” is measured: kept by at least 6 in 10 of the 11 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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 LICENSE_NOTIFICATION.txt does not already exist in this skill 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.

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