string-database

# 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.