synthetic-sciences/openscience is an AI workbench that carries out scientific research by reading papers, forming hypotheses, writing and running code, conducting experiments, analyzing results, and preparing reports. Researchers use it for work in machine learning, biology, physics, and chemistry with remote or local models. Catalogue add-ons extend its scientific workflows through skills and instructions.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/synthetic-sciences/openscience/string-databasenpx skills add synthetic-sciences/openscience --skill string-databasegit clone --depth 1 https://github.com/synthetic-sciences/openscienceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/synthetic-sciences/openscience/string-database)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/string-database"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/string-database.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00044 | $0.04210 |
| Opus 5 | $0.00022 | $0.02105 |
| Sonnet 5 | $0.00009 | $0.00842 |
| Haiku 4.5 | $0.00004 | $0.00421 |
Grade A, and why
string-database scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
3 near-identical copies found in the catalogue:
- string-database — 100% identical, 7 lines differ
- string-database — 98% identical, 3 lines differ
- string-database — 92% identical, 6 lines differ
How it starts
The opening of the file, as written. The whole thing — 534 lines — stays where its author put it; the contents beside it link to each section on GitHub.
STRING Database
Overview
STRING is a comprehensive database of known and predicted protein-protein interactions covering 59M proteins and 20B+ interactions across 5000+ organisms. Query interaction networks, perform functional enrichment, discover partners via REST API for systems biology and pathway analysis.
When to Use This Skill
This skill should be used when:
- Retrieving protein-protein interaction networks for single or multiple proteins
- Performing functional enrichment analysis (GO, KEGG, Pfam) on protein lists
- Discovering interaction partners and expanding protein networks
- Testing if proteins form significantly enriched functional modules
- Generating network visualizations with evidence-based coloring
- Analyzing homology and protein family relationships
- Conducting cross-species protein interaction comparisons
- Identifying hub proteins and network connectivity patterns
Quick Start
The skill provides:
- Python helper functions (
scripts/string_api.py) for all STRING REST API operations - Comprehensive reference documentation (
references/string_reference.md) with detailed API specifications
When users request STRING data, determine which operation is needed and use the appropriate function from scripts/string_api.py.
Core Operations
1. Identifier Mapping (string_map_ids)
Convert gene names, protein names, and external IDs to STRING identifiers.
When to use: Starting any STRING analysis, validating protein names, finding canonical identifiers.
Usage:
from scripts.string_api import string_map_ids
# Map single protein
result = string_map_ids('TP53', species=9606)
# Map multiple proteins
result = string_map_ids(['TP53', 'BRCA1', 'EGFR', 'MDM2'], species=9606)
# Map with multiple matches per query
result = string_map_ids('p53', species=9606, limit=5)
Parameters:
species: NCBI taxon ID (9606 = human, 10090 = mouse, 7227 = fly)limit: Number of matches per identifier (default: 1)echo_query: Include query term in output (default: 1)
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 534 lines · 44 tokens per session scan A da9eb33a5eb5
string-database is a skill published in the GitHub repository synthetic-sciences/openscience (3,493 stars, last pushed today), licensed Apache-2.0. It adds 44 tokens to every session and 4,210 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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