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/leonchaox/qinyan-academic-skills/string-databasenpx skills add LeonChaoX/qinyan-academic-skills --skill string-databasegit clone --depth 1 https://github.com/LeonChaoX/qinyan-academic-skillsWrote 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/leonchaox/qinyan-academic-skills/string-database)<a href="https://agentmods.dev/skills/leonchaox/qinyan-academic-skills/string-database"><img src="https://agentmods.dev/badge/skills/leonchaox/qinyan-academic-skills/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 | $0.04208 |
| Opus 5 | $0.00022 | $0.02104 |
| 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 3d 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.
This is a copy
98% identical to string-database — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 533 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.
- 3d ago First seen · 533 lines · 44 tokens per session scan A ff76c8271d25
string-database is a skill published in the GitHub repository LeonChaoX/qinyan-academic-skills (871 stars, last pushed 1mo ago), licensed MIT. It adds 44 tokens to every session and 4,208 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to string-database, differing in 3 lines, and is treated as a copy.
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