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/kegg-databasenpx skills add synthetic-sciences/openscience --skill kegg-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/kegg-database)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/kegg-database"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/kegg-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 | $0.00060 | $0.03172 |
| Opus 5 | $0.00030 | $0.01586 |
| Sonnet 5 | $0.00012 | $0.00634 |
| Haiku 4.5 | $0.00006 | $0.00317 |
Grade A, and why
kegg-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
8 near-identical copies found in the catalogue:
- kegg-database — 100% identical, 3 lines differ
- kegg-database — 100% identical, 3 lines differ
- kegg-database — 100% identical, 3 lines differ
- kegg-database — 98% identical, 7 lines differ
- kegg-database — 91% identical, 6 lines differ
- kegg-database — 91% identical, 6 lines differ
- kegg-database — 91% identical, 6 lines differ
- alterlab-kegg — 91% identical, 36 lines differ
How it starts
The opening of the file, as written. The whole thing — 377 lines — stays where its author put it; the contents beside it link to each section on GitHub.
KEGG Database
Overview
KEGG (Kyoto Encyclopedia of Genes and Genomes) is a comprehensive bioinformatics resource for biological pathway analysis and molecular interaction networks.
Important: KEGG API is made available only for academic use by academic users.
When to Use This Skill
This skill should be used when querying pathways, genes, compounds, enzymes, diseases, and drugs across multiple organisms using KEGG's REST API.
Quick Start
The skill provides:
- Python helper functions (
scripts/kegg_api.py) for all KEGG REST API operations - Comprehensive reference documentation (
references/kegg_reference.md) with detailed API specifications
When users request KEGG data, determine which operation is needed and use the appropriate function from scripts/kegg_api.py.
Core Operations
1. Database Information (kegg_info)
Retrieve metadata and statistics about KEGG databases.
When to use: Understanding database structure, checking available data, getting release information.
Usage:
from scripts.kegg_api import kegg_info
# Get pathway database info
info = kegg_info('pathway')
# Get organism-specific info
hsa_info = kegg_info('hsa') # Human genome
Common databases: kegg, pathway, module, brite, genes, genome, compound, glycan, reaction, enzyme, disease, drug
2. Listing Entries (kegg_list)
List entry identifiers and names from KEGG databases.
When to use: Getting all pathways for an organism, listing genes, retrieving compound catalogs.
Usage:
from scripts.kegg_api import kegg_list
# List all reference pathways
pathways = kegg_list('pathway')
# List human-specific pathways
hsa_pathways = kegg_list('pathway', 'hsa')
# List specific genes (max 10)
genes = kegg_list('hsa:10458+hsa:10459')
Common organism codes: hsa (human), mmu (mouse), dme (fruit fly), sce (yeast), eco (E. coli)
3. Searching (kegg_find)
Search KEGG databases by keywords or molecular properties.
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 · 377 lines · 60 tokens per session scan A b87fa5f1a412
kegg-database is a skill published in the GitHub repository synthetic-sciences/openscience (3,473 stars, last pushed today), licensed Apache-2.0. It adds 60 tokens to every session and 3,172 once invoked, about $0.0003 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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