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 skills add beita6969/ScienceClaw --skill kegg-databasegit clone --depth 1 https://github.com/beita6969/ScienceClawWrote 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/beita6969/scienceclaw/kegg-database)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/kegg-database"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/kegg-database/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/beita6969/scienceclaw/kegg-database"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/kegg-database.svg" alt="Reviewed on agentmods" width="80" 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 | $0.03170 |
| Opus 5 | $0.00030 | $0.01585 |
| 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 5d 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
100% identical to kegg-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 — 376 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.
- 5d ago First seen · 376 lines · 60 tokens per session scan A 718436319c23
kegg-database is a skill published in the GitHub repository beita6969/ScienceClaw (896 stars, last pushed 3mo ago), licensed MIT. It adds 60 tokens to every session and 3,170 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to kegg-database, differing in 3 lines, and is treated as a copy.
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