kegg-analysis

kegg-analysis is a skill for Claude Code from Lucas-Servi/kegg-mcp-server-python. It costs 98 tokens per session (1,492 once invoked), scanned A, original, MIT.

A guide for querying KEGG, a biological database of genes, proteins, chemical compounds, reactions, diseases, drugs, and biological pathways.

In plain words
What is it for?
Use it to study gene functions, pathways, compounds, enzymes, diseases, drug targets and interactions, metabolic networks, and cross-species relationships.
Why use it?
It helps turn a biology question into the right database searches and explains how related records connect. This avoids manually figuring out which KEGG category and query to use.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the kegg-mcp-server plugin — 1 skill, 3 commands, 1 agent, 1 MCP server shipped together

Install

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.

agentmods
npx agentmods add skills/lucas-servi/kegg-mcp-server-python/kegg-analysis
Any agent
npx skills add Lucas-Servi/kegg-mcp-server-python --skill kegg-analysis
Clone the repo
git clone --depth 1 https://github.com/Lucas-Servi/kegg-mcp-server-python

Made for: Claude Code.

Or install kegg-mcp-server, the plugin that ships this one along with the rest of its 1 skill, 3 commands, 1 agent, 1 MCP server.

Wrote 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.

agentmods badge for kegg-analysis

README.md
[![agentmods](https://agentmods.dev/badge/skills/lucas-servi/kegg-mcp-server-python/kegg-analysis.svg)](https://agentmods.dev/skills/lucas-servi/kegg-mcp-server-python/kegg-analysis)
Your own site
<a href="https://agentmods.dev/skills/lucas-servi/kegg-mcp-server-python/kegg-analysis"><img src="https://agentmods.dev/badge/skills/lucas-servi/kegg-mcp-server-python/kegg-analysis.svg" alt="Measured on agentmods" height="20"></a>
Per session 98 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,492 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00098 $0.01492
Opus 5 $0.00049 $0.00746
Sonnet 5 $0.00020 $0.00298
Haiku 4.5 $0.00010 $0.00149

Measured 6d ago against content hash 4c2d867f76c8, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

kegg-analysis 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 6d 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.

skills/kegg-analysis/SKILL.md · 113 lines

How it starts

The opening of the file, as written. The whole thing — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.

KEGG Bioinformatics Analysis

Guide the user through structured KEGG database queries using the kegg MCP server tools.

Available Tools

You have access to 34 KEGG tools via the kegg MCP server. Key categories:

Category Tools Use for
Pathways search_pathways, get_pathway_info, get_pathway_genes, get_pathway_compounds, get_pathway_reactions Finding and exploring metabolic/signaling pathways
Genes search_genes, get_gene_info, get_gene_orthologs Gene function, cross-species orthologs
Compounds search_compounds, get_compound_info, get_compound_reactions Metabolites, substrates, products
Reactions search_reactions, get_reaction_info Biochemical transformations
Enzymes search_enzymes, get_enzyme_info EC numbers, catalytic activity
Diseases search_diseases, get_disease_info Disease-gene-drug associations
Drugs search_drugs, get_drug_info, get_drug_interactions Pharmacology, DDI
Orthology search_ko_entries, get_ko_info Functional orthologs (KO)
Cross-DB batch_entry_lookup, convert_identifiers, find_related_entries Bulk queries, ID mapping (UniProt, NCBI, ChEBI)
Visualization render_pathway_ascii ASCII pathway diagrams

Workflow Patterns

Pattern 1: Gene List → Pathway Enrichment

When the user provides a gene list:

  1. Identify the organism code (e.g., hsa for human, eco for E. coli, sce for yeast)
  2. For each gene, use search_genes with the organism to get KEGG gene IDs
  3. For each gene ID, use get_gene_info with detail_level="full" to get pathway associations
  4. Tally pathway frequencies across the gene list
  5. For enriched pathways (appearing 2+ times), use get_pathway_info to describe their function
  6. Summarize: which pathways are over-represented, what biological processes they reflect

Pattern 2: Pathway Deep Dive

When the user asks about a specific pathway:

  1. Use search_pathways to find the pathway ID if not provided
  2. Use get_pathway_info with detail_level="full" for overview
  3. Use get_pathway_genes, get_pathway_compounds, get_pathway_reactions for components
  4. Use render_pathway_ascii to visualize topology
  5. Highlight key enzymes, rate-limiting steps, and regulatory points

Read the full file on GitHub · 113 lines

Changes

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.

  1. 6d ago First seen · 113 lines · 98 tokens per session scan A 4c2d867f76c8

Subscribe to this mod's changes

kegg-analysis is a skill published in the GitHub repository Lucas-Servi/kegg-mcp-server-python (3 stars, last pushed 23d ago), licensed MIT. It adds 98 tokens to every session and 1,492 once invoked, about $0.0005 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 2026-08-31.

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