reactome-database

A connector for Reactome, a curated database of biological pathways—sets of reactions and processes in living systems. It supports pathway analysis, identifier mapping, reaction details, hierarchy browsing, diagram export, and related searches.

In plain words
What is it for?
Use it to analyse gene or protein lists, find enriched pathways, map identifiers, inspect reaction inputs and outputs, browse pathway relationships, and export pathway diagrams.
Why use it?
It avoids switching between separate Reactome services for gene-list analysis and pathway research. It can also show which identifiers were not found or could not be mapped.

Skill for Claude CodeCodex

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/google-deepmind/science-skills/reactome_database
Any agent
npx skills add google-deepmind/science-skills --skill reactome_database
Clone the repo
git clone --depth 1 https://github.com/google-deepmind/science-skills

Made for: Claude Code, Codex.

Per session 73 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,801 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 $0.00073 $0.02801
Opus 5 $0.00036 $0.01401
Sonnet 5 $0.00015 $0.00560
Haiku 4.5 $0.00007 $0.00280

Measured 3d ago against content hash bccfb05ac68a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

reactome-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.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/reactome_analysis.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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/reactome_database/SKILL.md · 282 lines

How it starts

The opening of the file, as written. The whole thing — 282 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Reactome Analysis & Content Service

Prerequisites

  1. uv: Read the uv skill and follow its Setup instructions to ensure uv is installed and on PATH.
  2. User Notification: If .licenses/reactome_database_LICENSE.txt does not already exist in the workspace root directory then (1) prominently notify the user to check the terms at https://reactome.org/license and https://reactome.org/cite, then (2) create the file recording the notification text and timestamp.

Overview

Reactome is a free, open-source, curated pathway database. This skill wraps both the Analysis Service (https://reactome.org/AnalysisService/) and the Content Service (https://reactome.org/ContentService/) providing pathway enrichment analysis, identifier mapping, reaction details, pathway hierarchy navigation, diagram export, cross-reference mapping, and search.

When to Use This Skill

  • Performing pathway enrichment (overrepresentation) analysis on gene/protein lists
  • Retrieving analysis results using a token from previous enrichment
  • Identifying which genes or proteins were not found in a pathway analysis
  • Analyzing gene expression data against pathway annotations
  • Mapping identifiers to Reactome entities across species
  • Retrieving reaction participants (inputs, outputs, catalysts, regulators)
  • Navigating pathway hierarchy and listing top-level pathways
  • Finding which complexes or sets contain a protein
  • Exporting pathway/reaction diagrams (PNG/SVG) with gene highlighting
  • Cross-referencing identifiers across databases (UniProt, Ensembl, etc.)
  • Searching the Reactome knowledgebase
  • Downloading analysis reports (PDF, CSV, JSON)
  • Comparing pathways across species

Common Species IDs

Reference list for common research organisms:

  • Homo sapiens
    • ID: 9606
  • Mus musculus (Mouse)
    • ID: 48892
  • Rattus norvegicus (Rat)
    • ID: 48895

Common Pathway IDs

Reference list for commonly used Reactome pathway stable IDs:

Read the full file on GitHub · 282 lines

Files

What ships with it

3 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.

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. 3d ago First seen · 282 lines · 73 tokens per session scan A bccfb05ac68a

Subscribe to this mod's changes

reactome-database is a skill published in the GitHub repository google-deepmind/science-skills (2,814 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 73 tokens to every session and 2,801 once invoked, about $0.0004 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 2026-08-30.

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