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Query Reactome

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BioTender-max
query-reactome

Query Reactome for biological pathways and reactions. Use when user asks about signaling cascades, biological processes, pathway diagrams, or reaction details. Triggers on "reactome", "signaling cascade", "biological pathway", "pathway diagram", "reaction mechanism".

Overview

PublisherBioTender-max
Repositoryawesome-bio-agent-skills
Skill namequery-reactome
Stars
204
Forks
36
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by BioTender-max on GitHub. Read the source before you install it.

Installation

Install the Query Reactome AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/BioTender-max/awesome-bio-agent-skills/tree/main/skills/bioclaw/query-reactome
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/BioTender-max/awesome-bio-agent-skills.git /tmp/awesome-bio-agent-skills
mkdir -p .claude/skills
cp -r /tmp/awesome-bio-agent-skills/skills/bioclaw/query-reactome .claude/skills/query-reactome
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Query Reactome in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Query Reactome on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Query Reactome is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Reactome Pathway Database

Query the Reactome ContentService and AnalysisService APIs.

When to Use

  • User asks about detailed biological pathways
  • User wants pathway diagrams
  • User asks about specific reactions in a pathway
  • User wants to do pathway enrichment with a gene list

How to Execute

python
import requests
import json

CONTENT_URL = "https://reactome.org/ContentService"
ANALYSIS_URL = "https://reactome.org/AnalysisService"

# 1. Search pathways by keyword
def search_pathways(keyword, species="Homo sapiens"):
    url = f"{CONTENT_URL}/search/query"
    params = {"query": keyword, "species": species, "types": "Pathway", "cluster": True}
    r = requests.get(url, params=params)
    r.raise_for_status()
    return r.json()

# 2. Get pathway details
def get_pathway(pathway_id):
    url = f"{CONTENT_URL}/data/query/{pathway_id}"
    r = requests.get(url, headers={"Accept": "application/json"})
    r.raise_for_status()
    return r.json()

# 3. Get genes/proteins in a pathway
def get_pathway_participants(pathway_id):
    url = f"{CONTENT_URL}/data/participants/{pathway_id}"
    r = requests.get(url, headers={"Accept": "application/json"})
    r.raise_for_status()
    return r.json()

# 4. Gene list pathway enrichment
def pathway_enrichment(gene_list):
    url = f"{ANALYSIS_URL}/identifiers/projection"
    genes_text = "\n".join(gene_list)
    headers = {"Content-Type": "text/plain"}
    r = requests.post(url, data=genes_text, headers=headers)
    r.raise_for_status()
    return r.json()

# 5. Look up a gene in Reactome
def query_gene(gene_symbol):
    url = f"{CONTENT_URL}/data/query/{gene_symbol}"
    r = requests.get(url, headers={"Accept": "application/json"})
    r.raise_for_status()
    return r.json()

# Example: DNA repair pathways
results = search_pathways("DNA repair")
entries = results.get("results", [])
for entry in entries[:5]:
    for e in entry.get("entries", []):
        print(f"{e.get('stId', 'N/A')}: {e.get('name', 'N/A')}")

# Pathway enrichment
enrichment = pathway_enrichment(["BRCA1", "BRCA2", "TP53", "ATM", "CHEK2"])
for p in enrichment.get("pathways", [])[:5]:
    name = p.get("name", "N/A")
    pval = p.get("entities", {}).get("pValue", "N/A")
    found = p.get("entities", {}).get("found", 0)
    print(f"{name} — p={pval:.2e}, {found} genes found")

Common Pathway IDs

  • DNA Repair: R-HSA-73894
  • Apoptosis: R-HSA-109581
  • Cell Cycle: R-HSA-1640170
  • Immune System: R-HSA-168256

Follow-up Suggestions

  • "Want me to do pathway enrichment with your gene list?"
  • "Should I compare with KEGG pathways?"
  • "Want me to find upstream regulators of this pathway?"

Frequently asked questions

What does the Query Reactome AI skill do?

Query Reactome for biological pathways and reactions. Use when user asks about signaling cascades, biological processes, pathway diagrams, or reaction details. Triggers on "reactome", "signaling cascade", "biological pathway", "pathway diagram", "reaction mechanism".

Why use Query Reactome on TypingMind?

Because you install it once and use it with any model. Query Reactome is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Query Reactome in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/BioTender-max/awesome-bio-agent-skills/tree/main/skills/bioclaw/query-reactome. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Query Reactome?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Query Reactome?

As many as you like. As long as a model supports skills, you can use Query Reactome with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Query Reactome AI skill free?

It is published on GitHub by BioTender-max. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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