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Query Opentarget

Community
BioTender-max
query-opentarget

Query OpenTargets for drug targets, disease associations, and therapeutic evidence. Use when user asks about drug targets, disease mechanisms, target validation, or drug-disease associations. Triggers on "opentarget", "drug target", "target validation", "disease association", "therapeutic target", "drug for disease".

Overview

PublisherBioTender-max
Repositoryawesome-bio-agent-skills
Skill namequery-opentarget
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 Opentarget 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-opentarget
  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-opentarget .claude/skills/query-opentarget
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Query Opentarget 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 Opentarget 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 Opentarget 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.

OpenTargets Platform Query

Query the OpenTargets Platform GraphQL API for drug-target-disease associations.

When to Use

  • User asks about drug targets for a disease
  • User wants disease-gene associations
  • User asks about drugs targeting a specific gene
  • User wants evidence for target validation

How to Execute

python
import requests
import json

OPENTARGETS_URL = "https://api.platform.opentargets.org/api/v4/graphql"

def query_opentargets(graphql_query, variables=None):
    payload = {"query": graphql_query, "variables": variables or {}}
    r = requests.post(OPENTARGETS_URL, json=payload, headers={"Content-Type": "application/json"})
    r.raise_for_status()
    return r.json()

# 1. Search for a target (gene)
def search_target(gene_name):
    query = '''
    query searchTarget($name: String!) {
      search(queryString: $name, entityNames: ["target"], page: {index: 0, size: 5}) {
        hits { id name description entity }
      }
    }'''
    return query_opentargets(query, {"name": gene_name})

# 2. Get diseases associated with a target
def target_diseases(ensembl_id, size=10):
    query = '''
    query targetDiseases($ensemblId: String!, $size: Int!) {
      target(ensemblId: $ensemblId) {
        id approvedSymbol approvedName
        associatedDiseases(page: {index: 0, size: $size}) {
          count
          rows { disease { id name } score
            datasourceScores { componentId score } }
        }
      }
    }'''
    return query_opentargets(query, {"ensemblId": ensembl_id, "size": size})

# 3. Get targets for a disease
def disease_targets(disease_id, size=10):
    query = '''
    query diseaseTargets($diseaseId: String!, $size: Int!) {
      disease(efoId: $diseaseId) {
        id name
        associatedTargets(page: {index: 0, size: $size}) {
          count
          rows { target { id approvedSymbol approvedName } score }
        }
      }
    }'''
    return query_opentargets(query, {"diseaseId": disease_id, "size": size})

# 4. Get drugs for a target
def target_drugs(ensembl_id):
    query = '''
    query targetDrugs($ensemblId: String!) {
      target(ensemblId: $ensemblId) {
        id approvedSymbol
        knownDrugs(size: 10) {
          count
          rows { drug { id name } mechanismOfAction phase status
            disease { id name } }
        }
      }
    }'''
    return query_opentargets(query, {"ensemblId": ensembl_id})

# 5. Search diseases
def search_disease(disease_name):
    query = '''
    query searchDisease($name: String!) {
      search(queryString: $name, entityNames: ["disease"], page: {index: 0, size: 5}) {
        hits { id name description entity }
      }
    }'''
    return query_opentargets(query, {"name": disease_name})

# Example: Find top drug targets for Alzheimer's
result = search_disease("Alzheimer")
hits = result.get("data", {}).get("search", {}).get("hits", [])
if hits:
    disease_id = hits[0]["id"]
    targets = disease_targets(disease_id, size=5)
    disease = targets.get("data", {}).get("disease", {})
    print(f"Disease: {disease.get('name')} ({disease.get('id')})")
    for row in disease.get("associatedTargets", {}).get("rows", []):
        t = row["target"]
        print(f"  {t['approvedSymbol']} ({t['approvedName']}) — score: {row['score']:.3f}")

Common Disease IDs (EFO)

  • Alzheimer's: EFO_0000249
  • Breast cancer: EFO_0000305
  • Type 2 diabetes: EFO_0001360
  • Parkinson's: EFO_0002508

Follow-up Suggestions

  • "Want me to check what drugs are in clinical trials for this target?"
  • "Should I look at the evidence breakdown by data source?"
  • "Want me to find the top genetic associations?"

Frequently asked questions

What does the Query Opentarget AI skill do?

Query OpenTargets for drug targets, disease associations, and therapeutic evidence. Use when user asks about drug targets, disease mechanisms, target validation, or drug-disease associations. Triggers on "opentarget", "drug target", "target validation", "disease association", "therapeutic target", "drug for disease".

Why use Query Opentarget on TypingMind?

Because you install it once and use it with any model. Query Opentarget 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 Opentarget 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-opentarget. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Query Opentarget?

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 Opentarget?

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

Is the Query Opentarget 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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