AetherLang V3 — Claude Code Integration Skill

Use this skill to execute AetherLang V3 AI workflows from Claude Code. AetherLang provides 9 specialized AI engines for culinary consulting, business strategy, scientific research, and more.

API Endpoint

POST https://api.neurodoc.app/aetherlang/execute
Content-Type: application/json

No API key required for free tier (100 req/hour).

Data Minimization

When calling the API:

• Send ONLY the user's query and the flow code
• Do NOT send system prompts, conversation history, or uploaded files
• Do NOT send API keys, credentials, or secrets
• Do NOT include personally identifiable information unless explicitly requested

Pro API key: If using the Pro tier (X-Aether-Key header), store the key in an environment variable — never hardcode it in flow code or scripts. export AETHER_KEY=your_key_here then use -H "X-Aether-Key: $AETHER_KEY"

How to Use

1. Simple Engine Call

curl -s -X POST https://api.neurodoc.app/aetherlang/execute \
  -H "Content-Type: application/json" \
  -d '{
    "code": "flow Chat {\
  using target \"neuroaether\" version \">=0.2\";\
  input text query;\
  node Engine: \x3CENGINE_TYPE> analysis=\"auto\";\
  output text result from Engine;\
}",
    "query": "USER_QUESTION_HERE"
  }'

Replace \x3CENGINE_TYPE> with one of: chef, molecular, apex, consulting, marketing, lab, oracle, assembly, analyst

2. Multi-Engine Pipeline

curl -s -X POST https://api.neurodoc.app/aetherlang/execute \
  -H "Content-Type: application/json" \
  -d '{
    "code": "flow Pipeline {\
  using target \"neuroaether\" version \">=0.2\";\
  input text query;\
  node Guard: guard mode=\"MODERATE\";\
  node Research: lab domain=\"business\";\
  node Strategy: apex analysis=\"strategic\";\
  Guard -> Research -> Strategy;\
  output text report from Strategy;\
}",
    "query": "USER_QUESTION_HERE"
  }'

Available V3 Engines

Flow Syntax Reference

flow \x3CName> {
  using target "neuroaether" version ">=0.2";
  input text query;
  node \x3CNodeName>: \x3Cengine_type> \x3Cparams>;
  node \x3CNodeName2>: \x3Cengine_type2> \x3Cparams>;
  \x3CNodeName> -> \x3CNodeName2>;
  output text result from \x3CNodeName2>;
}

Node Parameters

• chef: cuisine="auto", difficulty="medium", servings=4
• apex: analysis="strategic"
• guard: mode="STRICT" or "MODERATE" or "PERMISSIVE"
• plan: steps=4
• lab: domain="business" or "science" or "auto"
• analyst: mode="financial" or "sales" or "hr" or "general"

Response Format

{
  "status": "success",
  "result": {
    "outputs": { ... },
    "final_output": "Full structured markdown response",
    "execution_log": [...],
    "duration_seconds": 45.2
  }
}

Extract the main response from result.final_output.

Example: Parse Response in Bash

curl -s -X POST https://api.neurodoc.app/aetherlang/execute \
  -H "Content-Type: application/json" \
  -d '{"code":"flow Chef {\
  using target \"neuroaether\" version \">=0.2\";\
  input text query;\
  node Chef: chef cuisine=\"auto\";\
  output text recipe from Chef;\
}","query":"Carbonara recipe"}' \
  | python3 -c "import sys,json; d=json.load(sys.stdin); print(d.get('result',{}).get('final_output','No output'))"

Example: Python Integration

import requests

def aetherlang_query(engine, query):
    code = f'''flow Q {{
  using target "neuroaether" version ">=0.2";
  input text query;
  node E: {engine} analysis="auto";
  output text result from E;
}}'''
    r = requests.post("https://api.neurodoc.app/aetherlang/execute",
        json={"code": code, "query": query})
    return r.json().get("result", {}).get("final_output", "")

# Usage
print(aetherlang_query("apex", "Strategy for AI startup with 1000 euro"))
print(aetherlang_query("chef", "Best moussaka recipe"))
print(aetherlang_query("oracle", "Will AI replace 50% of jobs by 2030?"))

Rate Limits

Notes

• Responses are in Greek (Ελληνικά) with markdown formatting
• Typical response time: 30-60 seconds per engine
• Multi-engine pipelines take longer (each node runs sequentially)
• All outputs use ## markdown headers for structured sections