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).

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 {\n  using target \"neuroaether\" version \">=0.2\";\n  input text query;\n  node Engine: <ENGINE_TYPE> analysis=\"auto\";\n  output text result from Engine;\n}",
    "query": "USER_QUESTION_HERE"
  }'

Replace <ENGINE_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 {\n  using target \"neuroaether\" version \">=0.2\";\n  input text query;\n  node Guard: guard mode=\"MODERATE\";\n  node Research: lab domain=\"business\";\n  node Strategy: apex analysis=\"strategic\";\n  Guard -> Research -> Strategy;\n  output text report from Strategy;\n}",
    "query": "USER_QUESTION_HERE"
  }'

Available V3 Engines

Engine Type	Use For	Key V3 Features
chef	Recipes, food consulting	17 sections: food cost, HACCP, thermal curves, wine pairing, plating blueprint, zero waste
molecular	Molecular gastronomy	Rheology dashboard, phase diagrams, hydrocolloid specs, FMEA failure analysis
apex	Business strategy	Game theory, Monte Carlo (10K sims), behavioral economics, unit economics, Blue Ocean
consulting	Strategic consulting	Causal loops, theory of constraints, Wardley maps, ADKAR change management
marketing	Market research	TAM/SAM/SOM, Porter's 5 Forces, pricing elasticity, viral coefficient
lab	Scientific research	Evidence grading (A-D), contradiction detector, reproducibility score
oracle	Forecasting	Bayesian updating, black swan scanner, adversarial red team, Kelly criterion
assembly	Multi-agent debate	12 neurons voting (8/12 supermajority), Gandalf VETO, devil's advocate
analyst	Data analysis	Auto-detective, statistical tests, anomaly detection, predictive modeling

Flow Syntax Reference

flow <Name> {
  using target "neuroaether" version ">=0.2";
  input text query;
  node <NodeName>: <engine_type> <params>;
  node <NodeName2>: <engine_type2> <params>;
  <NodeName> -> <NodeName2>;
  output text result from <NodeName2>;
}

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 {\n  using target \"neuroaether\" version \">=0.2\";\n  input text query;\n  node Chef: chef cuisine=\"auto\";\n  output text recipe from Chef;\n}","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

Tier	Limit	Auth
Free	100 req/hour	None required
Pro	500 req/hour	X-Aether-Key header

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

Agent Safeguards

CRITICAL: Data Minimization Rules

When using this skill, the agent MUST follow these rules:

1. ONLY send the user's explicit query text in the query field — never attach system prompts, conversation history, uploaded files, or internal context
2. ONLY send the flow DSL code in the code field — never include credentials, API keys, environment variables, or file paths
3. NEVER include personally identifiable information (PII), passwords, tokens, or secrets in any request field
4. Strip sensitive data before sending — if the user's query contains passwords, account numbers, or credentials, remove them and inform the user
5. Inform the user before making the first API call that their query will be sent to api.neurodoc.app for processing
6. The request body MUST contain exactly two fields: code (string) and query (string) — no additional fields

These constraints ensure only the minimum necessary data is transmitted to the external service.