AetherLang V3 for Claude Code

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name: aetherlang-claude-code description: Execute AetherLang V3 AI workflows from Claude Code using nine specialized engines for culinary, business, research, marketing, and strategic analyses. version: 1.0.3 author: contrario homepage: https://masterswarm.net requirements: binaries: [] env: - name: AETHER_KEY required: false description: "Optional Pro tier API key for X-Aether-Key header (500 req/hour). Get from masterswarm.net." metadata: skill_type: api_connector external_endpoints: - https://api.neurodoc.app/aetherlang/execute operator_note: "api.neurodoc.app operated by NeuroDoc Pro (same as masterswarm.net), Hetzner DE" privacy_policy: https://masterswarm.net license: MIT

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

Replace 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  {
  using target "neuroaether" version ">=0.2";
  input text query;
  node :  ;
  node :  ;
   -> ;
  output text result from ;
}

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

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