aetherlang-v3-claude-code-integration-skill

# 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 ```bash 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 ```bash 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 ```json { "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 ```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 ```python 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.