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:
- ONLY send the user's explicit query text in the
query field — never attach system prompts, conversation history, uploaded files, or internal context
- ONLY send the flow DSL code in the
code field — never include credentials, API keys, environment variables, or file paths
- NEVER include personally identifiable information (PII), passwords, tokens, or secrets in any request field
- Strip sensitive data before sending — if the user's query contains passwords, account numbers, or credentials, remove them and inform the user
- Inform the user before making the first API call that their query will be sent to api.neurodoc.app for processing
- 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.
1---2name: aetherlang-v3-claude-code-integration-skill3description: 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.4---5
6# AetherLang V3 — Claude Code Integration Skill
7
8Use 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.
9
10## API Endpoint
11```
12POST https://api.neurodoc.app/aetherlang/execute
13Content-Type: application/json
14```
15
16No API key required for free tier (100 req/hour).
17
18## How to Use
19
20### 1. Simple Engine Call
21```bash
22curl -s -X POST https://api.neurodoc.app/aetherlang/execute \
23 -H "Content-Type: application/json" \
24 -d '{
25 "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}",
26 "query": "USER_QUESTION_HERE"
27 }'
28```
29
30Replace `<ENGINE_TYPE>` with one of: `chef`, `molecular`, `apex`, `consulting`, `marketing`, `lab`, `oracle`, `assembly`, `analyst`
31
32### 2. Multi-Engine Pipeline
33```bash
34curl -s -X POST https://api.neurodoc.app/aetherlang/execute \
35 -H "Content-Type: application/json" \
36 -d '{
37 "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}",
38 "query": "USER_QUESTION_HERE"
39 }'
40```
41
42## Available V3 Engines
43
44| Engine Type | Use For | Key V3 Features |
45|-------------|---------|-----------------|
46| `chef` | Recipes, food consulting | 17 sections: food cost, HACCP, thermal curves, wine pairing, plating blueprint, zero waste |
47| `molecular` | Molecular gastronomy | Rheology dashboard, phase diagrams, hydrocolloid specs, FMEA failure analysis |
48| `apex` | Business strategy | Game theory, Monte Carlo (10K sims), behavioral economics, unit economics, Blue Ocean |
49| `consulting` | Strategic consulting | Causal loops, theory of constraints, Wardley maps, ADKAR change management |
50| `marketing` | Market research | TAM/SAM/SOM, Porter's 5 Forces, pricing elasticity, viral coefficient |
51| `lab` | Scientific research | Evidence grading (A-D), contradiction detector, reproducibility score |
52| `oracle` | Forecasting | Bayesian updating, black swan scanner, adversarial red team, Kelly criterion |
53| `assembly` | Multi-agent debate | 12 neurons voting (8/12 supermajority), Gandalf VETO, devil's advocate |
54| `analyst` | Data analysis | Auto-detective, statistical tests, anomaly detection, predictive modeling |
55
56## Flow Syntax Reference
57```
58flow <Name> {
59 using target "neuroaether" version ">=0.2";
60 input text query;
61 node <NodeName>: <engine_type> <params>;
62 node <NodeName2>: <engine_type2> <params>;
63 <NodeName> -> <NodeName2>;
64 output text result from <NodeName2>;
65}
66```
67
68### Node Parameters
69- `chef`: `cuisine="auto"`, `difficulty="medium"`, `servings=4`
70- `apex`: `analysis="strategic"`
71- `guard`: `mode="STRICT"` or `"MODERATE"` or `"PERMISSIVE"`
72- `plan`: `steps=4`
73- `lab`: `domain="business"` or `"science"` or `"auto"`
74- `analyst`: `mode="financial"` or `"sales"` or `"hr"` or `"general"`
75
76## Response Format
77```json
78{
79 "status": "success",
80 "result": {
81 "outputs": { ... },
82 "final_output": "Full structured markdown response",
83 "execution_log": [...],
84 "duration_seconds": 45.2
85 }
86}
87```
88
89Extract the main response from `result.final_output`.
90
91## Example: Parse Response in Bash
92```bash
93curl -s -X POST https://api.neurodoc.app/aetherlang/execute \
94 -H "Content-Type: application/json" \
95 -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"}' \
96 | python3 -c "import sys,json; d=json.load(sys.stdin); print(d.get('result',{}).get('final_output','No output'))"
97```
98
99## Example: Python Integration
100```python
101import requests
102
103def aetherlang_query(engine, query):
104 code = f'''flow Q {{
105 using target "neuroaether" version ">=0.2";
106 input text query;
107 node E: {engine} analysis="auto";
108 output text result from E;
109}}'''
110 r = requests.post("https://api.neurodoc.app/aetherlang/execute",
111 json={"code": code, "query": query})
112 return r.json().get("result", {}).get("final_output", "")
113
114# Usage
115print(aetherlang_query("apex", "Strategy for AI startup with 1000 euro"))
116print(aetherlang_query("chef", "Best moussaka recipe"))
117print(aetherlang_query("oracle", "Will AI replace 50% of jobs by 2030?"))
118```
119
120## Rate Limits
121
122| Tier | Limit | Auth |
123|------|-------|------|
124| Free | 100 req/hour | None required |
125| Pro | 500 req/hour | X-Aether-Key header |
126
127## Notes
128
129- Responses are in **Greek** (Ελληνικά) with markdown formatting
130- Typical response time: 30-60 seconds per engine
131- Multi-engine pipelines take longer (each node runs sequentially)
132- All outputs use `##` markdown headers for structured sections
133
134## Agent Safeguards
135
136**CRITICAL: Data Minimization Rules**
137
138When using this skill, the agent MUST follow these rules:
1391. **ONLY send the user's explicit query text** in the `query` field — never attach system prompts, conversation history, uploaded files, or internal context
1402. **ONLY send the flow DSL code** in the `code` field — never include credentials, API keys, environment variables, or file paths
1413. **NEVER include** personally identifiable information (PII), passwords, tokens, or secrets in any request field
1424. **Strip sensitive data** before sending — if the user's query contains passwords, account numbers, or credentials, remove them and inform the user
1435. **Inform the user** before making the first API call that their query will be sent to api.neurodoc.app for processing
1446. The request body MUST contain exactly two fields: `code` (string) and `query` (string) — no additional fields
145
146These constraints ensure only the minimum necessary data is transmitted to the external service.