Aetherlang Karpathy Skill

AetherLang Karpathy Agent Nodes

> What this skill does: Sends requests to the hosted AetherLang API > (api.neurodoc.app). It does NOT modify local files, execute local code, > or access credentials on your machine. All execution happens server-side.

Execute 10 advanced AI agent node types through the AetherLang Omega API.

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API Endpoint

URL: https://api.neurodoc.app/aetherlang/execute Method: POST Headers: Content-Type: application/json Auth: None required (public API)

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Data Minimization — ALWAYS FOLLOW

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 of any kind
• Do NOT include personally identifiable information unless explicitly requested by user
• Do NOT send contents of local files without explicit user consent

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Request Format

curl -s -X POST https://api.neurodoc.app/aetherlang/execute \
  -H "Content-Type: application/json" \
  -d '{
    "code": "flow FlowName {\n  input text query;\n  node X:  ;\n  query -> X;\n  output text result from X;\n}",
    "query": "user question here"
  }'

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The 10 Node Types

1. plan — Self-Programming

AI breaks task into steps and executes autonomously.

node P: plan steps=3;

2. code_interpreter — Real Math

Sandboxed Python execution on the server. Accurate calculations, no hallucinations.

node C: code_interpreter;

3. critique — Self-Improvement

Evaluates quality (0-10), retries until threshold met.

node R: critique threshold=8 max_retries=3;

4. router — Intelligent Branching

LLM picks optimal path, skips unselected routes (10x speedup).

node R: router;
R -> A | B | C;

5. ensemble — Multi-Agent Synthesis

Multiple AI personas in parallel, synthesizes best insights.

node E: ensemble agents=chef:French_chef|yiayia:Greek_grandmother synthesize=true;

6. memory — Persistent State

Store/recall data across executions (server-side, scoped to namespace).

node M: memory namespace=user_prefs action=store key=diet;
node M: memory namespace=user_prefs action=recall;

7. tool — External API Access

> Security note: The tool node calls public REST URLs you specify. > Only use trusted, public APIs. Never pass credentials or private URLs > as tool parameters. The agent will ask for confirmation before > calling any URL not in the examples below.

node T: tool url=https://api.coingecko.com/api/v3/simple/price?ids=bitcoin&vs_currencies=usd method=GET;

8. loop — Iterative Execution

Repeat node over items. Use | separator.

node L: loop over=Italian|Greek|Japanese target=A max=3;

9. transform — Data Reshaping

Template, extract, format, or LLM-powered reshaping.

node X: transform mode=llm instruction=Summarize_the_data;

10. parallel — Concurrent Execution

Run nodes simultaneously. 3 calls in ~0.2s.

node P: parallel targets=A|B|C;

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Common Pipelines

Live Data → Analysis

flow CryptoAnalysis {
  input text query;
  node T: tool url=https://api.coingecko.com/api/v3/simple/price?ids=bitcoin&vs_currencies=usd method=GET;
  node X: transform mode=llm instruction=Summarize_price;
  node A: llm model=gpt-4o-mini;
  query -> T -> X -> A;
  output text result from A;
}

Multi-Agent + Quality Control

flow QualityEnsemble {
  input text query;
  node E: ensemble agents=analyst:Financial_analyst|strategist:Strategist synthesize=true;
  node R: critique threshold=8;
  query -> E -> R;
  output text result from R;
}

Batch Processing

flow MultiRecipe {
  input text query;
  node L: loop over=Italian|Greek|Japanese target=A max=3;
  node A: llm model=gpt-4o-mini;
  query -> L;
  output text result from L;
}

Parallel API Fetching

flow ParallelFetch {
  input text query;
  node P: parallel targets=A|B|C;
  node A: tool url=https://api.coingecko.com/api/v3/ping method=GET;
  node B: tool url=https://api.coingecko.com/api/v3/simple/price?ids=bitcoin&vs_currencies=usd method=GET;
  node C: tool url=https://api.coingecko.com/api/v3/simple/price?ids=ethereum&vs_currencies=usd method=GET;
  query -> P;
  output text result from P;
}

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Response Parsing

import json
response = json.loads(raw_response)
result = response["result"]["outputs"]["result"]
text = result["response"]
node_type = result["node_type"]
duration = response["result"]["duration_seconds"]

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Parameter Quick Reference

| Node | Key Params | |------|-----------| | plan | steps=3 | | code_interpreter | model=gpt-4o-mini | | critique | threshold=7 max_retries=3 | | router | strategy=single | | ensemble | agents=a:Persona\|b:Persona synthesize=true | | memory | namespace=X action=store\|recall\|search\|clear key=X | | tool | url=https://... method=GET timeout=10 | | loop | over=A\|B\|C target=NodeAlias max=10 mode=collect | | transform | mode=llm\|template\|extract\|format instruction=X | | parallel | targets=A\|B\|C merge=combine |

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AetherLang Karpathy Skill v1.0.1 — API connector for api.neurodoc.app All execution is server-side. No local code runs. No local files modified.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: zrtch
• Source: zrtch/awesome-skills
• License: MIT

Install and usage instructions live in the source repository linked above.