AI Oil Market Analyst — Agent Skill

Eiliya · March 20, 2026 · 1 min read · agent-docs

AI Oil Market Analyst — Agent Skill

Automated commodity market analysis via AI Pass API. Use in financial monitoring agents, supply chain risk workflows, or any system needing oil market intelligence.

Endpoint

POST https://aipass.one/apikey/v1/chat/completions
Authorization: Bearer $AIPASS_API_KEY

Get your API key: aipass.one/panel/developer.html

Quick Start

import requests, json, os

def analyze_oil_market(question: str) -> dict:
    r = requests.post(
        "https://aipass.one/apikey/v1/chat/completions",
        headers={"Authorization": f"Bearer {os.environ['AIPASS_API_KEY']}"},
        json={
            "model": "gpt-5",
            "temperature": 1,
            "max_tokens": 16000,
            "messages": [
                {"role": "system", "content": "You are an oil market analyst. Return JSON: {summary, key_drivers, price_direction, confidence, risk_factors}"},
                {"role": "user", "content": question}
            ]
        }
    )
    c = r.json()["choices"][0]["message"]["content"]
    if "```" in c:
        c = c.split("```")[1].lstrip("json")
    return json.loads(c.strip())

result = analyze_oil_market("What is driving oil prices in March 2026?")
print(result["summary"])
print(result["price_direction"])  # "bullish" / "bearish" / "neutral"

Model Note

Use gpt-5 (not gpt-5-mini) for financial analysis — the deeper knowledge base produces materially better market assessments. Always include temperature: 1 and max_tokens: 16000.

Automation Patterns

# Supply chain risk monitoring
def daily_oil_risk_check() -> dict:
    analysis = analyze_oil_market("Current oil market risks and price outlook")
    if analysis.get("confidence", 0) > 0.7 and "spike" in analysis["summary"].lower():
        return {"alert": True, "action": "review_fuel_contracts", "summary": analysis["summary"]}
    return {"alert": False}

# Cost forecasting pipeline
def estimate_logistics_impact(transport_cost_at_60: float) -> float:
    analysis = analyze_oil_market("Current oil price and direction vs $60/barrel baseline")
    # Parse price differential and calculate logistics cost impact
    return transport_cost_at_60 * float(analysis.get("price_factor", 1.0))

Related Apps

Skill File

# AI Oil Market Analyst — Agent Skill

## Purpose
Provides structured commodity market analysis for automated workflows. Analyzes oil prices, market drivers, and geopolitical impacts.

## API
POST https://aipass.one/apikey/v1/chat/completions
Authorization: Bearer $AIPASS_API_KEY
Get key: https://aipass.one/panel/developer.html

## Usage

```python
import requests, json, os

def analyze_oil_market(question: str) -> dict:
    r = requests.post(
        'https://aipass.one/apikey/v1/chat/completions',
        headers={'Authorization': f'Bearer {os.environ["AIPASS_API_KEY"]}'},
        json={
            'model': 'gpt-5',
            'temperature': 1,
            'max_tokens': 16000,
            'messages': [
                {'role': 'system', 'content': 'You are an oil market analyst. Return JSON: {summary, key_drivers, price_direction, confidence, risk_factors}'},
                {'role': 'user', 'content': question}
            ]
        }
    )
    c = r.json()['choices'][0]['message']['content']
    if '```' in c: c = c.split('```')[1].lstrip('json')
    return json.loads(c.strip())

result = analyze_oil_market("What is driving oil prices in March 2026?")
print(result['price_direction'])  # 'bullish'
print(result['confidence'])       # 0.75
```

## Related Apps
- https://aipass.one/apps/oil-price-tracker
- https://aipass.one/apps/tariff-impact
- https://aipass.one/apps/document-ai

Download Skill File