OpenAI LLM Skill

Invoke OpenAI models for text generation, reasoning, code analysis, and complex tasks using the Python openai SDK.

Available Models

Model ID	Description	Best For
`gpt-4o`	Flagship multimodal model	General tasks, vision, analysis
`gpt-4o-mini`	Fast and cost-efficient	Quick tasks, high throughput
`o1`	Advanced reasoning model	Complex reasoning, math, code
`o1-mini`	Fast reasoning	Moderate reasoning tasks
`o3-mini`	Newest reasoning model	Deep reasoning, planning

Configuration

API Key Location: C:\Users\USERNAME\env (OPENAI_API_KEY)

Default API Key: Use environment variable OPENAI_API_KEY

Usage

Basic Text Generation

bash

python -c "
from openai import OpenAI
client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
response = client.chat.completions.create(
    model='gpt-4o',
    messages=[{'role': 'user', 'content': 'YOUR_PROMPT_HERE'}]
)
print(response.choices[0].message.content)
"

With System Instructions

bash

python -c "
from openai import OpenAI
client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
response = client.chat.completions.create(
    model='gpt-4o',
    messages=[
        {'role': 'system', 'content': 'You are a helpful coding assistant.'},
        {'role': 'user', 'content': 'YOUR_PROMPT_HERE'}
    ],
    temperature=0.7,
    max_tokens=4096
)
print(response.choices[0].message.content)
"

Streaming Response

bash

python -c "
from openai import OpenAI
client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
stream = client.chat.completions.create(
    model='gpt-4o',
    messages=[{'role': 'user', 'content': 'YOUR_PROMPT_HERE'}],
    stream=True
)
for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end='', flush=True)
print()
"

Using Reasoning Models (o1, o3-mini)

bash

python -c "
from openai import OpenAI
client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
response = client.chat.completions.create(
    model='o1',
    messages=[{'role': 'user', 'content': 'YOUR_COMPLEX_REASONING_PROMPT'}]
)
print(response.choices[0].message.content)
"

Workflow

When this skill is invoked:

•
Parse the user request to determine:
- •The prompt/task to send to OpenAI
- •Which model to use (default: gpt-4o)
- •Any configuration options (temperature, max tokens, system message)
•
Select the appropriate model:
- •General tasks/analysis → gpt-4o
- •Quick responses → gpt-4o-mini
- •Complex reasoning/math → o1 or o3-mini
- •Moderate reasoning → o1-mini

•

Execute the Python command using Bash tool:

bash

python -c "
from openai import OpenAI
client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
response = client.chat.completions.create(
    model='MODEL_ID',
    messages=[{'role': 'user', 'content': '''PROMPT'''}]
)
print(response.choices[0].message.content)
"

•
Return the response to the user

Example Invocations

Code Review

bash

python -c "
from openai import OpenAI
client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
response = client.chat.completions.create(
    model='gpt-4o',
    messages=[{'role': 'user', 'content': '''Review this Python code for bugs and improvements:

def calculate_total(items):
    total = 0
    for item in items:
        total += item.price * item.quantity
    return total
'''}]
)
print(response.choices[0].message.content)
"

Complex Reasoning (with o1)

bash

python -c "
from openai import OpenAI
client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
response = client.chat.completions.create(
    model='o1',
    messages=[{'role': 'user', 'content': 'Solve this step by step: A farmer has 17 sheep. All but 9 die. How many are left?'}]
)
print(response.choices[0].message.content)
"

Generate Code

bash

python -c "
from openai import OpenAI
client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
response = client.chat.completions.create(
    model='gpt-4o',
    messages=[
        {'role': 'system', 'content': 'You are an expert Python developer. Write clean, efficient, well-documented code.'},
        {'role': 'user', 'content': 'Write a Python function to merge two sorted lists'}
    ],
    temperature=0.3
)
print(response.choices[0].message.content)
"

Multi-turn Conversations

For conversations with history:

bash

python -c "
from openai import OpenAI
client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
response = client.chat.completions.create(
    model='gpt-4o',
    messages=[
        {'role': 'user', 'content': 'What is Python?'},
        {'role': 'assistant', 'content': 'Python is a high-level programming language...'},
        {'role': 'user', 'content': 'How do I install it?'}
    ]
)
print(response.choices[0].message.content)
"

Model Notes

Reasoning Models (o1, o3-mini)

•Do NOT support system messages - use user messages only
•Do NOT support temperature parameter
•May take longer to respond (they "think" internally)
•Best for math, logic, complex code problems

GPT-4o Models

•Support system messages and all parameters
•Fast responses
•Good for general tasks, vision, multimodal

Error Handling

The skill handles common errors:

•Rate Limiting: Wait and retry with exponential backoff
•Token Limits: Truncate input or use streaming for large outputs
•Invalid Model: Fall back to gpt-4o

Tools to Use

•Bash: Execute Python commands
•Read: Load files to include in prompts
•Write: Save OpenAI responses to files