rdfitted/openai-llm

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)

API Key: Set via $OPENAI_API_KEY environment variable

Usage

Basic Text Generation

python -c "
import os
from openai import OpenAI
client = OpenAI(api_key=os.environ['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

python -c "
import os
from openai import OpenAI
client = OpenAI(api_key=os.environ['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

python -c "
import os
from openai import OpenAI
client = OpenAI(api_key=os.environ['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)

python -c "
import os
from openai import OpenAI
client = OpenAI(api_key=os.environ['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:

1. 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)

2. 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

3. Execute the Python command using Bash tool:

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

4. Return the response to the user

Example Invocations

Code Review

python -c "
import os
from openai import OpenAI
client = OpenAI(api_key=os.environ['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)

python -c "
import os
from openai import OpenAI
client = OpenAI(api_key=os.environ['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

python -c "
import os
from openai import OpenAI
client = OpenAI(api_key=os.environ['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:

python -c "
import os
from openai import OpenAI
client = OpenAI(api_key=os.environ['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