prompt-engineering-openai-integration
Sub-skill of prompt-engineering: OpenAI Integration (+1).
Best use case
prompt-engineering-openai-integration is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of prompt-engineering: OpenAI Integration (+1).
Teams using prompt-engineering-openai-integration should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/openai-integration/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How prompt-engineering-openai-integration Compares
| Feature / Agent | prompt-engineering-openai-integration | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Sub-skill of prompt-engineering: OpenAI Integration (+1).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# OpenAI Integration (+1)
## OpenAI Integration
```python
import openai
def create_openai_caller(model: str = "gpt-4", temperature: float = 0.7):
"""Create OpenAI API caller."""
client = openai.OpenAI()
def call(prompt: str, system: str = None) -> str:
messages = []
if system:
*See sub-skills for full details.*
## Anthropic Integration
```python
import anthropic
def create_anthropic_caller(model: str = "Codex-3-opus-20240229"):
"""Create Anthropic API caller."""
client = anthropic.Anthropic()
def call(prompt: str, system: str = None) -> str:
response = client.messages.create(
model=model,
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