DSPy Skills Collection automates LLM programming and prompt optimization for operations teams. It enables systematic RAG pipeline development, connecting to Python-based workflows and Claude agents.
git clone https://github.com/OmidZamani/dspy-skills.gitDSPy Skills Collection automates LLM programming and prompt optimization for operations teams. It enables systematic RAG pipeline development, connecting to Python-based workflows and Claude agents.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/OmidZamani/dspy-skillsCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Act as a DSPy Skills Collection expert. I need to automate a [PROCESS] for [COMPANY] in the [INDUSTRY] sector. The process involves [DATA] and requires [SPECIFIC_TASKS]. Generate a DSPy script to optimize this workflow and connect it to our existing Python-based systems. Include error handling and logging capabilities.
# DSPy Script for [COMPANY] Workflow Automation
```python
from dspy import DSPy
# Initialize DSPy with company-specific parameters
company_dspy = DSPy(
company_name="[COMPANY]",
industry="[INDUSTRY]",
data_type="[DATA]",
api_key="your_api_key_here"
)
# Define the workflow automation function
def automate_workflow(process_data):
"""
Automates the [PROCESS] for [COMPANY]
Args:
process_data (dict): Dictionary containing the data to be processed
Returns:
dict: Processed data with additional insights
"""
try:
# Connect to existing Python workflows
python_workflow = connect_to_python_workflow(process_data)
# Optimize the workflow using DSPy
optimized_workflow = company_dspy.optimize_workflow(python_workflow)
# Log the process
log_process("Workflow optimization completed", optimized_workflow)
return optimized_workflow
except Exception as e:
log_process("Error in workflow automation", str(e))
raise
# Example usage
example_data = {
"input_data": ["sample1", "sample2", "sample3"]
}
result = automate_workflow(example_data)
print(result)
```
## Key Features
- **Automated Workflow**: Connects seamlessly with existing Python-based systems
- **Error Handling**: Includes robust error handling and logging
- **Optimization**: Uses DSPy to optimize the workflow for better performance
- **Industry-Specific**: Tailored for the [INDUSTRY] sector and [COMPANY]Unlock data insights with interactive dashboards and collaborative analytics capabilities.
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