sionic-maestro-skills

🥈Silver

Maestro Skills automates multi-LLM coding workflows for operations teams. It orchestrates multiple language models to produce accurate, harmonious output. Integrates with Python-based CLI tools and workflows.

3930Updated Today

Intermediate30min to implementautomation

Saves ~120 min per use

Quick InstallView Source

git clone https://github.com/sionic-ai/sionic-maestro-skills.git

Works with:

Claude

Overview

About This Skill

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/sionic-ai/sionic-maestro-skills

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Create a Python script using Maestro Skills to automate a data pipeline for [COMPANY]. The pipeline should ingest [DATA] from [INDUSTRY] sources, clean the data, and generate a summary report. Use at least three different language models to handle different parts of the workflow.

Example Output

# Data Pipeline Automation Report

## Pipeline Overview

The automated data pipeline for [COMPANY] has been successfully implemented using Maestro Skills. The pipeline orchestrates three language models to handle different stages of the workflow:

- **Model 1**: Data Ingestion
- **Model 2**: Data Cleaning
- **Model 3**: Report Generation

## Data Sources

The pipeline ingests data from the following [INDUSTRY] sources:

- API 1: [SOURCE 1]
- API 2: [SOURCE 2]
- Database: [SOURCE 3]

## Data Cleaning Process

The data cleaning model has identified and resolved the following issues:

- Missing values: 15% (resolved by interpolation)
- Outliers: 8% (resolved by capping)
- Inconsistent formats: 5% (resolved by standardization)

## Summary Report

The generated report provides insights into [DATA] trends over the past year. Key findings include:

- A 20% increase in [METRIC 1]
- A 10% decrease in [METRIC 2]
- Seasonal trends observed in [METRIC 3]

## Recommendations

Based on the analysis, the following actions are recommended:

1. Investigate the cause of the decrease in [METRIC 2].
2. Capitalize on the increase in [METRIC 1] by [ACTION].
3. Prepare for the upcoming [SEASONAL TREND] by [ACTION].