mirobody

🥈Silver

MiroBody is an open-source AI-native data engine for personal data. It enables operations teams to automate data processing and analysis. It connects to Python workflows and supports Claude agents. Use it to streamline data handling and improve operational efficiency.

1,0341060Updated Today

Intermediate30min to implementautomation

Saves ~15 min per use

Quick InstallView Source

git clone https://github.com/thetahealth/mirobody.git

Works with:

Claude

Overview

About This Skill

https://mirobody.ai

How to Use

1. **Setup:** Install MiroBody using `pip install mirobody` and ensure your dataset is accessible in Python (CSV, SQL, or API). Configure your Python environment with required libraries (pandas, numpy). 2. **Data Loading:** Import your dataset into a pandas DataFrame. For large datasets, use MiroBody's streaming capabilities with `mirobody.stream_data()`. 3. **Processing:** Chain MiroBody operations in your script. Start with data cleaning functions (`clean_data()`, `handle_missing_values()`), then apply analysis methods (`generate_stats()`, `detect_trends()`). 4. **Automation:** Use the generated Python script as a template for scheduled jobs. Set up cron jobs (Linux/macOS) or Task Scheduler (Windows) to run updates. For cloud environments, deploy as a Lambda function or Airflow DAG. 5. **Integration:** Connect MiroBody to your existing workflows. Use the output statistics to update dashboards (Tableau, Power BI), trigger alerts (Slack, email), or feed into other AI agents for deeper analysis. Tips: - Start with a small test dataset to validate your processing pipeline - Use MiroBody's visualization tools to spot anomalies before full analysis - Document your data transformations for reproducibility

Use Cases

Develop custom health analysis tools that integrate with personal health data from various devices.

Create conversational agents that provide personalized responses based on user-specific data.

Automate data analysis processes using Python scripts that can be easily integrated into the Mirobody framework.

Build finance or legal assistants that utilize personal data to offer tailored advice and insights.

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/thetahealth/mirobody

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

Use MiroBody to process and analyze the [DATASET_NAME] dataset stored in [DATA_LOCATION]. Perform the following tasks: 1) Clean the data by handling missing values, duplicates, and outliers using [METHODS]. 2) Generate summary statistics and key insights for [METRICS]. 3) Create a Python script to automate future updates to this dataset. 4) Document the findings in a structured report with recommendations for [USE_CASE].

Example Output

### MiroBody Data Processing Report: Customer Support Tickets (Q2 2024)

**Dataset Overview:**
- Source: Zendesk export (CSV) from /data/support_tickets/q2_2024.csv
- Records processed: 12,487 tickets
- Timeframe: April 1 - June 30, 2024

**Data Cleaning Results:**
- Missing values resolved: 892 (7.2%) - primarily in 'assigned_agent' field
- Duplicates detected: 247 (1.98%) - merged using ticket_id + timestamp
- Outliers identified: 189 high-priority tickets with resolution time > 7 days (flagged for review)

**Key Insights:**
1. **Response Time Trends:** Average first response time improved from 4.2h (April) to 3.1h (June) after implementing new triage workflows.
2. **Agent Performance:** Top 3 agents (Agent_7, Agent_12, Agent_3) handled 38% of all tickets with 94% satisfaction scores.
3. **Issue Categories:** 42% of tickets were billing-related (highest volume), followed by 28% technical issues and 15% account management.

**Automation Script Generated:**
```python
import pandas as pd
from mirobody import clean_data, generate_stats

def process_tickets(filepath):
    df = pd.read_csv(filepath)
    cleaned = clean_data(df, 
                        missing_strategy='median',
                        duplicate_key='ticket_id',
                        outlier_threshold=7)
    stats = generate_stats(cleaned, 
                          metrics=['response_time', 'satisfaction_score'],
                          group_by='agent')
    return cleaned, stats
```

**Recommendations:**
- Implement automated tagging for billing tickets to route to specialized team
- Create agent performance dashboards using the generated statistics
- Schedule monthly data quality checks using the provided script

**Next Steps:**
1. Deploy the cleaning pipeline to run nightly
2. Set up alerts for tickets exceeding 7-day resolution time
3. Share insights with support leadership by EOD Friday