Download the ClusterCollie AI Tools
The ClusterCollie AI Nutanix Forecasting Tool is available now as a local Streamlit application. The AI Playbook (Ansible) and Security & Compliance tools are still in active development — their project pages have full details, and downloads will appear here as soon as each one reaches beta.
v0.9.0-beta · Updated 2026
ClusterCollie AI Nutanix Forecasting Tool — Beta Build
Packaged as a source distribution. Requires Python 3.10+ and a Nutanix Prism Central API connection (or sample data mode for evaluation).
Download from GitHubBeta software — see the Terms of Use before installing.
Planned · In Development
ClusterCollie AI Playbook (Ansible)
Describe an infrastructure task in plain English and get back a validated, syntax-checked Nutanix Ansible playbook, grounded in the official nutanix.ncp collection.
No build available yet — see the project page for status and progress updates.
Planned · In Development
ClusterCollie AI Security & Compliance Tool
A read-only Nutanix cluster compliance scanner that translates raw audit findings into governance-friendly language mapped to CIS Benchmarks and NIST 800-53 control families.
View ProjectNo build available yet — see the project page for status and progress updates.
System Requirements — Nutanix Forecasting Tool
| Component | Requirement |
|---|---|
| Python | 3.10 or later |
| OS | Windows, macOS, or Linux |
| Browser | Chrome, Edge, or Firefox (current version) |
| Nutanix Access | Prism Central API credentials (read-only recommended) or Nutanix MCP server endpoint |
| Optional | Sample/demo dataset included for evaluation without live cluster access |
Installation Steps — Nutanix Forecasting Tool
Get the source from the GitHub repository or unzip the distributed package into a local project folder.
python -m venv venv then activate it with source venv/bin/activate (macOS/Linux) or venv\Scripts\activate (Windows).pip install -r requirements.txtCopy
.env.example to .env and enter your Prism Central host, API credentials, or MCP server endpoint. Leave defaults in place to run in sample-data demo mode.streamlit run app.py — the dashboard opens automatically at http://localhost:8501.Select a cluster, choose a forecast horizon (30/60/90 days), and review projected compute, storage, and network headroom.