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new-project

Automate setup of an Olakai-enabled AI agent project with KPI configuration.

11|2|Updated Mar 21, 2026
Search Tags:#workflow#analytics#sdk#agent#kpis#olakai#customdata
One-click install
npx skills add https://github.com/nrl-ai/chub --skill new-project-nrl-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: new-project
Source: https://github.com/nrl-ai/chub/tree/main/content/olakai/skills/new-project
Command: npx skills add https://github.com/nrl-ai/chub --skill new-project-nrl-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill guides teams to build a complete Olakai-integrated AI agent project with KPI tracking, governance, and analytics from scratch.

Core Features & Use Cases

  • Scaffold an end-to-end Olakai-enabled agent project, including prerequisites, workflow and agent setup, and KPI configuration.
  • Configure CustomData fields and KPI formulas to enable production-grade analytics and governance monitoring.
  • Validate the integration with an end-to-end workflow that demonstrates usage of the Olakai SDK across TypeScript and Python examples.

Quick Start

Create a new Olakai project and initialize the SDK-integrated agent with KPI tracking.

Frequently Asked Questions about new-project

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I bootstrap an AI agent project with KPI tracking and analytics?▼

To bootstrap an AI agent project with KPI tracking and analytics, you can use an automated scaffolding process that configures the SDK, custom data fields, and KPI formulas. This ensures your agent has production-grade governance monitoring from the start.

What is the best way to set up production-ready governance for an AI agent?▼

Setting up production-ready governance for an AI agent requires aligning custom data fields and KPI formulas within your workflow. Automating this setup ensures proper SDK integration and continuous analytics monitoring across your agent's operations.

Can I configure custom KPI formulas for my AI agent in both TypeScript and Python?▼

Yes, you can configure custom KPI formulas for your AI agent in both TypeScript and Python. The integration validates the workflow end-to-end, demonstrating SDK usage and KPI tracking across both language examples.

Do I need to manually install the SDK before initializing an analytics-enabled AI agent?▼

You do not need to manually manage SDK installation separately when initializing an analytics-enabled AI agent. The project scaffolding automates SDK installation and aligns it with your custom data and KPI configuration.

How does an end-to-end workflow validate AI agent KPI integration?▼

An end-to-end workflow validates AI agent KPI integration by demonstrating actual SDK usage across TypeScript and Python examples. This process ensures your custom data fields and KPI formulas are correctly aligned for production analytics.

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