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andrewyng
andrewyngCOMMUNITY
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new-project

Creates new AI agent projects with Olakai monitoring, SDK integration, and custom KPIs.

13.9k|1.2k|Updated Oct 30, 2025
Search Tags:#monitoring#project setup#ai agent#kpi#olakai#sdk integration
One-click install
npx skills add https://github.com/andrewyng/context-hub --skill new-project-andrewyng
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: new-project
Source: https://github.com/andrewyng/context-hub/tree/main/content/olakai/skills/new-project
Command: npx skills add https://github.com/andrewyng/context-hub --skill new-project-andrewyng

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires @olakai/sdk, openai, olakai-sdk, and includes references (resource) components.

What problem does it solve?

This Skill streamlines the creation of new AI agents by integrating Olakai's monitoring, KPI tracking, and governance features from the ground up.

Core Features & Use Cases

  • Project Setup: Guides through initial agent project configuration.
  • SDK Integration: Provides clear instructions for integrating the Olakai SDK in TypeScript and Python.
  • KPI Configuration: Details how to define and implement custom business-specific Key Performance Indicators (KPIs).
  • End-to-End Validation: Includes steps for testing and validating the integration and data flow.
  • Use Case: A team needs to launch a new AI-powered customer support chatbot. This Skill ensures the chatbot is set up with Olakai from day one, allowing for immediate tracking of response times, customer satisfaction KPIs, and error rates.

Quick Start

Use the new-project skill to set up a new AI agent with Olakai monitoring, including SDK integration and KPI configuration.

Frequently Asked Questions about new-project

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

FAQPage Schema
How do I set up a new AI agent project with KPI monitoring and SDK integration?▼

You can set up a new AI agent by configuring the platform, designing custom data schemas, integrating the Olakai SDK in TypeScript or Python, and validating the data flow to enable immediate KPI tracking.

What is the best way to define custom KPIs for an AI customer support chatbot?▼

Defining custom KPIs for an AI chatbot involves using provided templates or custom formulas during project setup, enabling operational tracking of metrics like response times, customer satisfaction, and error rates.

Does Olakai SDK integration support both TypeScript and Python for AI agent monitoring?▼

Yes, Olakai SDK integration supports both TypeScript and Python, providing clear instructions for implementing monitoring, governance, and KPI tracking for agentic and assistive AI agents.

Can I use custom formulas to track operational KPIs for agentic and assistive AI?▼

Yes, you can implement custom formulas to define business-specific KPIs for both agentic and assistive AI types, ensuring operational tracking and business insights via SDK or REST API implementation.

How do I validate data flow and SDK integration after creating an AI agent?▼

You validate data flow by following the end-to-end validation steps included in the project setup, testing the SDK implementation and ensuring KPI tracking and monitoring data are flowing correctly.

When do I need to configure a custom data schema for AI agent monitoring?▼

You need to configure a custom data schema during initial project setup when your AI agent requires specific business insights and operational tracking, forming the foundation for accurate KPI definition.

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