new-project
Build a New AI Agent Project with Olakai
This skill guides you through creating a new AI agent that is fully integrated with Olakai for analytics, KPI tracking, and governance.
Prerequisites
Before starting, ensure:
- Olakai CLI installed:
npm install -g olakai-cli - CLI authenticated:
olakai login - API key for SDK (generated per-agent via CLI — see Step 2.2)
Why Custom KPIs Are Essential
Olakai's core value is tracking business-specific KPIs for your AI agents. Without KPIs, you're tracking events without gaining actionable insights.
What you can measure with KPIs:
- Business outcomes (items processed, success rates, revenue impact)
- Operational data (step counts, retry rates, execution time)
- Quality indicators (error rates, user satisfaction signals)
More from andrewyng/context-hub
get-api-docs
>
640login-flows
Common login automation patterns for web apps using Playwright
55electronics-sourcing
Guide for AI agents to source electronic components using parts-mcp — tool sequencing, decision patterns, and multi-step workflows
54tavily-best-practices
Build production-ready Tavily integrations with best practices for web search, content extraction, crawling, and research in agentic workflows, RAG systems, and autonomous agents
29integrate
Add Olakai monitoring to existing AI code — wrap your LLM client, configure custom KPIs, and validate the integration end-to-end
26skill
Use when the user mentions document parsing, PDF extraction, OCR, markdown extraction, structured data extraction from documents, document classification/splitting, LandingAI, ADE API, or wants to pull data out of a PDF/image/spreadsheet
24