Your AI Learning Journey

Track your skills, follow structured paths, and get personalized recommendations from your agent.

5 proficient 3 learning 8 gaps 5 tracks

Profile last updated 2026-03-10

Skill Map

Proficient

  • Prompt Engineering Daily practice with Claude
  • Agentic Workflows Building the Intelligence Hub
  • Static Site Generation Astro + Vercel pipeline
  • GitHub API Automation Content publishing pipeline
  • Node.js Scripting Agent development

Learning

  • MCP (Model Context Protocol) Understand concept, haven't built servers yet
  • Claude API Using in agent, learning patterns
  • Multi-Agent Systems Phase 2D goal

Gaps

  • RAG (Retrieval Augmented Generation) trending Haven't explored yet
  • Fine-tuning No experience
  • Vector Databases No experience
  • LangChain / LlamaIndex Aware but haven't used
  • Python ML Stack Focused on Node.js so far
  • Model Evaluation & Benchmarks trending Consumer-level understanding
  • AI Safety & Alignment trending General awareness only
  • Computer Vision Haven't explored

Learning Tracks

All tracks →

Model Selection & Evaluation

beginner

Understand model tradeoffs, read benchmarks critically, and design multi-model routing strategies.

3 stages 8h estimated not started

Prompt Engineering

beginner

From basic prompts to advanced system prompt design — learn to communicate effectively with AI models.

4 stages 10h estimated in progress

Agentic Workflows

intermediate

Build AI systems that act autonomously — from single-step tools to multi-stage pipelines with decision loops.

5 stages 20h estimated in progress
Requires: prompt-engineering

MCP Integration

intermediate

Master the Model Context Protocol — from understanding the spec to building servers and orchestrating multi-agent systems.

4 stages 15h estimated not started
Requires: agentic-workflows

RAG Systems

intermediate

Build Retrieval Augmented Generation pipelines — from understanding embeddings to building and evaluating production RAG systems.

4 stages 15h estimated not started
Requires: prompt-engineering

Learning Queue

Full queue →

105 new items waiting

  • AI Safety & Alignment 1hr intermediate

    read

    As you build agentic systems with real API access and automation, understanding how agents handle permission boundaries is a practical safety gap worth closing now.

  • Agentic Workflows 15min intermediate

    read

    Agent-optimized CLI design patterns are directly applicable to your own tooling decisions in the Intelligence Hub pipeline.

  • Multi-Agent Systems 15min intermediate

    read

    Enterprise agent evaluation across 121 tools maps directly to your CIM Pipeline and Intelligence Hub — gives you a framework for thinking about robustness at scale.

  • Model Evaluation & Benchmarks 1hr intermediate

    read

    Bridges your consumer-level benchmark understanding toward practical eval design — especially relevant since you're building agentic systems that use Claude models.

  • Multi-Agent Systems 1hr intermediate

    read

    Directly informs Phase 2D goals — understanding how agents manage memory in long-running tasks is foundational architecture knowledge for scaling the Intelligence Hub.