Add specialization study tracks guide to Web3AI

Author: lippytm

SPECIALIZATION_STUDY_TRACKS.md

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+# Specialization Study Tracks
+
+This document defines how deeper specialization areas can be taught as reusable study tracks inside the wider `lippytm` ecosystem.
+
+## Purpose
+Turn growing specialization areas into clearer tracks that builders can study, practice, review, and eventually package into reusable systems.
+
+## Core Principle
+A good specialization track should connect setup, exercises, review loops, sandbox practice, and reusable outputs instead of being just a list of topics.
+
+## Study Track Types
+
+### 1. Programming Tracks
+Use for:
+- language practice
+- debugging and testing
+- toolkit usage
+- environment-specific exercises
+
+### 2. Machine Learning Tracks
+Use for:
+- workflow practice
+- evaluation exercises
+- retrieval and embeddings practice
+- experimentation and review loops
+
+### 3. Blockchain Tracks
+Use for:
+- wallet practice
+- contract practice
+- testing and deployment exercises
+- safe experimentation paths
+
+### 4. Agent and Workflow Tracks
+Use for:
+- routing exercises
+- secure workflow practice
+- evaluation loops
+- sandboxed agent patterns
+
+## Core Parts of a Track
+- setup path
+- lesson or exercise path
+- review path
+- sandbox path
+- reuse path into packs, kits, or templates
+
+## Best Practices
+- keep tracks modular
+- attach setup, review, and security notes
+- connect repeated study patterns into reusable packs
+- use study loops to decide what should become a specialization product or kit
+- archive weak tracks clearly when stronger versions replace them
+
+## Connected Repositories
+- `lippytm-lippytm.ai-tower-control-ai`
+- `MyClaw.lippytm.AI-`
+- `OpenClaw-lippytm.AI-`
+- `Factory.ai`
+- `The-Encyclopedia-of-Everything-Applied-ChatAIBots`
+
+## Guiding Principle
+Specialization becomes more teachable when it is organized into repeatable study tracks with setup, practice, review, and reuse paths.