• Level 0 → Basic Prompting
• Level 1 → Structured Prompt Engineering
• Level 2 → Rules & Skills Layer
• Level 3 → MCP & Tool Use
• Level 4 → Agent Architecture
• Level 5 → RAG for Large Codebases
• Level 6 → CI/CD Automation
• Level 7 → Multi-Agent Systems
• Level 8 → Production AI Engineering Platform

Goal: Use Claude effectively in daily coding tasks.

Learn:

• Role prompting
• Clear constraints
• Output formatting
• Step-by-step reasoning
• Diff generation

Keywords:

• Prompt engineering
• Zero-shot vs Few-shot
• Chain-of-thought
• Structured output
• Markdown diff

Goal: Make prompts predictable and reusable.

Learn:

• Prompt templates
• Role / Task / Constraints / Output format structure
• Self-critique loops
• Persona switching
• JSON output enforcement

Keywords:

• Meta-prompt
• Output schema
• Deterministic formatting
• Guardrails
• Prompt injection awareness

Goal: Create reusable AI capabilities.

Learn:

• Global system rules
• Skill abstraction
• Prompt libraries
• Code-style enforcement
• Domain-specific DSL prompts

Keywords:

• System prompt
• Skill templates
• Reusable instruction blocks
• Behavioral constraints
• AI coding standards

Goal: Let Claude interact with your codebase.

Learn:

• Model Context Protocol (MCP)
• Tool calling
• Filesystem access
• Git integration
• Context window management

Keywords:

• MCP
• Tool invocation
• Function calling
• Agent tool loop
• Context control

Goal: Build Claude as a reasoning agent.

Learn:

• Observe → Plan → Act → Reflect loop
• Multi-step reasoning
• File search orchestration
• Error recovery loops
• Iterative refinement

Keywords:

• ReAct pattern
• Agent loop
• Reflection prompting
• Autonomous task execution
• Task decomposition

Goal: Scale Claude to large repositories.

Learn:

• Embeddings
• Vector databases
• Chunking strategies
• Retrieval ranking
• Code summarization

Keywords:

• Retrieval-Augmented Generation (RAG)
• Vector DB (FAISS, Pinecone, Weaviate)
• Similarity search
• Context compression
• Hybrid search

Goal: Integrate Claude into development lifecycle.

Learn:

• Git hooks
• PR review bots
• GitHub Actions
• Structured diff comments
• AI quality gates

Keywords:

• AI code review
• Pre-commit hooks
• Pull request summarizer
• CI automation
• Static analysis augmentation

Goal: Multiple AI roles collaborating.

Example Agents:

• Architect Agent
• Refactor Agent
• Test Generator Agent
• Security Agent
• Performance Agent

Keywords:

• Multi-agent orchestration
• Task planner agent
• Tool router
• Hierarchical agents
• Cooperative AI

Goal: Build internal AI coding infrastructure.

Learn:

• Token budgeting
• Cost optimization
• Logging & observability
• Output evaluation metrics
• Human-in-the-loop systems

Keywords:

• AI observability
• Token efficiency
• Acceptance rate metrics
• Prompt versioning
• Model fallback strategy

• AST-based code modification
• Tree-sitter parsing
• Semantic diff generation
• LLM hallucination detection
• Spec-driven development
• AI security hardening
• Sandboxed execution

Month 1

• Level 0–1
• Structured prompts + JSON output

Month 2

• Level 2
• Git hook integration

Month 3

• Level 3–4
• Agent loop + MCP

Month 4

• Level 5
• RAG for your repository

Month 5

• Level 6+
• CI automation + Metrics

Claude Code is not just AI that writes code.

It becomes a programmable reasoning engine integrated into your engineering workflow.

Serious power begins at Level 4 and above.