Exporting Workshops

Workshop Export

Export learning workshops to standalone projects with all necessary artifacts for maximum AI-assisted learning

Workshop Export Skill

Purpose

Export any learning workshop to a standalone project with all necessary artifacts for maximum AI-assisted learning.

Artifacts Generated

| Artifact | Purpose | |-|| | .cursorrules | Project-level AI rules and context | | {directories.rules}/<lang>.md | Technology-specific coding rules | | .cursor/knowledge/*.json | Relevant knowledge files | | .cursor/WORKSHOP_CONTEXT.md | AI assistant guidance for workshop phases | | README.md | Workshop overview, exercises, objectives | | package.json / requirements.txt | Dependencies | | src/exercises/ | Starter code for exercises | | .solutions/ | Hidden solutions (gitignored) | | .gitignore | Clean repository patterns |

Usage

Via Python Script

# From factory root
python {directories.scripts}/workshops/export_workshop.py <workshop_id> <target_directory>

# Examples
python {directories.scripts}/workshops/export_workshop.py l01-ethereum-fundamentals {TARGET_DIR}
python {directories.scripts}/workshops/export_workshop.py l07-langchain-fundamentals {TARGET_DIR}
python {directories.scripts}/workshops/export_workshop.py l05-crewai-multiagent {TARGET_DIR}

Via Agent Request

When user asks to start a workshop:

1. Identify the workshop from available options:

   • Blockchain: L1 (Ethereum), L2 (Bitcoin), L4 (Solana)
   • AI: L3 (LangGraph), L5 (CrewAI), L6 (HuggingFace), L7 (LangChain), L8 (RAG), L16-L18
   • Web: L9 (React), L10 (Next.js), L11 (FastAPI)
   • Cloud: L12 (Kubernetes), L13 (Docker)
   • ML: L14 (PyTorch), L15 (Fine-tuning)

2. Ask for target directory if not specified

3. Run the export script:

   python {directories.scripts}/workshops/export_workshop.py {workshop_id} {target_dir}
   

4. Guide user to open the new project in Cursor

Available Workshops

| ID | Name | Category | |-||-| | l01-ethereum-fundamentals | Ethereum Smart Contracts | Blockchain | | l02-bitcoin-lightning | Bitcoin & Lightning | Blockchain | | l03-langgraph-workflows | LangGraph Workflows | AI | | l04-solana-fundamentals | Solana Development | Blockchain | | l05-crewai-multiagent | CrewAI Multi-Agent | AI | | l06-huggingface-transformers | HuggingFace Transformers | AI | | l07-langchain-fundamentals | LangChain Agents | AI | | l08-rag-systems | RAG Systems | AI | | l09-react-modern | Modern React | Web | | l10-nextjs-fullstack | Next.js Fullstack | Web | | l11-fastapi-production | FastAPI Production | Web | | l12-kubernetes-production | Kubernetes | Cloud | | l13-docker-containerization | Docker | Cloud | | l14-pytorch-deeplearning | PyTorch Deep Learning | ML | | l15-llm-finetuning | LLM Fine-Tuning | ML | | l16-langsmith-observability | LangSmith Observability | AI | | l17-anthropic-tool-agents | Anthropic Tool Agents | AI | | l18-deep-research-agents | Deep Research Agents | AI |

Post-Export Steps

After export, guide the user:

1. Open the project in a new Cursor window
2. Install dependencies:
   • npm: npm install
   • pip: pip install -r requirements.txt
3. Read README.md for workshop overview
4. Follow the phases: Concept → Demo → Exercise → Challenge → Reflection

Customization

The export script uses stack configurations to customize output:

• Blockchain projects: Hardhat config, Solidity rules, contract templates
• Python AI projects: pytest config, type hints, async patterns
• Web projects: Framework-specific configs and patterns
• Cloud projects: YAML configs, deployment templates

Integration with Workshop Facilitator

The exported project is designed to work with the @workshop-facilitator agent:

@workshop-facilitator Guide me through the L1 Ethereum workshop

The facilitator uses:

• WORKSHOP_CONTEXT.md for phase-specific guidance
• Knowledge files for accurate information
• Exercise hints for progressive assistance

Example Workflow

User: I want to learn LangChain

Agent: I'll set up the LangChain workshop for you.

1. Runs: python {directories.scripts}/workshops/export_workshop.py l07-langchain-fundamentals {TARGET_DIR}
2. Project created with:
   - .cursorrules with LangChain context
   - {directories.rules}/python.md with Python best practices
   - Knowledge files for LangChain patterns
   - Exercise starter code
   - Comprehensive README

3. Guide user to open project and begin learning

Troubleshooting

Best Practices

• Validate workshop ID before export - confirm the workshop exists and matches user's learning goals to avoid wasted time
• Ensure target directory is clean or doesn't exist - prevent accidental overwrites by checking directory state first
• Include comprehensive README.md in exports - learners need clear guidance on objectives, phases, and how to use the workshop
• Structure exercises progressively - start with concepts, move to demos, then exercises, building complexity gradually
• Keep solutions hidden but accessible - use .solutions/ directory with .gitignore so learners can check answers without temptation
• Test exported projects before delivery - verify dependencies install correctly and starter code runs on clean environments

Part of the Antigravity Agent Factory Learning System

When to Use

This skill should be used when strict adherence to the defined process is required.

Prerequisites

• Basic understanding of the agent factory context.
• Access to the necessary tools and resources.

Process

1. Review the task requirements.
2. Apply the skill's methodology.
3. Validate the output against the defined criteria.