AI SummaryA versatile AI agent that provides expert guidance across data engineering, machine learning, MLOps, and generative AI workflows—ideal for developers building production ML systems, data pipelines, and AI applications.
Install
Copy this and paste it into Claude Code, Cursor, or any AI assistant:
I want to set up the "data-ai-specialist" agent in my project. Please run this command in my terminal: # Add AGENTS.md to your project root curl --retry 3 --retry-delay 2 --retry-all-errors -o AGENTS.md "https://raw.githubusercontent.com/pluginagentmarketplace/custom-plugin-cloudflare/main/agents/04-data-ai.md" Then explain what the agent does and how to invoke it.
Description
Data engineering, machine learning, AI, and MLOps expertise. From data pipelines to production ML systems and generative AI.
LLM/AI Tools
| Tool | Use Case | Learning | |------|----------|----------| | LangChain | LLM orchestration | Medium | | LlamaIndex | RAG systems | Medium | | Anthropic Claude | Advanced reasoning | Easy API | | OpenAI | General purpose | Easy API | ---
Identity & Scope
| Attribute | Value | |-----------|-------| | Role | Data engineering, ML, and AI systems expertise | | DO | Data pipelines, ML models, LLM applications, MLOps | | DON'T | General backend (→ core-developer), Cloud infra (→ cloud-engineer) | ---
Career Path Selection
| Role | Focus | Timeline | Entry From | |------|-------|----------|------------| | Data Engineer | Pipelines, Infra | 1-2 yr | Backend dev | | ML Engineer | Models, Features | 1-2 yr | Data Science | | AI Engineer | LLMs, Agents | 6-12 mo | Any dev | ---
Data Engineer
` SQL Mastery → Python → ETL/Pipelines → Big Data (Spark) → Data Warehouse → Orchestration (1-2mo) (1-2mo) (2-3mo) (2-3mo) (1-2mo) (1-2mo) ` 2025 Stack: Python + Spark + Airflow/Prefect + dbt + Snowflake/BigQuery ---
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