Abigail Green
Data Engineering Manager
Salt Lake City, Utah, United States
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Abigail Green is a Data Engineering Manager at CHG Healthcare, where she leads teams across several Data Platform Engineering contexts, managing several large-scale dbt Cloud environments on Snowflake (4,200+ SQL files). She is a contributor to jCodeMunch MCP, where she authored the extensible context provider framework and the dbt-specific integration that enriches code indexes with model metadata, column documentation, and lineage from dbt manifests. She has built extensively with MCP tooling to connect AI agents to dbt, Jira, Snowflake, and documentation workflows.
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Stop Feeding Your Agent the Whole Repo: Token-Efficient Code Intelligence for dbt Projects
Most AI agents explore dbt projects the expensive way opening entire files and skimming thousands of irrelevant lines. We benchmarked this on a ~4,200 model dbt project: understanding a 2,455 line model costs 35,000 tokens via file reads. With structured retrieval via tree-sitter AST indexing, it's 3,000 tokens and adds critical business-context summaries instead of raw SQL to help your agent focus on what's important.
I'll share how I contributed a dbt context provider to jCodeMunch MCP that auto-enriches the code index with model descriptions, tags, and resolved {{ doc() }} column documentation from our project's schema.yml. I'll walk through benchmarked savings (64% token reduction, ~$11/session across 20 queries), the search_columns tool for cross-model column discovery (77% cheaper than Grep), and honest guidance on where structured retrieval wins versus where traditional tools still do.
The talk focuses on cost optimization, developer experience, and working with AI agents.
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