Hey everyone! I've been struggling for weeks with the repetitive parts of building data pipelines—you know, writing the same boilerplate for every new dbt model, Airflow DAG, or Python data utility. I kept making tiny mistakes in the setup that would cause hours of debugging later. 😅
I finally tried using Aider's custom template feature, and it's been a game-changer for my sanity. I set up a few templates for my most common tasks:
* A dbt model template that pre-populates my standard config block, YAML refs, and a basic SELECT statement.
* A simple Airflow PythonOperator DAG template with the imports, default_args, and task skeleton already there.
* A Python class for loading data from our API to BigQuery, with the credential handling and error logging I always forget.
Before, I'd be copying from old files and hoping I didn't miss a comma or an import. Now I just tell Aider something like "Create a new dbt model for monthly customer aggregations using the sales source," and it builds the whole file correctly from my template. The consistency is amazing.
Has anyone else tried this approach? I'm curious about a couple of things:
* How do you organize your template files? I just have a folder in my project, but wondering if there's a better way.
* What are your most-used templates for data work? I feel like I'm probably missing some obvious ones for tests or Snowflake stages.
It’s not perfect—I still had to tweak the templates a few times to get them right—but it’s cut down my "starting friction" on new components by like 80%.
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Totally feel you on the sanity-saving aspect. That tiny import error you'd spend an hour on is exactly the kind of friction that adds up and kills momentum.
I've done something similar but found it's crucial to version control the templates alongside the project code. Otherwise, you'll have a teammate try to use a template that references an old library version or a deprecated config field. A little documentation in the repo README about how to use them goes a long way for onboarding.
How do you handle prompts when you need a variation on the template, like a different type of Airflow operator? Do you have separate templates, or do you edit the generated file?
Completely agree on version controlling the templates, that's non-negotiable. We treat them as shared infrastructure and even have a small CI check that lints them.
>How do you handle prompts when you need a variation on the template?
I use a single base template with Jinja-style placeholders for the variable parts, and my prompt to Aider specifies the values. For example, my Airflow template has `{{ operator_type }}`, `{{ task_id }}`, and `{{ connection_id }}` slots. The prompt looks like "Generate a DAG using the airflow_dag template, operator_type=PythonOperator, task_id=transform_data, connection_id=postgres_default". This keeps the template count manageable. The downside is you need to remember the exact placeholder names, but a commented section at the top of the template file lists them all.
Show me the benchmarks.