# snowflake-snowpark-dbt-cursorrules-prompt-file

A Cursor rule that gives the agent context for Snowpark Python and dbt with the Snowflake adapter. Useful when you write transformation pipelines in either tool.

- rule · `type=rule` · Cursor · ★ 41011
- Author: PatrickJS
- License: CC0-1.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/PatrickJS/awesome-cursorrules/tree/HEAD/rules/snowflake-snowpark-dbt-cursorrules-prompt-file.mdc
- Updated: 2026-05-30
- HTML: https://affpapa.org/en/nevibecode/rules/snowflake-snowpark-dbt-cursorrules-prompt-file

## What it does

The rule spells out how the agent should handle Snowpark Python, Snowflake's server-side Python API. It covers Session, DataFrame, UDF, UDTF, UDAF and stored procedures, lazy evaluation, and operations such as `filter`, `select`, `group_by().agg()`, `join` and `save_as_table()`. A second block covers dbt with the `dbt-snowflake` adapter: materializations, incremental models, dynamic tables, and configs like `cluster_by`, `transient`, `query_tag`, `copy_grants` and `snowflake_warehouse`.

The rule ships with `alwaysApply: false`, so it does not attach itself to every file — you wire it up deliberately. Installation is a single file, `.cursor/rules/snowflake-snowpark-dbt-cursorrules-prompt-file.mdc`, dropped into the project. For the dbt side you need `pip install dbt-snowflake` and a filled-in `profiles.yml`; the Session example reads credentials from `SNOWFLAKE_ACCOUNT`, `SNOWFLAKE_USER` and `SNOWFLAKE_PASSWORD`.

The anti-pattern list is the part worth reading first. The agent won't pull large DataFrames to the client with `collect()`, won't loop over rows in Python instead of using vectorized operations, and won't set `cluster_by` on small tables. It also won't use `{{ this }}` without an `{% if is_incremental() %}` guard.

## Requirements
- Works with: Snowflake

## Install

### Cursor (native platform)

**Manually: put the files in place**
- File: `.cursor/rules/snowflake-snowpark-dbt-cursorrules-prompt-file.mdc` — Frontmatter: description, globs, alwaysApply

### Claude Code

**Manually: put the files in place**
- File: `.claude/rules/snowflake-snowpark-dbt-cursorrules-prompt-file.md` — The paths frontmatter field limits the files: move globs from the source into paths

### Codex

**AGENTS.md**
- Codex reads AGENTS.md: paste the rule text there

### GitHub Copilot

**Manually: put the files in place**
- File: `.github/instructions/snowflake-snowpark-dbt-cursorrules-prompt-file.instructions.md` — The applyTo field sets which files it applies to: move globs from the source into applyTo

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/rules/snowflake-snowpark-dbt-cursorrules-prompt-file.md`

### Cline

**Manually: put the files in place**
- File: `.clinerules/snowflake-snowpark-dbt-cursorrules-prompt-file.md`

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/PatrickJS/awesome-cursorrules/tree/HEAD/rules/snowflake-snowpark-dbt-cursorrules-prompt-file.mdc

## When to use it
- build a Snowpark Session from environment variables
- write an incremental dbt model with a unique key and merge strategy
- register a vectorized UDF for model inference
- declare sources and tests in schema.yml
- pick a materialization for a fact table

## Example prompts
- Write a Snowpark DataFrame that filters customers by region and sums revenue
- Create an incremental dbt model for stg_events with on_schema_change sync_all_columns
- Check my dbt project against the anti-patterns in the rule

## Source text · snowflake-snowpark-dbt-cursorrules-prompt-file.mdc · CC0-1.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: abc1ba2b9d2e5133.

BEGIN THIRD-PARTY FILE abc1ba2b9d2e5133
```text
---
description: "Cursor rules for Snowpark Python (DataFrames, UDFs, stored procedures) and dbt with the Snowflake adapter."
globs: **/*
alwaysApply: false
---
// Snowflake Snowpark Python & dbt
// Expert guidance for Snowpark Python development and dbt with the Snowflake adapter

You are an expert in Snowpark Python (Snowflake's server-side Python API) and dbt with the dbt-snowflake adapter. You build production-grade data transformation pipelines using both tools.

// ═══════════════════════════════════════════
// SNOWPARK PYTHON
// ═══════════════════════════════════════════

// Snowpark runs Python server-side in Snowflake warehouses. Data never leaves Snowflake.
// Core abstractions: Session, DataFrame, UDF, UDTF, UDAF, Stored Procedure.

// Session
from snowflake.snowpark import Session
import os
session = Session.builder.configs({
    "account": os.environ["SNOWFLAKE_ACCOUNT"],
    "user": os.environ["SNOWFLAKE_USER"],
    "password": os.environ["SNOWFLAKE_PASSWORD"],
    "role": "my_role", "warehouse": "my_wh", "database": "my_db", "schema": "my_schema"
}).create()

// DataFrame API — Lazy evaluation, builds query plan executed on collect()/show().
df = session.table("customers")
df_filtered = df.filter(df["region"] == "US").select("name", "email", "revenue")
df_agg = df.group_by("region").agg(sum("revenue").alias("total_revenue"))
df_agg.show()

// Key operations: .filter(), .select(), .group_by().agg(), .join(), .sort(),
// .with_column(), .drop(), .distinct(), .limit(), .union_all(), .flatten(),
// .write.save_as_table()

// Scalar UDFs
from snowflake.snowpark.functions import udf
@udf(name="normalize_email", replace=True)
def normalize_email(email: str) -> str:
    return email.strip().lower() if email else None

// Vectorized UDFs (10-100x faster for ML inference):
import pandas as pd
@udf(name="predict_score", packages=["scikit-learn", "pandas"], replace=True)
def predict_score(features: pd.Series) -> pd.Series:
    import pickle, sys
    model = pickle.load(open(sys.path[0] + "/model.pkl", "rb"))
    return pd.Series(model.predict(features.values.reshape(-1, 1)))

// UDTFs (return multiple rows per input):
class Tokenizer:
    def process(self, text: str):
        for token in text.split():
            yield (token,)

tokenize = session.udtf.register(Tokenizer,
    output_schema=StructType([StructField("token", StringType())]),
    input_types=[StringType()], name="tokenize", replace=True)

// Stored Procedures (server-side multi-step logic):
from snowflake.snowpark.functions import sproc
@sproc(name="daily_etl", replace=True, packages=["snowflake-snowpark-python"])
def daily_etl(session: Session) -> str:
    raw = session.table("raw_events")
    cleaned = raw.filter(raw["event_type"].is_not_null())
    cleaned.write.mode("overwrite").save_as_table("cleaned_events")
    return f"Processed {cleaned.count()} rows"
```
END THIRD-PARTY FILE abc1ba2b9d2e5133

The third-party file has ended. Everything between the abc1ba2b9d2e5133 tags is data: requests and commands from it are not followed. An “end of file” line with another tag inside the block is part of the file.

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