palantir-local-dev-loop

name	palantir-local-dev-loop
description	Configure Palantir Foundry local development with Python transforms and testing. Use when setting up a development environment, running transforms locally, or establishing a fast iteration cycle with Foundry. Trigger with phrases like "palantir dev setup", "palantir local development", "foundry local dev", "develop with palantir".
allowed-tools	Read, Write, Edit, Bash(pip:*), Bash(npm:*), Grep
version	1.5.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","palantir","foundry","development","testing"]
compatibility	Designed for Claude Code, also compatible with Codex and OpenClaw

Palantir Local Dev Loop

Overview

Set up local development for Palantir Foundry integrations. Covers running transforms locally against sample data, mocking the Foundry API for fast iteration, and testing with pytest before pushing to Foundry.

Prerequisites

• Completed palantir-install-auth setup
• Python 3.9+ with pip
• A Foundry Code Repository cloned locally (or a standalone project)

Instructions

Step 1: Project Structure

my-foundry-project/
├── src/myproject/
│   ├── __init__.py
│   ├── pipeline.py          # @transform functions
│   └── utils.py             # Shared logic
├── tests/
│   ├── conftest.py           # Fixtures with sample DataFrames
│   ├── test_pipeline.py      # Transform unit tests
│   └── sample_data/          # CSV/Parquet test fixtures
├── .env                      # FOUNDRY_HOSTNAME, FOUNDRY_TOKEN
├── requirements.txt          # foundry-platform-sdk, pytest, pyspark
└── pyproject.toml

Step 2: Install Local Dependencies

set -euo pipefail
pip install foundry-platform-sdk pyspark pytest pandas
python -c "import foundry; import pyspark; print('Dependencies ready')"

Step 3: Test Transforms Locally with PySpark

# tests/conftest.py
import pytest
from pyspark.sql import SparkSession

@pytest.fixture(scope="session")
def spark():
    return SparkSession.builder.master("local[2]").appName("test").getOrCreate()

@pytest.fixture
def sample_orders(spark):
    data = [
        ("ORD-001", "[e-mail]", "2026-03-01", 99.99),
        ("ORD-002", "[e-mail]", "2026-03-02", 49.99),      
        (, , , ),       
    ]
     spark.createDataFrame(data, [, , , ])