palantir-core-workflow-a
Build Palantir Foundry data pipelines using Python transforms. Use when creating ETL pipelines, writing @transform decorators, or building dataset-to-dataset processing in Foundry. Trigger with phrases like "palantir pipeline", "foundry transform", "palantir ETL", "palantir data pipeline", "foundry python transform". '
Profile →npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill palantir-core-workflow-a --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Palantir Core Workflow A — Data Pipelines with Transforms ## Overview Build Foundry data pipelines using the `transforms-python` library. Covers the `@transform` and `@transform_df` decorators, input/output dataset wiring, incremental transforms, and `@configure` for Spark tuning. This is the primary workflow for all data processing in Foundry. ## Prerequisites - Completed `palantir-install-auth
What does the palantir-core-workflow-a skill do?
Build Palantir Foundry data pipelines using Python transforms. Use when creating ETL pipelines, writing @transform decorators, or building dataset-to-dataset processing in Foundry. Trigger with phrases like "palantir pipeline", "foundry transform", "palantir ETL", "palantir data pipeline", "foundry python transform". '
How do I install it?
Run `npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill palantir-core-workflow-a --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From jeremylongshore/claude-code-plugins-plus-skills, a repository with 2,596 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.