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Agent skill

Palantir Core Workflow A

by jeremylongshore in jeremylongshore/tons-of-skills-marketplace

Design and validate a Foundry Python transform pipeline with explicit datasets, compute choice, expectations, and incremental semantics.

MITAuto-check passedData & Analytics

Install Palantir Core Workflow A

skills CLI
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill palantir-core-workflow-a -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace palantir-core-workflow-a --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.curated/palantir-core-workflow-a .claude/skills/palantir-core-workflow-a && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
palantir-core-workflow-a
GitHub stars
2.8k
Token cost
~1.3k tokens
SKILL.md length
602 words
Files
2 (incl. references)
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Design and validate a Foundry Python transform pipeline with explicit datasets, compute choice, expectations, and incremental semantics.

  • Works in 5 steps: Write the pipeline contract: owner,… → Choose the engine from required features… → Implement the transform with explicit… → …
  • Changing batch data pipelines
  • SKILL.md covers Overview, Prerequisites, Current Contract and Instructions, plus 7 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Palantir Core Workflow A is an agent skill from jeremylongshore/tons-of-skills-marketplace. Design and validate a Foundry Python transform pipeline with explicit datasets, compute choice, expectations, and incremental semantics. Use when building or changing batch data pipelines. Trigger with "Foundry transform" or "Palantir data pipeline".

Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/official-docs.md`). Compatibility notes: Requires current Palantir Foundry documentation and approved access for any live resource, permission, data, build, application, or deployment change

It sits in Data & Analytics, covering Data pipelines and ETL. It works with Python. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.

When your agent uses it

  • Changing batch data pipelines
  • With Foundry transform
  • Palantir data pipeline

Example prompts

  • “Foundry transform”
  • “Palantir data pipeline”
  • “/palantir-core-workflow-a”

Requirements

  • Python 3
  • Compatibility (from SKILL.md): Requires current Palantir Foundry documentation and approved access for any live resource, permission, data, build, application, or deployment change
  • Pre-approved tools (allowed-tools): Read, Glob, Grep, Write, Edit

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Write the pipeline contract: owner, inputs, output, primary key or deduplication rule, schema, data-quality expectations, and recovery…
  2. Choose the engine from required features and observed scale; document why single-node or Spark is appropriate.
  3. Implement the transform with explicit input and output declarations and keep pure business logic separately testable.
  4. If incremental processing is justified, define input read modes, output write mode, late-arrival behavior, and snapshot recovery.
  5. Preview representative cases, run repository checks, build on the branch, inspect metrics and output transactions, then request review.

What it can do on your machine

Read from SKILL.md and the folder at commit cfae287. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Glob
    • Grep
    • Write
    • Edit

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Requires current Palantir Foundry documentation and approved access for any live resource, permission, data, build, application, or deployment change

    From compatibility in the SKILL.md frontmatter.

Context cost

Palantir Core Workflow A loads about 1.3k tokens when it runs, and up to ~1.6k if it reads all its reference files. Until then it costs about 69 tokens; SKILL.md has 602 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~69
When it runs · the whole SKILL.md, loaded when a task matches
~1.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 602 words, ~1,338 tokens.

Download SKILL.mdSave it as .claude/skills/palantir-core-workflow-a/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
palantir-core-workflow-a
description
Design and validate a Foundry Python transform pipeline with explicit datasets, compute choice, expectations, and incremental semantics. Use when building or changing batch data pipelines. Trigger with "Foundry transform" or "Palantir data pipeline".
allowed-tools
Read, Glob, Grep, Write, Edit
compatibility
Requires current Palantir Foundry documentation and approved access for any live resource, permission, data, build, application, or deployment change
version
2.0.0
argument-hint
[repository-and-output-dataset]
model
inherit
effort
high
author
Jeremy Longshore <jeremy@intentsolutions.io>
license
MIT
tags
saas, palantir, foundry, transforms, data-pipelines

Palantir Python Transform Pipeline

Overview

Create a pipeline whose inputs, outputs, engine, write behavior, and quality checks are explicit before the first production build. Prefer the simplest supported compute engine that satisfies data scale and feature requirements, then prove the choice with Foundry metrics.

Prerequisites

  • Identify the owning project, Code Repository, input and output datasets, schema contract, data classification, and build schedule.
  • Confirm whether the workload requires Spark or can use a single-node engine such as Polars, pandas, or DuckDB.
  • Read references/official-docs.md and inspect current input transaction history before selecting incremental semantics.
  • Develop on a sandbox branch with representative but appropriately protected data.

Current Contract

  • Python transforms support batch and incremental pipelines, reusable libraries, expectations, and single-node or distributed engines.
  • Input and output datasets must differ; using the same dataset creates a cyclic dependency.
  • Incremental input modes and output write modes have precise transaction semantics; modify and replace are not interchangeable.
  • A snapshot build may be needed when incremental transaction history becomes progressively slow or invalid.

Instructions

  1. Write the pipeline contract: owner, inputs, output, primary key or deduplication rule, schema, data-quality expectations, and recovery objective.

  2. Choose the engine from required features and observed scale; document why single-node or Spark is appropriate.

  3. Implement the transform with explicit input and output declarations and keep pure business logic separately testable.

  4. If incremental processing is justified, define input read modes, output write mode, late-arrival behavior, and snapshot recovery.

  5. Preview representative cases, run repository checks, build on the branch, inspect metrics and output transactions, then request review.

Tool Discipline

  • Use Glob to locate candidate repositories, manifests, configurations, and evidence without widening scope.
  • Use Grep to find relevant identifiers, declarations, permissions, errors, and stale claims.
  • Use Read to inspect the smallest required files and authoritative evidence.
  • Use Write only for a new approved local draft, test, manifest, or evidence artifact.
  • Use Edit only for a bounded approved change whose rollback is known.
  • Do not use file tools as a substitute for authenticated Foundry operations or owner approval.

Approval Boundaries

The data owner must approve new outputs, schema changes, marking changes, retention behavior, and production schedules. An incremental conversion also requires an approved snapshot and rollback plan.

Show full SKILL.md (237 more words)Show less

Output

A pipeline contract, reviewed transform change, test evidence, branch build, metrics, output validation, ownership record, and recovery procedure. Include the exact input transactions and output transaction used for acceptance.

Error Handling

ConditionResponse
A cycle is detectedSeparate the input and output datasets and redesign any feedback loop.
Incremental output duplicates rowsStop promotion and correct keys, read modes, write mode, or late-arrival handling; rebuild from a controlled snapshot.
The build is memory-boundInspect Foundry metrics, then change engine or resources from measured evidence rather than a fixed size band.
Schema drifts unexpectedlyFail the expectation, quarantine the output transaction, and resolve the producer contract.

Examples

Example 1

Convert an append-only event transform to incremental processing by defining the added input behavior, deduplication key, modify output behavior, snapshot recovery, and parity check against a full rebuild.

Example 2

Move a medium-scale production transform from Spark to Polars only after feature compatibility, branch-build duration, memory, and output parity demonstrate the single-node engine is appropriate.

Validation

  • Repository checks and unit tests pass on the exact branch commit.
  • Preview and full build cover representative edge cases and protected-data rules.
  • Incremental and snapshot outputs reconcile to the defined tolerance.
  • Metrics support the selected engine and resource request.
  • The output owner confirms schema, quality, lineage, and rollback.

Resources

© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in skills/.curated/palantir-core-workflow-a of jeremylongshore/tons-of-skills-marketplace.

  • SKILL.md
  • references/official-docs.md

Open the folder on GitHubat commit cfae287

Compare with similar skills

Palantir Core Workflow A next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Palantir Core Workflow A compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Palantir Core Workflow A this skilljeremylongshore/tons-of-skills-marketplace2.8k—~1.3kAutomated safety check: PassMIT
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Monitor With HaolemeHaolemeApp/Haoleme157—~1.3kAutomated safety check: PassAGPL-3.0
Tushare Plugin BuilderYourdaylight/stock_datasource189—~2.5kAutomated safety check: PassMIT
Credit Risk Data Cleaninggithub/awesome-copilot40k1 repos~1.5kAutomated safety check: PassMIT
Dbt Parser Refreshyu-iskw/dbt-artifacts-parser118—~716Automated safety check: PassApache-2.0

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Works with

Categories

Questions about Palantir Core Workflow A

What does Palantir Core Workflow A do?

Design and validate a Foundry Python transform pipeline with explicit datasets, compute choice, expectations, and incremental semantics. Palantir Core Workflow A is an agent skill from jeremylongshore/tons-of-skills-marketplace. Design and validate a Foundry Python transform pipeline with explicit datasets, compute choice, expectations, and incremental semantics.

When should I use Palantir Core Workflow A?

Palantir Core Workflow A fits situations like: changing batch data pipelines; with Foundry transform; palantir data pipeline.

How do I install Palantir Core Workflow A in Claude Code?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill palantir-core-workflow-a -a claude-code`. Or copy the skill folder (skills/.curated/palantir-core-workflow-a in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/palantir-core-workflow-a in your project. Claude Code loads it when a task matches its description.

How do I install Palantir Core Workflow A in Codex?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill palantir-core-workflow-a -a codex`. Or copy the skill folder (skills/.curated/palantir-core-workflow-a in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/palantir-core-workflow-a in your project. Codex loads it when a task matches its description.

Can I use Palantir Core Workflow A in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill palantir-core-workflow-a -a cursor` (or -a -a, -a or -a for the others). To copy it by hand, put the folder in .cursor/skills/palantir-core-workflow-a, .gemini/skills/palantir-core-workflow-a, .github/skills/palantir-core-workflow-a and .opencode/skills/palantir-core-workflow-a in your project.

What does Palantir Core Workflow A need to run?

SKILL.md names no scripts, command-line tools or credentials: Palantir Core Workflow A is instructions for the agent only. Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Glob, Grep, Write, Edit. Compatibility (from SKILL.md): Requires current Palantir Foundry documentation and approved access for any live resource, permission, data, build, application, or deployment change.

Does Palantir Core Workflow A access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Palantir Core Workflow A safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Palantir Core Workflow A use?

Palantir Core Workflow A is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Palantir Core Workflow A use?

About 1.3k tokens (SKILL.md is roughly 5.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 261 tokens, read only when the agent opens those files.

What are the alternatives to Palantir Core Workflow A?

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Who maintains Palantir Core Workflow A?

jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,827 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 10, 2026.

Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.