Pltr Cli

by anjor

datacli

Helps you work with Palantir Foundry using the pltr CLI. Use this when you need to query datasets, manage orchestration builds, work with ontologies, run SQL queries, manage folders/spaces/projects, copy datasets, or perform admin operations in Foundry. Triggers: Foundry, pltr, dataset, SQL query, ontology, build, schedule, RID.

Skill Details

Repository Files

19 files in this skill directory


name: pltr-cli description: Helps you work with Palantir Foundry using the pltr CLI. Use this when you need to query datasets, manage orchestration builds, work with ontologies, run SQL queries, manage folders/spaces/projects, copy datasets, or perform admin operations in Foundry. Triggers: Foundry, pltr, dataset, SQL query, ontology, build, schedule, RID.

pltr-cli: Palantir Foundry CLI

This skill helps you use the pltr-cli to interact with Palantir Foundry effectively.

Compatibility

  • Skill version: 1.1.0
  • pltr-cli version: 0.12.0+
  • Python: 3.9, 3.10, 3.11, 3.12
  • Dependencies: foundry-platform-sdk >= 1.69.0

Overview

pltr-cli is a comprehensive CLI with 100+ commands for:

  • Dataset operations: Get info, list files, download files, manage branches and transactions
  • SQL queries: Execute queries, export results, manage async queries
  • Ontology: List ontologies, object types, objects, execute actions and queries
  • Orchestration: Manage builds, jobs, and schedules
  • Filesystem: Folders, spaces, projects, resources
  • Admin: User, group, role management
  • Connectivity: External connections and data imports
  • MediaSets: Media file management
  • Language Models: Interact with Anthropic Claude models and OpenAI embeddings
  • Streams: Create and manage streaming datasets, publish real-time data
  • Functions: Execute queries and inspect value types
  • AIP Agents: Manage AI agents, sessions, and versions
  • Models: ML model registry for model and version management

Critical Concepts

RID-Based API

The Foundry API is RID-based (Resource Identifier). Most commands require RIDs:

  • Datasets: ri.foundry.main.dataset.{uuid}
  • Folders: ri.compass.main.folder.{uuid} (root: ri.compass.main.folder.0)
  • Builds: ri.orchestration.main.build.{uuid}
  • Schedules: ri.orchestration.main.schedule.{uuid}
  • Ontologies: ri.ontology.main.ontology.{uuid}

Users must know RIDs in advance (from Foundry web UI or previous API calls).

Authentication

Before using any command, ensure authentication is configured:

# Configure interactively
pltr configure configure

# Or use environment variables
export FOUNDRY_TOKEN="your-token"
export FOUNDRY_HOST="foundry.company.com"

# Verify connection
pltr verify

Output Formats

All commands support multiple output formats:

pltr <command> --format table    # Default: Rich table
pltr <command> --format json     # JSON output
pltr <command> --format csv      # CSV format
pltr <command> --output file.csv # Save to file

Profile Selection

Use --profile to switch between Foundry instances:

pltr <command> --profile production
pltr <command> --profile development

Reference Files

Load these files based on the user's task:

Task Type Reference File
Setup, authentication, getting started reference/quick-start.md
Dataset operations (get, files, branches, transactions) reference/dataset-commands.md
SQL queries reference/sql-commands.md
Builds, jobs, schedules reference/orchestration-commands.md
Ontologies, objects, actions reference/ontology-commands.md
Users, groups, roles, orgs reference/admin-commands.md
Folders, spaces, projects, resources, permissions reference/filesystem-commands.md
Connections, imports reference/connectivity-commands.md
Media sets, media items reference/mediasets-commands.md
Anthropic Claude models, OpenAI embeddings reference/language-models-commands.md
Streaming datasets, real-time data publishing reference/streams-commands.md
Functions queries, value types reference/functions-commands.md
AIP Agents, sessions, versions reference/aip-agents-commands.md
ML model registry, model versions reference/models-commands.md

Workflow Files

For common multi-step tasks:

Workflow File
Data exploration, SQL analysis, ontology queries workflows/data-analysis.md
ETL pipelines, scheduled jobs, data quality workflows/data-pipeline.md
Setting up permissions, resource roles, access control workflows/permission-management.md

Common Commands Quick Reference

# Verify setup
pltr verify

# Current user info
pltr admin user current

# Execute SQL query
pltr sql execute "SELECT * FROM my_table LIMIT 10"

# Get dataset info
pltr dataset get ri.foundry.main.dataset.abc123

# List files in dataset
pltr dataset files list ri.foundry.main.dataset.abc123

# Download file from dataset
pltr dataset files get ri.foundry.main.dataset.abc123 "/path/file.csv" "./local.csv"

# Copy dataset to another folder
pltr cp ri.foundry.main.dataset.abc123 ri.compass.main.folder.target456

# List folder contents
pltr folder list ri.compass.main.folder.0  # root folder

# Search builds
pltr orchestration builds search

# Interactive shell mode
pltr shell

# Send message to Claude model
pltr language-models anthropic messages ri.language-models.main.model.xxx \
    --message "Explain this concept"

# Generate embeddings
pltr language-models openai embeddings ri.language-models.main.model.xxx \
    --input "Sample text"

# Create streaming dataset
pltr streams dataset create my-stream \
    --folder ri.compass.main.folder.xxx \
    --schema '{"fieldSchemaList": [{"name": "value", "type": "STRING"}]}'

# Publish record to stream
pltr streams stream publish ri.foundry.main.dataset.xxx \
    --branch master \
    --record '{"value": "hello"}'

# Execute a function query
pltr functions query execute myQuery --parameters '{"limit": 10}'

# Get AIP Agent info
pltr aip-agents get ri.foundry.main.agent.abc123

# List agent sessions
pltr aip-agents sessions list ri.foundry.main.agent.abc123

# Get ML model info
pltr models model get ri.foundry.main.model.abc123

# List model versions
pltr models version list ri.foundry.main.model.abc123

Best Practices

  1. Always verify authentication first: Run pltr verify before starting work
  2. Use appropriate output format: JSON for programmatic use, CSV for spreadsheets, table for readability
  3. Use async for large queries: pltr sql submit + pltr sql wait for long-running queries
  4. Export results: Use --output to save results for further analysis
  5. Use shell mode for exploration: pltr shell provides tab completion and history

Getting Help

pltr --help                    # All commands
pltr <command> --help          # Command help
pltr <command> <sub> --help    # Subcommand help

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Skill Information

Category:Technical
Last Updated:1/31/2026