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

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

Reference Files

Load these files based on the user's task:

reference/quick-start.md

reference/dataset-commands.md

reference/sql-commands.md

reference/orchestration-commands.md

reference/ontology-commands.md

reference/admin-commands.md

reference/filesystem-commands.md

reference/connectivity-commands.md

reference/mediasets-commands.md

reference/language-models-commands.md

reference/streams-commands.md

reference/functions-commands.md

reference/aip-agents-commands.md

reference/models-commands.md

Workflow Files

For common multi-step tasks:

workflows/data-analysis.md

workflows/data-pipeline.md

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