pltr-cli

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.

7 stars

Best use case

pltr-cli is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

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.

Teams using pltr-cli should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/pltr-cli/SKILL.md --create-dirs "https://raw.githubusercontent.com/Demerzels-lab/elsamultiskillagent/main/public/skills/anjor/pltr-cli/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/pltr-cli/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How pltr-cli Compares

Feature / Agentpltr-cliStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

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.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 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:
```bash
# 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:
```bash
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:
```bash
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

```bash
# 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

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

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