palantir-reference-architecture
Implement Palantir Foundry reference architecture with best-practice project layout. Use when designing new Foundry integrations, planning data pipeline architecture, or establishing patterns for Ontology-driven applications. Trigger with phrases like "palantir architecture", "foundry best practices", "foundry project structure", "how to organize palantir".
Best use case
palantir-reference-architecture is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Implement Palantir Foundry reference architecture with best-practice project layout. Use when designing new Foundry integrations, planning data pipeline architecture, or establishing patterns for Ontology-driven applications. Trigger with phrases like "palantir architecture", "foundry best practices", "foundry project structure", "how to organize palantir".
Teams using palantir-reference-architecture 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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/palantir-reference-architecture/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How palantir-reference-architecture Compares
| Feature / Agent | palantir-reference-architecture | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Implement Palantir Foundry reference architecture with best-practice project layout. Use when designing new Foundry integrations, planning data pipeline architecture, or establishing patterns for Ontology-driven applications. Trigger with phrases like "palantir architecture", "foundry best practices", "foundry project structure", "how to organize palantir".
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.
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SKILL.md Source
# Palantir Reference Architecture
## Overview
Production-ready architecture for Foundry-integrated applications. Covers the standard data pipeline pattern (ingest > clean > model > serve), Ontology design, external API integration, and multi-repo project layout.
## Prerequisites
- Foundry enrollment with project access
- Understanding of Ontology concepts (object types, link types, actions)
- Familiarity with `palantir-core-workflow-a` (transforms) and `palantir-core-workflow-b` (Ontology)
## Instructions
### Step 1: Data Pipeline Architecture
```
┌─────────────┐ ┌──────────────┐ ┌─────────────┐ ┌───────────┐
│ Raw Layer │────>│ Clean Layer │────>│ Model Layer │────>│ Ontology │
│ (ingested) │ │ (validated) │ │ (enriched) │ │ (objects) │
└─────────────┘ └──────────────┘ └─────────────┘ └───────────┘
↑ Connectors @transform_df @transform_df Object types
↑ REST sync null checks joins, aggs Link types
↑ File upload type casting ML features Actions
```
### Step 2: Project Layout (Foundry)
```
Foundry Project: "Customer Analytics"
├── Datasets/
│ ├── raw/ # Ingested from sources
│ │ ├── raw_orders # REST connector → CRM
│ │ ├── raw_customers # JDBC connector → DB
│ │ └── raw_products # File upload (CSV/Parquet)
│ ├── clean/ # Validated, typed
│ │ ├── clean_orders # Nulls removed, dates parsed
│ │ ├── clean_customers # Deduped, normalized
│ │ └── clean_products # Schema enforced
│ └── model/ # Enriched, analytics-ready
│ ├── order_enriched # Joined with customer + product
│ ├── customer_360 # Aggregated customer view
│ └── daily_summary # Time-series aggregation
├── Code Repositories/
│ ├── pipeline-ingestion/ # Connectors and raw → clean
│ ├── pipeline-analytics/ # Clean → model transforms
│ └── ontology-actions/ # Action implementations
└── Ontology/
├── Object Types: Customer, Order, Product
├── Link Types: Customer→Orders, Order→Products
└── Actions: createOrder, updateCustomerSegment
```
### Step 3: External API Integration Pattern
```python
# External app consuming Foundry Ontology via Platform SDK
my-external-app/
├── src/
│ ├── foundry/
│ │ ├── client.py # Singleton FoundryClient
│ │ ├── objects.py # Object query helpers
│ │ ├── actions.py # Action wrappers
│ │ └── cache.py # TTL cache layer
│ ├── api/
│ │ ├── routes.py # REST endpoints
│ │ └── webhooks.py # Foundry event handlers
│ └── main.py
├── tests/
│ ├── conftest.py # Mocked FoundryClient
│ ├── test_objects.py
│ └── test_actions.py
├── .env # FOUNDRY_HOSTNAME, credentials
└── requirements.txt
```
### Step 4: Ontology Design Patterns
| Pattern | When to Use | Example |
|---------|-------------|---------|
| Hub-and-spoke | Central entity with many relationships | Customer → Orders, Tickets, Payments |
| Event sourcing | Audit trail needed | OrderEvent (created, shipped, delivered) |
| Computed properties | Derived values | `totalRevenue` on Customer (sum of orders) |
| Composite actions | Multi-step mutations | `processReturn`: update order + create credit + notify |
### Step 5: Security Layers
```
┌──────────────────────────────────────────┐
│ Layer 1: Network (VPN/private link) │
├──────────────────────────────────────────┤
│ Layer 2: OAuth2 (service user per app) │
├──────────────────────────────────────────┤
│ Layer 3: Scopes (minimum per app) │
├──────────────────────────────────────────┤
│ Layer 4: Project roles (Viewer/Editor) │
├──────────────────────────────────────────┤
│ Layer 5: Marking (data classification) │
└──────────────────────────────────────────┘
```
## Output
- Standard 3-layer data pipeline (raw > clean > model)
- Ontology design with typed objects, links, and actions
- External app architecture with caching and webhooks
- Security model with 5 defense layers
## Error Handling
| Architecture Issue | Symptom | Fix |
|--------------------|---------|-----|
| Circular dependencies | Builds fail | Restructure pipeline DAG |
| Missing clean layer | Bad data in model | Always validate between raw and model |
| Monolithic transforms | Slow builds | Split into focused transforms |
| No caching | API rate limits | Add TTL cache layer |
## Resources
- [Foundry Documentation](https://www.palantir.com/docs/foundry)
- [Ontology SDK Overview](https://www.palantir.com/docs/foundry/ontology-sdk/overview)
- [Transforms Guide](https://www.palantir.com/docs/foundry/transforms-python/transforms)
## Next Steps
For data handling and compliance, see `palantir-data-handling`.Related Skills
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