# Hamilton Dev Workflow

> Systematic 5-step workflow for building Hamilton DAGs - DOT graphs, signatures, validation, TDD implementation. Use this workflow when creating new Hamilton modules from scratch.

- Skill: `apache/hamilton-dev-workflow` (Agent Skill)
- Install (CLI): `npx skillmds@latest add apache/hamilton-dev-workflow`
- Raw SKILL.md: https://api.skillmd.com/api/skills/apache/hamilton-dev-workflow/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: apache (https://skillmd.com/u/apache)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/apache/hamilton-dev-workflow

---

<!-- SPDX-License-Identifier: Apache-2.0 -->

# Hamilton Development Workflow for Claude Code

**TL;DR**: Build Hamilton DAGs systematically using DOT → Signatures → Validation → TDD implementation.

## Why This Workflow?

This workflow optimizes for:
- **Token efficiency**: DOT language is extremely concise
- **Early validation**: Catch graph errors before implementation
- **LLM-friendly**: Well-structured, incremental approach
- **Testability**: Build complex systems node-by-node with tests
- **Pragmatic typing**: Handle type annotations strategically

## The 5-Step Workflow

### Step 1: Natural Language → DOT Graph

**Convert user requirements into DOT language representation**

DOT language is:
- Simple and well-understood by LLMs
- Extremely token-efficient
- Perfect for representing DAG structure
- Easy to validate visually

**Example:**
```
User: "Load CSV data, clean nulls, calculate 7-day rolling average, output top 10"

DOT representation:
digraph hamilton_dag {
    // Inputs
    data_path [shape=box, color=blue]

    // Pipeline (arrows show data flow: upstream -> downstream)
    data_path -> raw_data
    raw_data -> cleaned_data
    cleaned_data -> rolling_7d
    rolling_7d -> top_10
}
```

**Best Practices:**
- Use descriptive node names (snake_case)
- Mark inputs with `[shape=box, color=blue]`
- Add comments for clarity
- Keep it simple - just the graph structure

### Step 2: DOT Graph → Function Signatures + Docstrings

**Convert DOT nodes into Python function signatures**

This step is mechanical and can be automated. For each node:
1. Create a function with the node name
2. Add parameters based on incoming edges
3. Add return type annotation (use `Any` if uncertain)
4. Write a clear docstring explaining what the node computes

**Example:**
```python
"""Data processing pipeline."""
from typing import Any
import pandas as pd

def raw_data(data_path: str) -> pd.DataFrame:
    """Load raw data from CSV file.

    :param data_path: Path to CSV file
    :return: Raw DataFrame
    """
    pass

def cleaned_data(raw_data: pd.DataFrame) -> pd.DataFrame:
    """Remove null values and duplicates.

    :param raw_data: Raw data from previous step
    :return: Cleaned DataFrame
    """
    pass

def rolling_7d(cleaned_data: pd.DataFrame) -> pd.Series:
    """Calculate 7-day rolling average of sales.

    :param cleaned_data: Cleaned data
    :return: 7-day rolling average
    """
    pass

def top_10(rolling_7d: pd.Series) -> pd.Series:
    """Get top 10 days by value.

    :param rolling_7d: Rolling average values
    :return: Top 10 values
    """
    pass
```

**Type Annotation Strategy:**
- **Known types**: Use specific types (pd.DataFrame, pd.Series, int, str)
- **Uncertain**: Use `Any` - you'll fix this later with MonkeyType
- **Complex types**: Start with `Any`, refine incrementally

### Step 3: Validate DAG with Hamilton CLI

**Verify the graph structure before implementing logic**

Use the Hamilton CLI to validate:
```bash
# Validate the DAG structure
hamilton build --module my_pipeline

# Visualize the graph
hamilton build --module my_pipeline --output dag.png

# List all nodes
hamilton build --module my_pipeline --list-nodes
```

**What to check:**
- ✅ No circular dependencies
- ✅ All edges connect properly
- ✅ Input nodes are identified correctly
- ✅ Output nodes are what you expect
- ✅ No typos in function/parameter names

**If validation fails:**
- Fix the function signatures (parameter names, function names)
- Re-run `hamilton build`
- Don't proceed to implementation until validation passes

### Step 4: Function Signatures → TDD Implementation

**Implement nodes incrementally with test-driven development**

This is where the workflow shines for LLMs. Instead of writing all code at once:

**Process:**
1. Pick ONE node to implement (start with inputs, work forward)
2. Write a test for that node first
3. Implement the node to pass the test
4. Run the test to verify
5. Move to the next node

**Example - Node by node:**

```python
# Test for raw_data
def test_raw_data(tmp_path):
    """Test CSV loading."""
    # Setup
    csv_file = tmp_path / "data.csv"
    csv_file.write_text("col_a,col_b\n1,2\n3,4")

    # Execute
    result = raw_data(str(csv_file))

    # Verify
    assert isinstance(result, pd.DataFrame)
    assert len(result) == 2
    assert list(result.columns) == ['col_a', 'col_b']

# Implementation
def raw_data(data_path: str) -> pd.DataFrame:
    """Load raw data from CSV file."""
    return pd.read_csv(data_path)
```

```python
# Test for cleaned_data
def test_cleaned_data():
    """Test data cleaning."""
    # Setup
    raw = pd.DataFrame({
        'col_a': [1, 2, None, 4],
        'col_b': [1, 1, 2, 2]
    })

    # Execute
    result = cleaned_data(raw)

    # Verify
    assert len(result) == 3  # One null removed
    assert result['col_a'].isna().sum() == 0

# Implementation
def cleaned_data(raw_data: pd.DataFrame) -> pd.DataFrame:
    """Remove null values and duplicates."""
    return raw_data.dropna().drop_duplicates()
```

**Why this works:**
- **Focused context**: LLM only thinks about one node at a time
- **Immediate validation**: Each node is tested before moving on
- **Easy refactoring**: Well-tested nodes can be safely refactored
- **Handles complexity**: Build complex pipelines without "spaghetti code"
- **Clear progress**: Know exactly what's done and what's left

### Step 5: Handle Type Annotations Pragmatically

**Use MonkeyType or runtime inference to add accurate types**

Don't spend time guessing types upfront. Instead:

**Option A: Use `Any` everywhere initially**
```python
from typing import Any

def my_function(input_data: Any) -> Any:
    """Process data."""
    # Implementation here
    pass
```

**Option B: Disable type validation in Driver**
```python
from hamilton import driver
from hamilton.lifecycle import base

# Use an adapter that doesn't validate types
class NoTypeCheckAdapter(base.BaseDo):
    """Adapter that skips type validation."""
    pass

dr = driver.Driver(
    {},
    module,
    adapter=NoTypeCheckAdapter()
)
```

**Option C: Run MonkeyType to infer types**

After implementing and testing:
```bash
# Record runtime types
monkeytype run -m pytest tests/

# Generate type stubs
monkeytype stub my_pipeline

# Apply types to source
monkeytype apply my_pipeline
```

MonkeyType will:
- Observe actual values passed at runtime
- Infer concrete types from observations
- Generate accurate type annotations
- Update your source files

**Result**: Accurate types without upfront guessing!

**Important**: MonkeyType isn't perfect, but it's:
- ✅ Much faster than manual annotation
- ✅ Based on real runtime behavior
- ✅ Good enough for first drafts
- ✅ Easy to refine manually later

## Complete Example

**User Request:**
"Create a pipeline that loads JSON data, filters by status='active', enriches with external API data, and outputs to Parquet"

**Step 1: DOT Graph**
```dot
digraph pipeline {
    // Inputs
    json_path [shape=box, color=blue]
    api_endpoint [shape=box, color=blue]
    output_path [shape=box, color=blue]

    // Pipeline (arrows show data flow: upstream -> downstream)
    json_path -> raw_data
    raw_data -> active_records
    active_records -> enriched_data
    api_endpoint -> enriched_data
    enriched_data -> final_output
    output_path -> final_output
}
```

**Step 2: Function Signatures**
```python
"""Data enrichment pipeline."""
from typing import Any
import pandas as pd

def raw_data(json_path: str) -> pd.DataFrame:
    """Load data from JSON file.

    :param json_path: Path to JSON file
    :return: Raw DataFrame
    """
    pass

def active_records(raw_data: pd.DataFrame) -> pd.DataFrame:
    """Filter for active status only.

    :param raw_data: Raw data
    :return: Filtered DataFrame
    """
    pass

def enriched_data(active_records: pd.DataFrame, api_endpoint: str) -> pd.DataFrame:
    """Enrich with external API data.

    :param active_records: Active records
    :param api_endpoint: API endpoint URL
    :return: Enriched DataFrame
    """
    pass

def final_output(enriched_data: pd.DataFrame, output_path: str) -> str:
    """Save to Parquet file.

    :param enriched_data: Enriched data
    :param output_path: Output file path
    :return: Path to saved file
    """
    pass
```

**Step 3: Validate**
```bash
hamilton build --module enrichment_pipeline
# Output: ✓ DAG is valid, 4 nodes, 0 cycles
```

**Step 4: TDD Implementation**

*Test 1:*
```python
def test_raw_data(tmp_path):
    json_file = tmp_path / "data.json"
    json_file.write_text('[{"id": 1, "status": "active"}]')
    result = raw_data(str(json_file))
    assert len(result) == 1
```

*Implementation 1:*
```python
def raw_data(json_path: str) -> pd.DataFrame:
    return pd.read_json(json_path)
```

*Test 2:*
```python
def test_active_records():
    raw = pd.DataFrame([
        {"id": 1, "status": "active"},
        {"id": 2, "status": "inactive"}
    ])
    result = active_records(raw)
    assert len(result) == 1
    assert result.iloc[0]["status"] == "active"
```

*Implementation 2:*
```python
def active_records(raw_data: pd.DataFrame) -> pd.DataFrame:
    return raw_data[raw_data["status"] == "active"]
```

...continue for each node...

**Step 5: Add Types with MonkeyType**
```bash
monkeytype run -m pytest tests/
monkeytype apply enrichment_pipeline
```

Result: Accurate type annotations added automatically!

## Key Benefits of This Workflow

### For LLMs (Claude):
- **Structured thinking**: Each step has clear boundaries
- **Token efficient**: DOT is concise, avoids redundant code
- **Incremental**: Build one piece at a time
- **Validatable**: Check work at each step
- **Manageable complexity**: Never overwhelmed with full implementation

### For Developers:
- **Visual validation**: See the DAG before coding
- **Test coverage**: Every node is tested
- **Maintainable**: Well-structured, documented code
- **Debuggable**: Issues isolated to specific nodes
- **Refactorable**: Safe to modify with test coverage

## Advanced Patterns

### Using Function Modifiers

Add decorators in Step 2:

```python
from hamilton.function_modifiers import parameterize

@parameterize(
    rolling_7d={'window': 7},
    rolling_30d={'window': 30},
    rolling_90d={'window': 90},
)
def rolling_average(data: pd.Series, window: int) -> pd.Series:
    """Calculate rolling average.

    :param data: Input data
    :param window: Rolling window size
    :return: Rolling average
    """
    pass
```

Update DOT graph to reflect expanded nodes:
```dot
data -> rolling_7d
data -> rolling_30d
data -> rolling_90d
```

### Async Functions

Mark async in Step 2:

```python
async def api_data(endpoint: str, record_id: int) -> dict:
    """Fetch data from API.

    :param endpoint: API endpoint
    :param record_id: Record ID to fetch
    :return: API response data
    """
    pass
```

Use async driver:
```python
from hamilton import async_driver

dr = await async_driver.Builder().with_modules(module).build()
```

## Common Pitfalls

### ❌ Implementing before validating
Don't write function bodies before running `hamilton build`. Validate structure first!

### ❌ Writing all code at once
Don't implement all nodes before testing. Go node-by-node with TDD.

### ❌ Guessing type annotations
Don't waste time guessing types. Use `Any` + MonkeyType or disable validation initially.

### ❌ Skipping the DOT step
Don't jump straight to code. The DOT graph is your blueprint - validate it first.

### ❌ Not running tests
Don't implement the next node until the current one passes its test.

## Quick Reference

```bash
# Step 3: Validate DAG
hamilton build --module my_module
hamilton build --module my_module --output dag.png

# Step 4: Run tests
pytest tests/test_my_module.py::test_my_function -v

# Step 5: Infer types
monkeytype run -m pytest tests/
monkeytype apply my_module

# Full pipeline validation
python -c "from hamilton import driver; import my_module; driver.Driver({}, my_module)"
```

## Summary

1. **DOT first**: Design the graph in DOT language (token-efficient, validatable)
2. **Signatures next**: Convert to function signatures with docstrings
3. **Validate early**: Use `hamilton build` before implementing
4. **TDD everything**: Implement one node at a time with tests
5. **Types pragmatically**: Use Any + MonkeyType or disable validation initially

**This workflow enables Claude to build complex Hamilton DAGs systematically, avoiding the pitfalls of monolithic implementation.**

---

For additional patterns and examples, see the skill-specific guides:
- `/hamilton-core` - Core patterns and decorators
- `/hamilton-scale` - Async, Ray, Dask, Spark
- `/hamilton-llm` - LLM and RAG workflows
- `/hamilton-observability` - Monitoring and tracking
- `/hamilton-integrations` - Airflow, FastAPI, Streamlit