hamilton-dev-workflow

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:

"""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:

# 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:

# 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)

# 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

from typing import Any

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

Option B: Disable type validation in Driver

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:

# 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

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

"""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

hamilton build --module enrichment_pipeline
# Output: ✓ DAG is valid, 4 nodes, 0 cycles

Step 4: TDD Implementation

Test 1:

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:

def raw_data(json_path: str) -> pd.DataFrame:
    return pd.read_json(json_path)

Test 2:

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:

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

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:

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:

data -> rolling_7d
data -> rolling_30d
data -> rolling_90d

Async Functions

Mark async in Step 2:

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:

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

# 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.

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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