python3-add-feature

<feature_description>$ARGUMENTS</feature_description>

Python Feature Addition Workflow

The model guides feature development through discovery, planning, implementation, and verification phases.

Arguments

<feature_description/>

Instructions

1. Understand the feature request from arguments
2. Discover project context (structure, patterns, existing code)
3. Plan implementation (files to create/modify, dependencies)
4. Implement with tests following TDD
5. Verify quality (linting, types, coverage)

Phase 1: Discovery

Gather Project Context

CHECK:
- [ ] pyproject.toml exists and has project configuration
- [ ] src/ or packages/ directory structure
- [ ] tests/ directory with existing test patterns
- [ ] Linting and type-check configuration (ruff; ty and/or mypy per project — see python3-standards)
- [ ] Existing patterns for similar features

Identify Integration Points

Determine where the feature fits:

• Which module/package owns this functionality?
• What existing classes/functions will interact with it?
• What new files need to be created?

Phase 2: Planning

Feature Specification

Create a clear specification:

## Feature: [Name]

**Purpose**: [One sentence describing what this enables]

**User Story**: As a [user type], I want [capability] so that [benefit].

**Acceptance Criteria**:
- [ ] Criterion 1
- [ ] Criterion 2
- [ ] Criterion 3

**Files to Create/Modify**:
- `src/module/new_feature.py` - Main implementation
- `tests/test_new_feature.py` - Test suite
- `src/module/__init__.py` - Export new functionality

**Dependencies**:
- Internal: [existing modules to import]
- External: [new packages if any]

MoSCoW Prioritization

<moscow_framework>

Categorize all requirements using MoSCoW:

Discipline Check: If everything is P0, nothing is P0. Re-evaluate.

Example:

### Requirements by Priority

**P0 (Must Have)**:
- [ ] Parse CSV input with header detection
- [ ] Output formatted report to stdout
- [ ] Handle malformed rows with error message

**P1 (Should Have)**:
- [ ] Support custom delimiter (--delimiter)
- [ ] Progress indicator for large files

**P2 (Could Have)**:
- [ ] JSON output format option
- [ ] Column filtering

**Won't Have (This Release)**:
- Excel format support (separate feature)
- Database export (requires new dependency)

</moscow_framework>

Acceptance Criteria Formats

<acceptance_criteria_patterns>

Use ONE of these formats for testable acceptance criteria:

Format 1: Given/When/Then (BDD)

Given [precondition]
When [user action]
Then [expected outcome]

Example:

Given a CSV file with 1000 rows
When the user runs `parse report.csv`
Then the output shows all rows within 2 seconds
And no memory warnings are logged

Format 2: Checklist with Specifics

- [ ] Command `parse --help` shows usage with examples
- [ ] Empty file input returns exit code 1 with message "Empty file"
- [ ] Unicode characters in data are preserved in output
- [ ] Ctrl+C during processing exits cleanly (no stack trace)

Anti-Patterns to Avoid:

</acceptance_criteria_patterns>

Design Interface First

Define the public API before implementation:

# Define function signatures and docstrings
def new_feature(input_data: InputType, *, option: str = "default") -> ResultType:
    """Process input data with new feature capability.

    Args:
        input_data: The data to process
        option: Configuration option

    Returns:
        Processed result

    Raises:
        ValidationError: If input_data is invalid
    """
    ...

Phase 3: Test-Driven Implementation

Write Tests First

import pytest
from pytest_mock import MockerFixture


class TestNewFeature:
    """Tests for new_feature functionality."""

    def test_basic_operation(self) -> None:
        """Test basic feature operation with valid input."""
        # Arrange
        input_data = create_valid_input()

        # Act
        result = new_feature(input_data)

        # Assert
        assert result.status == "success"

    def test_handles_invalid_input(self) -> None:
        """Test feature raises error for invalid input."""
        with pytest.raises(ValidationError, match="Invalid input"):
            new_feature(invalid_input)

    def test_option_affects_behavior(self) -> None:
        """Test that option parameter changes processing."""
        result_default = new_feature(data, option="default")
        result_custom = new_feature(data, option="custom")

        assert result_default != result_custom

Implement to Pass Tests

1. Run tests - they should fail (red)
2. Implement minimal code to pass (green)
3. Refactor for quality (refactor)
4. Repeat for each test case

Implementation Checklist

- [ ] All functions have complete type hints
- [ ] Docstrings follow Google style
- [ ] Error handling uses specific exceptions
- [ ] No hardcoded values (use constants or config)
- [ ] Follows existing project patterns

Phase 4: Integration

Update Module Exports

# src/module/__init__.py
from .new_feature import new_feature, ResultType

__all__ = [
    "new_feature",
    "ResultType",
    # ... existing exports
]

Add to CLI (if applicable)

@app.command()
def feature_command(input_file: Annotated[Path, typer.Argument(help="Input file")]) -> None:
    """Run new feature on input file."""
    result = new_feature(load_data(input_file))
    console.print(f"Result: {result}")

Phase 5: Verification

Run Quality Checks

# Linting
uv run ruff check src/ tests/
uv run ruff format --check src/ tests/

# Type checking — match hooks/CI (ty vs mypy); do not use mypy only because [tool.mypy] exists
uv run ty check src/ tests/
# If hooks/CI run mypy:
# uv run mypy src/ tests/

# Tests with coverage
uv run pytest tests/ --cov=src --cov-report=term-missing

Coverage Requirements

• New feature code: 100% coverage
• Integration points: Covered by integration tests
• Overall project: Maintain or improve existing coverage

Success Metrics

<success_metrics_framework>

Define measurable success criteria before implementation:

Leading Indicators (Observable in days-weeks):

Lagging Indicators (Observable in weeks-months):

For CLI Features:

# Performance baseline
hyperfine 'uv run <command> <args>' --warmup 3

# Memory usage
/usr/bin/time -v uv run <command> <args> 2>&1 | grep "Maximum resident"

Evaluation Window: Specify when metrics will be reviewed (e.g., "1 week post-merge", "after 100 invocations").

</success_metrics_framework>

Documentation

Update relevant documentation:

• README.md if user-facing feature
• API docs if public interface
• CHANGELOG.md with feature description

Example Workflow

Request: "Add CSV export functionality to the report module"

Discovery

Project structure:
- src/reports/generator.py - existing report generation
- src/reports/formats/ - existing format handlers
- tests/reports/ - existing report tests

Pattern: Format handlers inherit from BaseFormatter

Planning

## Feature: CSV Export

**Purpose**: Export reports in CSV format

**Files**:
- `src/reports/formats/csv_formatter.py` - CSV implementation
- `tests/reports/formats/test_csv_formatter.py` - Tests

**Interface**:
class CsvFormatter(BaseFormatter):
    def format(self, report: Report) -> str: ...

Implementation

1. Write tests for CsvFormatter
2. Implement CsvFormatter class
3. Register in format factory
4. Add CLI option --format csv
5. Run verification checks

Quality Standards

Consult ../python3-development/references/python3-standards.md when verifying the feature against shared plugin standards. Ensure:

1. Type Safety: All code passes the project's type checker — match hooks/CI (ty vs mypy); use uv run ty check when ty is what the repo runs; use uv run mypy only when mypy is actually invoked there (not merely because [tool.mypy] exists)
2. Linting: Zero ruff errors or warnings
3. Tests: New code has 100% coverage
4. Patterns: Follows existing project conventions
5. Documentation: Docstrings on all public interfaces

References

• pytest Documentation
• pytest-mock
• Google Python Style Guide