This document defines a technical implementation plan for applying Deep Coding—a specification-driven, human–AI collaborative development methodology—to Python software projects. The plan translates the core principles of Deep Coding into concrete Python constructs, toolchains, and phase-based workflows.
The implementation rests on four technical pillars:
All execution steps, evaluation metrics, and verification gates are defined in Python-native terms, avoiding philosophical framing in favor of actionable technical procedures.
The specification is expressed using Python’s static and runtime type systems:
typing.Protocol to define expected behavior without requiring inheritance. Protocols enforce structural subtyping at static check time.pydantic.BaseModel for all data structures. Models provide runtime validation, JSON serialization, and schema generation.__init__.py files with explicit __all__ lists. All cross-module dependencies must reference only these public interfaces.@field_validator, @model_validator) or as abstract methods in protocol definitions.Example specification fragment :
from typing import Protocol
from pydantic import BaseModel
class EntitySpec(Protocol):
def update(self, delta_time: float) -> None: ...
def render(self, surface) -> None: ...
class GameState(BaseModel):
score: int
lives: int
invincible_frames: int = 0
Skeleton (Manually Maintained, AI-Inaccessible)
abc.ABC that implement template method patterns.skeleton/) with write permissions restricted to human developers.Tissue (AI-Generated, Regenerable)
The skeleton guarantees architectural invariants: because the skeleton owns the execution flow (e.g., a run method that calls abstract methods), any AI-generated tissue code is constrained by the skeleton’s calling order and error boundaries.
For each module or feature, the generation process follows:
mypy --strict to verify type conformance.Any failure at steps 3 or 4 triggers a regeneration attempt. The system does not proceed to the next phase until verification passes.
After each development phase, a structured summary is committed as a fixed premise. The summary includes:
This summary is stored in a version-controlled file (e.g., FIXED_PREMISE.md or as a structured YAML file). Subsequent phases treat this summary as immutable. Changes to the specification must be explicitly planned and approved before the next phase.
Development proceeds in phases, each consisting of the following steps:
mypy --strict passes with zero errors.pytest passes all existing tests plus new tests for added functionality.Phase 1: Core Engine Skeleton
GameLoopSkeleton as an abstract base class with template methods setup, handle_event, update, render, cleanup.Phase 2: Player and Input Handling
Player protocol with move, shoot methods. Define InputState pydantic model.Player implementation inheriting from skeleton.mypy --strict confirms Player conforms to protocol. pytest validates input mapping.Phase 3: Enemies and Collision
Enemy protocol and CollisionDetector class with defined interface.Phase 4: Data-Driven Stage System
Stage pydantic model with fields for enemy spawn patterns, background properties, etc.Stage model; generates stage loader.Phase 5: UI and Editor Integration
StageEditor protocol and serialization interface (JSON export/import).Phase 6: Optimization and Polish
All generated code must pass the following automated checks before phase completion:
| Gate | Tool | Command | Pass Condition |
|---|---|---|---|
| Type Safety | mypy | mypy --strict --no-implicit-optional . |
0 errors |
| Linting | ruff | ruff check . |
0 errors, 0 warnings |
| Formatting | black | black --check . |
No formatting changes required |
| Unit Tests | pytest | pytest --cov=. --cov-fail-under=80 |
100% pass rate, coverage >= 80% |
| Docstring Coverage | pydocstyle | pydocstyle . |
0 violations |
| Complexity | radon | radon cc -a -nb . |
Average cyclomatic complexity < 10 |
These gates are implemented as pre-commit hooks and CI pipeline checks, ensuring that any generated code that fails verification is rejected before integration.
The AI (LLM) is invoked through a structured prompt that provides:
The AI’s output is restricted to:
The AI does not have permission to modify:
This interface is implemented as a function call or API endpoint that wraps the LLM with these constraints.
The success of the implementation is measured by:
| Metric | Target | Measurement Method |
|---|---|---|
| Skeleton Modification Count | 0 per phase | Git diff on skeleton/ directory |
| Type Check Pass Rate | 100% | mypy exit code |
| Test Pass Rate | 100% | pytest exit code |
| Regeneration Iterations | <= 3 per phase | Count of generation attempts before gate pass |
| Specification Change Lead Time | < 15 minutes | Time from spec change to passing gates |
| Code Churn | < 5% per feature | Lines changed / lines added across phases |
| Human Intervention Ratio | < 10% of phases | Phases requiring human code edits / total phases |
These metrics are collected automatically via CI logs and version control history.
pydantic >= 2.0mypy >= 1.0ruff >= 0.1black >= 23.0pytest >= 7.0 with pytest-covradon >= 6.0pre-commit for gate automationOptional for advanced metrics:
networkx + custom curvature scripts for dependency graph analysisscikit-learn for embedding-based semantic density measurementThis implementation plan assumes:
When these conditions are not met:
This implementation plan provides a concrete, toolchain-integrated pathway for adopting Deep Coding in Python projects. By encoding architectural constraints in Python’s native type system, enforcing generative conformance through automated verification gates, and maintaining fixed premises across recursive refinement phases, the plan translates theoretical principles into executable development workflows.
The result is a development process where:
This is not a philosophical proposal but a technically executable methodology grounded in existing Python tools and established software engineering practices. ```