# MMA Migration: Epics and Detailed Tasks

## Track 1: The Memory Foundations (AST Parser)

**Goal:** Build the engine that prevents token-bloat by turning massive source files into curated memory views.

### 1. TDD Approach for `tree-sitter` Integration
- Create `tests/test_file_cache_ast.py`.
- Define mock Python source files containing various structures (classes, functions, docstrings, `@core_logic` decorators, `# [HOT]` comments).
- Write failing tests that instantiate `ASTParser` and assert that `get_skeleton_view()` and `get_curated_view()` return the precisely filtered strings.
- **Red Phase:** Ensure tests fail because `ASTParser` does not exist.
- **Green Phase:** Implement the tree-sitter logic iteratively until strings match exactly.

### 2. `ASTParser` Extraction Rules (Tasks)
- **Task 1.1: Dependency Setup**
  - Add `tree-sitter` and `tree-sitter-python` to `pyproject.toml` / `requirements.txt`.
- **Task 1.2: Core Parser Class**
  - Create `ASTParser` in `file_cache.py` that initializes the language parser.
- **Task 1.3: Skeleton View Extraction**
  - Write query to extract `function_definition` and `class_definition`.
  - Keep signatures, parameters, and return type hints.
  - Replace all bodies with `pass`.
- **Task 1.4: Curated View Extraction**
  - Write query to keep class structures and `expression_statement` docstrings.
  - Implement heuristic to preserve full bodies of functions decorated with `@core_logic` or containing `# [HOT]` comments.
  - Replace all other function bodies with `... # Hidden`.

### 3. Acceptance Testing Criteria
- **Unit Tests:** All AST parsing tests pass with >90% coverage for `file_cache.py`.
- **Integration Test:** Execute the parser on a large, complex project file (e.g., `ai_client.py`). The output `Skeleton View` must be less than 15% of the original token count. The `Curated View` must correctly retain docstrings and marked functions while stripping standard bodies.
## Track 2: State Machine & Data Structures

**Goal:** Define the rigid Python objects (Pydantic/Dataclasses) that AI agents will pass to each other, enforcing structured data over loose chat strings.

### 1. TDD Approach for \models.py\
- Create \	ests/test_models.py\.
- Write failing tests that instantiate \Track\, \Ticket\, and \WorkerContext\ with various valid and invalid schemas.
- Write tests that assert state transitions (e.g., from \pending\ to \locked\, from \step_paused\ to \completed\) correctly update internal flags and dependencies.
- **Red Phase:** Tests fail because \models.py\ classes are undefined or lack transition methods.
- **Green Phase:** Implement the dataclasses and state mutators.

### 2. State Machine Tasks
- **Task 2.1: The Dataclasses**
  - Create \models.py\. Define \Ticket\ (id, target_file, prompt, worker_archetype, status, dependencies).
  - Define \Track\ (id, title, description, status, tickets).
- **Task 2.2: Worker Context Definition**
  - Define \WorkerContext\ holding a \Ticket\ ID, assigned model, configuration injection, and an ephemeral \messages\ array.
- **Task 2.3: State Mutator Methods**
  - Implement methods like \	icket.mark_blocked(dependency_id)\, \	icket.mark_complete()\, and \	rack.get_executable_tickets()\. Ensure strict validation of valid state transitions.

### 3. Acceptance Testing Criteria
- **Unit Tests:** \models.py\ has 100% test coverage for all state transitions.
- **Integration Test:** Instantiate a \Track\ with 3 dependent \Tickets\ in Python. Programmatically mark tickets as complete and assert that the subsequent dependent tickets transition from \locked\ to \pending\ without any AI involvement.