docs(mma): Draft Track 1 - The Memory Foundations
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# MMA Migration: Epics and Detailed Tasks
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## Track 1: The Memory Foundations (AST Parser)
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**Goal:** Build the engine that prevents token-bloat by turning massive source files into curated memory views.
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### 1. TDD Approach for `tree-sitter` Integration
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- Create `tests/test_file_cache_ast.py`.
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- Define mock Python source files containing various structures (classes, functions, docstrings, `@core_logic` decorators, `# [HOT]` comments).
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- Write failing tests that instantiate `ASTParser` and assert that `get_skeleton_view()` and `get_curated_view()` return the precisely filtered strings.
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- **Red Phase:** Ensure tests fail because `ASTParser` does not exist.
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- **Green Phase:** Implement the tree-sitter logic iteratively until strings match exactly.
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### 2. `ASTParser` Extraction Rules (Tasks)
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- **Task 1.1: Dependency Setup**
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- Add `tree-sitter` and `tree-sitter-python` to `pyproject.toml` / `requirements.txt`.
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- **Task 1.2: Core Parser Class**
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- Create `ASTParser` in `file_cache.py` that initializes the language parser.
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- **Task 1.3: Skeleton View Extraction**
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- Write query to extract `function_definition` and `class_definition`.
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- Keep signatures, parameters, and return type hints.
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- Replace all bodies with `pass`.
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- **Task 1.4: Curated View Extraction**
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- Write query to keep class structures and `expression_statement` docstrings.
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- Implement heuristic to preserve full bodies of functions decorated with `@core_logic` or containing `# [HOT]` comments.
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- Replace all other function bodies with `... # Hidden`.
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### 3. Acceptance Testing Criteria
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- **Unit Tests:** All AST parsing tests pass with >90% coverage for `file_cache.py`.
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- **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.
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