WIP almost done with track planning

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# Deep AST-Driven Context Pruning
**Track ID:** deep_ast_context_pruning_20260306
**Status:** Planned
**See Also:**
- [Spec](./spec.md)
- [Plan](./plan.md)

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{
"id": "deep_ast_context_pruning_20260306",
"name": "Deep AST-Driven Context Pruning",
"status": "planned",
"created_at": "2026-03-06T00:00:00Z",
"updated_at": "2026-03-06T00:00:00Z",
"type": "feature",
"priority": "medium"
}

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# Implementation Plan: Deep AST-Driven Context Pruning (RAG for Code) (deep_ast_context_pruning)
# Implementation Plan: Deep AST Context Pruning (deep_ast_context_pruning_20260306)
## Phase 1: Research & Design
- [ ] Task: Analyze existing backend implementation
- [ ] Task: Design GUI/UX approach
- [ ] Task: Conductor - User Manual Verification
## Phase 1: AST Integration
- [ ] Task: Initialize MMA Environment
- [ ] Task: Verify tree_sitter availability
- WHERE: requirements.txt, imports
- WHAT: Ensure tree_sitter installed and importable
- HOW: pip install tree_sitter
## Phase 2: Implementation
- [ ] Task: Implement feature
- [ ] Task: Write tests
- [ ] Task: Conductor - User Manual Verification
## Phase 2: Skeleton Generation
- [ ] Task: Implement AST parser
- WHERE: src/file_cache.py or new module
- WHAT: Parse Python AST using tree_sitter
- HOW: tree_sitter.Language + Parser
- SAFETY: Exception handling for parse errors
- [ ] Task: Implement skeleton generator
- WHERE: src/file_cache.py
- WHAT: Extract function signatures and docstrings
- HOW: Walk AST, collect Def nodes
- SAFETY: Handle large files gracefully
- [ ] Task: Integrate with worker dispatch
- WHERE: src/multi_agent_conductor.py
- WHAT: Inject skeleton into worker prompt
- HOW: Modify context generation
- SAFETY: Verify tokens reduced
## Phase 3: Verification
- [ ] Task: Run test suite
- [ ] Task: Verify coverage
- [ ] Task: Conductor - Phase Completion Verification
## Phase 3: Tests & Verification
- [ ] Task: Write AST parsing tests
- [ ] Task: Verify token reduction
- [ ] Task: Conductor - Phase Verification

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# Track Specification: Deep AST-Driven Context Pruning
# Track Specification: Deep AST-Driven Context Pruning (deep_ast_context_pruning_20260306)
## Overview
Use tree_sitter to parse target file AST and inject condensed skeletons into worker prompts.
Use tree_sitter to parse target file AST and inject condensed skeletons into worker prompts. Currently workers receive full file context; this track reduces token burn by injecting only relevant function/method signatures.
## Current State Audit
### Already Implemented
- file_cache.py with skeleton generation
- outline_tool.py for code outlines
- AST-based file analysis
### Gaps to Fill
- No automatic AST pruning based on edit target
- Skeletons not customizable by user
## Goals
- Parse target file AST before worker dispatch
- Strip unrelated function bodies
- Inject surgical skeleton to reduce tokens
## Architectural Constraints
- **Parsing Performance**: AST parsing MUST complete in <100ms per file.
- **Caching**: Parsed AST trees SHOULD be cached to avoid re-parsing.
- **Selective Exposure**: Only target functions/classes related to the ticket's target_file should be included.
## Functional Requirements
- tree_sitter integration
- Target function/method detection
- Configurable skeleton depth
- **AST Parser**: Integrate tree_sitter to parse Python files.
- **Target Detection**: Identify which functions/methods to include based on ticket target_file.
- **Skeleton Generation**: Generate condensed skeletons with signatures and docstrings only.
- **Worker Integration**: Inject skeleton into worker prompt instead of full file content.
## Non-Functional Requirements
- <100ms parsing time per file
- >50% token reduction target
## Acceptance Criteria
- [ ] tree_sitter successfully parses Python AST.
- [ ] Skeleton includes only target function/class with signature and docstring.
- [ ] Token count reduced by >50% for typical worker prompts.
- [ ] Workers can still complete tickets with skeleton-only context.
- [ ] >80% test coverage.