chore(conductor): Archive strategic overview and split into granular tracks

conductor/archive/code_path_analysis_20260507/spec.md

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+# Specification: Code Path & Data Pipeline Analysis (code_path_analysis_20260507)
+
+## Overview
+A deep architectural audit focused on mapping the "processing routes" and "data pipelines" of the Manual Slop codebase. This analysis will treat the program as a series of data-driven pipelines (similar to Ryan Fleury's model), identifying exactly how data flows through `./src` and `./simulation`.
+
+## Scope
+- **Core Codebase:** `./src`
+- **Simulation Infrastructure:** `./simulation`
+- **Granularity:** Both high-level module interactions and detailed function-to-function execution flows.
+
+## Functional Requirements
+1.  **Pipeline Mapping:**
+    - Identify major execution "routes" (e.g., UI Event Loop, AI Tool-Call Loop, Context Aggregation Pipeline).
+    - Map these routes from entry point to terminal state.
+2.  **Data Responsibility Audit:**
+    - For every major path, define which data structures it owns, modifies, or depends upon.
+    - Identify state boundaries and potential "data leaks" or redundant processing.
+3.  **Simulation Pipeline Audit:**
+    - Fully map the lifecycle of a simulation: State Setup -> Agent Injection -> Execution Loop -> Verification -> Cleanup.
+4.  **Automated Extraction:**
+    - Utilize MCP tools and potentially custom `tree-sitter` scripts to verify call graphs and data dependencies.
+
+## Acceptance Criteria
+- [ ] Comprehensive `PIPELINE_ANALYSIS.md` report created in the root.
+- [ ] Mermaid flowcharts documenting every major processing route.
+- [ ] Data responsibility table for all mapped paths.
+- [ ] Full mapping of the `./simulation` pipeline.