# Implementation Plan: GenCpp Dogfood Feedback Loop

## Phase 1: Verify GenCpp Project Access
Focus: Ensure Manual Slop can properly target and load the gencpp project

- [ ] Task 1.1: Investigate how `_allowed_paths` is populated in `mcp_client.py:configure()` - determine if gencpp can be added as a secondary project
- [ ] Task 1.2: If access is possible, verify `C:/projects/gencpp` is accessible as a project root in Manual Slop
- [ ] Task 1.3: Check that `.ai/manual_slop.toml` or `manual_slop.toml` is recognized at the gencpp root
- [ ] Task 1.4: Confirm conductor directory resolves to `.manual_slop/conductor` relative to gencpp root
- [ ] Task 1.N: Write tests for project path resolution targeting external directories

## Phase 2: Establish Feedback Capture Mechanism
Focus: Create a structured way to log issues found during dogfooding

- [ ] Task 2.1: Verify JOURNAL.md in the gencpp conductor directory is writable
- [ ] Task 2.2: Create a "Findings" section in the track state for capturing issues
- [ ] Task 2.3: Document the feedback workflow in track index.md
- [ ] Task 2.N: Write tests for findings capture mechanism

## Phase 3: Validate Feedback Loop
Focus: Confirm the feedback mechanism works end-to-end

- [ ] Task 3.1: User reports an issue encountered while using Manual Slop with gencpp
- [ ] Task 3.2: Issue is captured in a structured format (findings log)
- [ ] Task 3.3: Issue can be converted into a new track for Manual Slop improvement
- [ ] Task 3.N: User manual verification of the complete feedback loop