---
timestamp: 2025-09-15T17:55:40.671840
type: agent_engineer
metadata: {"agent_type": "engineer", "agent_id": "engineer_acdd2a61-4fd4-4cee-ab6e-dc4ccba95358", "session_id": "acdd2a61-4fd4-4cee-ab6e-dc4ccba95358", "delegation_context": {"description": "Fix line count calculation with exclusions", "timestamp": "2025-09-15T17:55:40.667934"}}
---


AGENT MEMORY - PROJECT-SPECIFIC KNOWLEDGE:
# Agent Memory: engineer
<!-- Last Updated: 2025-08-05 15:39:13 | Auto-updated by: engineer -->

<!-- MEMORY LIMITS: 8KB max | 10 sections max | 15 items per section -->

## Project Architecture (Max: 15 items)
- Service-oriented architecture with clear module boundaries
- Three-tier agent hierarchy: project → user → system
- Agent definitions use standardized JSON schema validation

## Coding Patterns Learned (Max: 15 items)
- Always use PathResolver for path operations, never hardcode paths
- SubprocessRunner utility for external command execution
- LoggerMixin provides consistent logging across all services

## Implementation Guidelines (Max: 15 items)
- Check docs/STRUCTURE.md before creating new files
- Follow existing import patterns: from claude_mpm.module import Class
- Use existing utilities instead of reimplementing functionality

## Domain-Specific Knowledge (Max: 15 items)
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## Effective Strategies (Max: 15 items)
<!-- Successful approaches discovered through experience -->

## Common Mistakes to Avoid (Max: 15 items)
- Don't modify Claude Code core functionality, only extend it
- Avoid duplicating code - check utils/ for existing implementations
- Never hardcode file paths, use PathResolver utilities

## Integration Points (Max: 15 items)
<!-- Key interfaces and integration patterns -->

## Performance Considerations (Max: 15 items)
<!-- Performance insights and optimization patterns -->

## Current Technical Context (Max: 15 items)
- EP-0001: Technical debt reduction in progress
- Target: 80% test coverage (current: 23.6%)
- Integration with Claude Code 1.0.60+ native agent framework

## Recent Learnings (Max: 15 items)
<!-- Most recent discoveries and insights -->


INSTRUCTIONS: Review your memory above before proceeding. Apply learned patterns and avoid known mistakes.


The GitFlow Analytics tool is calculating line counts from git commits without applying the path exclusions during the initial fetch. This results in inflated line counts that include boilerplate files like vendor.js, package-lock.json, etc.

The issue is in the data fetching phase - when commits are fetched and cached, the line counts (insertions/deletions) are calculated from git without checking against the exclude_paths patterns.

Please investigate and fix the following:

1. Find where line counts are calculated during the git data fetch (likely in data_fetcher.py or analyzer.py)
2. Ensure that exclude_paths patterns are applied DURING the line count calculation, not after
3. The _should_exclude_file method exists in analyzer.py but needs to be used during the initial fetch
4. Modify the code so that when calculating insertions/deletions for a commit, files matching exclude_paths patterns are skipped

Key files to check:
- src/gitflow_analytics/core/data_fetcher.py - where commits are fetched from git
- src/gitflow_analytics/core/analyzer.py - has _should_exclude_file method
- src/gitflow_analytics/core/cache.py - where commit data is cached

The goal is to have accurate line counts that exclude boilerplate files right from the initial calculation, not just during report generation.

Current problem: HOSANNA_UI shows 105,861 lines changed but this includes package-lock.json and test files. CNA_FRONTEND shows 131,144 lines but includes vendor.js with 41,836 deletions.

Expected result: Line counts should only include non-boilerplate code files.