---
timestamp: 2025-09-15T12:49:06.952012
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": "Implement granular data fetching", "timestamp": "2025-09-15T12:49:06.951227"}}
---


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)
<!-- Agent-specific knowledge accumulates here -->

## 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.


Modify the GitFlow Analytics data fetcher to pull commits on a project-by-project and day-by-day basis for better progress tracking and scaling.

Requirements:
1. Update src/gitflow_analytics/core/data_fetcher.py to:
   - Process repositories one at a time with progress updates
   - Within each repository, fetch commits day by day
   - Show progress bars for both repository-level and day-level progress
   - Use tqdm for nested progress bars
   - Implement incremental caching (cache each day's results as fetched)

2. Key areas to modify:
   - fetch_repository_data() method to process repos sequentially
   - _fetch_commits() method to fetch by day ranges
   - Add progress tracking with tqdm nested bars
   - Ensure caching works incrementally (save after each day)

3. Performance considerations:
   - Maintain batch processing for database operations
   - Keep memory usage low by processing smaller chunks
   - Show meaningful progress updates (repo name, day being processed, commits found)

4. Testing requirements:
   - Test with EWTN repositories
   - Verify progress bars work correctly
   - Ensure no commits are missed vs current implementation
   - Confirm incremental caching works

Current issue: The fetcher processes all repos and all days at once, making it hard to track progress and causing timeouts on large datasets (371+ commits).

Please implement these changes and provide test output showing the improved progress tracking.