---
timestamp: 2025-09-15T13:44:46.128661
type: agent_ops
metadata: {"agent_type": "ops", "agent_id": "ops_acdd2a61-4fd4-4cee-ab6e-dc4ccba95358", "session_id": "acdd2a61-4fd4-4cee-ab6e-dc4ccba95358", "delegation_context": {"description": "Complete package build and publication", "timestamp": "2025-09-15T13:44:46.127636"}}
---


AGENT MEMORY - PROJECT-SPECIFIC KNOWLEDGE:
# Agent Memory: ops
<!-- Last Updated: 2025-08-10 11:39:35 | Auto-updated by: ops -->

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

## Project Context
gitflow-analytics: python (with javascript) standard application
- Main modules: gitflow_analytics, gitflow_analytics/classification, gitflow_analytics/metrics, gitflow_analytics/identity_llm
- Testing: Python unittest pattern
- Key patterns: Unit Testing

## Project Architecture
- Standard Application with python implementation
- Main directories: src, tests, docs
- Core modules: gitflow_analytics, gitflow_analytics/classification, gitflow_analytics/metrics, gitflow_analytics/identity_llm

## Coding Patterns Learned
- Python project: use type hints, follow PEP 8 conventions
- Project uses: Unit Testing

## Implementation Guidelines
- Use pip for dependency management
- Follow python unittest pattern
- Follow tests in /tests/ directory
- Key config files: pyproject.toml

## Domain-Specific Knowledge
<!-- Agent-specific knowledge for gitflow-analytics domain -->
- Key project terms: metrics, models, classification, gitflow

## Effective Strategies
<!-- Successful approaches discovered through experience -->

## Common Mistakes to Avoid
- Avoid circular imports - use late imports when needed
- Don't ignore virtual environment - always activate before work
- Don't ignore database transactions in multi-step operations
- Avoid N+1 queries - use proper joins or prefetching

## Integration Points
- Sqlite database integration

## Performance Considerations
- Use list comprehensions over loops where appropriate
- Consider caching for expensive operations
- Index frequently queried columns
- Use connection pooling for database connections

## Current Technical Context
- Tech stack: python
- Data storage: sqlite
- Key dependencies: click>=8.1, gitpython>=3.1, pygithub>=2.0, tqdm>=4.65
- Documentation: README.md, CONTRIBUTING.md, CHANGELOG.md

## Recent Learnings
<!-- Most recent discoveries and insights -->


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


Complete the GitFlow Analytics package build and publication process.

Current situation:
- Version 1.3.9 has been created by semantic-release
- The CI pipeline keeps failing on formatting checks 
- We've fixed multiple formatting issues but more keep appearing
- Need to get the package successfully built and published to PyPI

Tasks:
1. Push the latest formatting fix (schema.py) to trigger CI
2. Monitor the CI workflow for successful completion
3. If it fails again, identify and fix any remaining issues
4. Ensure the package gets built and published to PyPI
5. Verify the new version is available on PyPI

Files that have been fixed:
- src/gitflow_analytics/core/data_fetcher.py (unused imports removed, black formatted)
- src/gitflow_analytics/cli.py (black formatted)
- src/gitflow_analytics/config/schema.py (black formatted)

The main improvements in this release:
- Fixed branch analysis to include all branches by default (was missing 61% of commits)
- Added day-by-day commit fetching with progress bars for better scalability
- Added incremental caching to prevent data loss on interruptions
- Improved progress visibility with nested progress bars

Please push the latest fix and ensure the package gets successfully published.