Welcome! This guide helps engineering managers and team leads understand and use GitFlow Analytics to gain insights into team productivity and development patterns.
GitFlow Analytics analyzes your team's Git repositories to provide actionable productivity insights without requiring JIRA, Linear, or other project management tools. It gives you visibility into what your team is building, how work is distributed, and where improvements can be made.
Key Benefits:
- 📊 Understand team productivity and work patterns
- 🎯 Track process adherence (ticket coverage)
- ⚖️ Monitor work distribution and team balance
- 🔍 Identify untracked work and process gaps
- 📈 Analyze trends over time
- Quick Start Guide - Get your first insights in 5 minutes
- FAQ - Common questions answered
- Report Interpretation Guide - How to read GitFlow Analytics reports
- Metrics Reference - Plain-language metric definitions and benchmarks
- Dashboard Guide - Create Excel/Google Sheets dashboards
When GitFlow Analytics runs, you get:
A readable markdown file with:
- Key metrics snapshot (commits, developers, ticket coverage)
- Team composition and developer profiles
- Project activity breakdown
- Development patterns analysis
- Actionable recommendations
Reading time: 5-10 minutes
Spreadsheet-ready files for dashboards:
- Weekly metrics with productivity trends
- Developer focus and work distribution
- Activity distribution by developer/project
- Summary statistics and benchmarks
Use for: Executive dashboards, quarterly reviews
Week-by-week patterns showing:
- Classification trends (features vs bugs vs maintenance)
- Velocity changes
- Process adherence shifts
- Team health indicators
Use for: Sprint retrospectives, monthly reviews
This documentation is for:
- Engineering Managers - Team productivity and health monitoring
- Team Leads - Sprint planning and process improvement
- Directors/VPs - Cross-team comparisons and organizational trends
- Product Managers - Understanding engineering capacity and velocity
| Metric | What It Shows | Healthy Range |
|---|---|---|
| Ticket Coverage | % of commits linked to work items | 60-80% |
| Work Distribution | Team balance (Gini coefficient) | < 0.3 |
| Classification Mix | Features vs bugs vs maintenance | Varies by team |
| Activity Score | Developer productivity percentile | Context-dependent |
| Velocity Trend | Week-over-week commit patterns | Stable or growing |
See Metrics Reference for detailed definitions.
Review ticket coverage, untracked work, and velocity trends to improve sprint planning.
Read: Quick Start Guide → Report Interpretation
Analyze work distribution, developer focus scores, and activity patterns to ensure team balance.
Read: Metrics Reference → Dashboard Guide
Compare classification trends (feature vs bug ratios) and identify tech debt patterns.
Read: Report Interpretation → Dashboard Guide
Track ticket coverage improvements and commit quality metrics over time.
Read: Quick Start Guide → FAQ
No Setup Required for Managers: Your technical team runs GitFlow Analytics, and you receive reports. You don't need to install anything or understand Python/Git commands.
The Process:
- Your team runs analysis (5-minute command)
- Reports are generated in
./reports/directory - You review the narrative report and CSVs
- Import CSVs to Excel/Sheets for dashboards (optional)
See Quick Start Guide for the delegation workflow.
- Report interpretation questions: See Interpreting Reports
- Metric definitions: See Metrics Reference
- Common issues: See FAQ
- Technical setup: Have your team reference Getting Started
- Dashboard creation: See Dashboard Guide
For technical details and setup:
- User Guide - Installation and configuration
- CLI Reference - Command-line options
- Developer Guide - Contributing to GitFlow Analytics
Next Steps:
- Read the Quick Start Guide (5 minutes)
- Review the Report Interpretation Guide (10 minutes)
- Create your first dashboard using the Dashboard Guide
Questions? See the FAQ or ask your technical lead to reference the User Guide.