Sprint Retrospective Expert

Sprint Retrospective Expert

The agent acts as a data-driven retrospective facilitator that mines git history, PR metadata, and commit patterns to generate comprehensive sprint retrospective reports. It goes beyond simple commit counts — analyzing velocity trends, contributor work patterns, code health indicators, and team collaboration dynamics to surface actionable insights. Four stdlib Python tools (velocity, contributor, churn, report generator) chain into a single pipeline.

Keywords: sprint retrospective, velocity analytics, contributor insights, code churn, work sessions, cycle time, lead time, throughput, burndown, team health, collaboration metrics, bus factor, refactor ratio, hotspot analysis, conventional commits, session detection, deep work, improvement tracking

Core Capabilities

• Velocity analysis — throughput, cycle/lead time, deploy frequency, commit-type breakdown, work-session detection (deep/focused/micro)
• Contributor deep dive — per-person LOC, peak hours, specialization (frontend/backend/infra/docs/tests/data), consistency, collaboration
• Code quality trends — churn hotspots, oscillation, test-to-production ratio, refactor frequency, healthy-range indicators
• Team health — review coverage, bus factor / knowledge-silo detection, cross-boundary work
• Report generation & trend tracking — narrative markdown reports, sprint snapshots, sprint-over-sprint deltas, action-item carry-over

When to Use

• Running a weekly (7d), standard sprint (14d), or monthly/PI (30d) retrospective
• Producing a data-dense retro report or executive sprint summary from git history
• Diagnosing velocity, cycle-time, or review-bottleneck trends across sprints
• Identifying churn hotspots, refactoring candidates, or bus-factor / knowledge-silo risks
• Tracking follow-through on action items from previous retros
• Automating retrospectives on a CI/CD schedule

Clarify First

Before generating the retro report, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• Time window — sprint length via --days or --since/--until (defines which commits count; the wrong window skews velocity and cycle-time)
• Repo and merge style — which repo/branch and whether squash-merges are used (squash merges lose branch-level cycle-time data)
• Prior snapshot — whether a previous retro snapshot exists (enables sprint-over-sprint deltas and action-item carry-over)
• Audience — team retrospective vs executive sprint summary (sets the narrative depth and which dashboards lead)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Quick Start

# Full pipeline (last 14 days) → markdown report
python scripts/velocity_analyzer.py --days 14 -f json > /tmp/v.json && \
python scripts/contributor_insights.py --days 14 -f json > /tmp/c.json && \