Sprint Retrospective

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 && \
python scripts/code_churn_analyzer.py --days 14 -f json > /tmp/ch.json && \
python scripts/retro_report_generator.py -v /tmp/v.json -c /tmp/c.json -u /tmp/ch.json -s "Sprint 23"

All tools support --format text|json, --days N, --since/--until YYYY-MM-DD, and --repo /path.

References

Load the reference that matches the task — keep this file lean and pull detail on demand:

• references/workflows-and-output.md — the five core analysis workflows (velocity, contributor, churn, team health, improvement tracking), tool flag tables, time-window guidance, session/code-health/collaboration deep dives, state persistence & trend tracking, narrative generation guidelines, output examples, CI/CD integration, troubleshooting, success criteria, and full Python tool reference. Read this for any hands-on retro analysis or report generation.
• references/retrospective_facilitation.md — 8 retro formats (Start/Stop/Continue, 4Ls, Sailboat, DAKI, etc.), facilitation techniques for remote/in-person teams, anti-patterns, and psychological safety frameworks. Read when facilitating the live ceremony.
• references/velocity_benchmarks.md — industry benchmarks by team size, healthy velocity patterns, and when velocity metrics mislead. Read when interpreting velocity numbers.
• references/red-flags.md — common ways this skill's output goes wrong, with fixes. Read before finalizing a retro report.

Scope & Limitations

In Scope: Git history analysis for velocity, contributor, and code churn metrics; session detection via commit-timestamp gaps; commit-type classification via conventional-commit prefixes; markdown report generation with executive summary, dashboards, and action-item tracking; sprint-over-sprint comparison; bus factor and knowledge-silo identification.

Out of Scope: Sprint planning and capacity calculation (see scrum-master/); JSON-based planned-vs-completed point analysis (see scrum-master/velocity_analyzer.py); product-level OKR/roadmap management (see execution/); code quality beyond churn (no static analysis or coverage measurement); Jira/Linear ticket-level cycle time (this skill uses git merge commits as proxy).

Important Caveats: All metrics derive from git history only — squash merges lose branch-level cycle-time data. Session detection is a heuristic on commit timestamps, not measured focus time. Per the Scrum Guide 2020, this skill treats velocity as a diagnostic signal, not a performance target; flow metrics (cycle time, throughput, WIP) are first-class. Rotate facilitation formats every 3-5 sprints to prevent staleness.

Integration Points