AI Sprint

Sprint

Purpose

Sprint lifecycle management: plan new sprints from backlog, run data-driven retrospectives comparing planned vs shipped, and track sprint-level goals. Bridges the gap between spec-level planning and day-to-day delivery.

Trigger

• Command: /ai-sprint plan|retro|goals
• Context: sprint boundary (start or end of sprint), goal tracking mid-sprint.

Pre-conditions (MANDATORY)

1. Read .ai-engineering/manifest.yml — work_items section.
2. Determine active provider (github or azure_devops).
3. Use provider-specific config:
   • Azure DevOps: filter by area_path, auto-detect current iteration_path
   • GitHub: filter by team_label, use milestones for sprint boundaries
4. Use all standard and custom fields the platform provides.

Modes

plan -- New sprint planning

1. Review backlog -- read open specs, GitHub Issues/Projects, and triaged items from /ai-triage.
2. Assess capacity -- count working days in sprint, factor in known absences or blockers from decision-store.
3. Select items -- pull highest-priority items that fit capacity. Apply RICE scores from triage.
4. Estimate effort -- use size labels (XS/S/M/L/XL) from issue standard. Flag items missing size estimates.
5. Draft sprint board -- output planned items grouped by priority:

## Sprint: {name} ({start} - {end})

### Goals
1. {Goal 1 -- measurable outcome}
2. {Goal 2 -- measurable outcome}

### Planned Items
| # | Priority | Size | Item | Spec |
|---|----------|------|------|------|
| 1 | p1 | M | Fix hook installation on Windows | spec-054 |
| 2 | p2 | L | Add telemetry dashboard | spec-054 |

6. Store -- save sprint plan to .ai-engineering/sprints/{name}.md.

retro -- Sprint retrospective

1. Load sprint plan -- read .ai-engineering/sprints/{name}.md.
2. Collect actuals -- scan merged PRs, completed spec tasks, and commit history for the sprint period.
3. Compare planned vs shipped:
   • Items completed as planned
   • Items carried over (not finished)
   • Side quests (unplanned work that entered the sprint)
   • Items descoped or deprioritized
4. Analyze patterns:
   • Estimation accuracy: actual effort vs estimated size
   • Side quest ratio: unplanned / total items delivered
   • Velocity trend: items completed vs previous sprints
5. Document learnings -- what went well, what to change, action items.
6. Output -- retrospective report appended to .ai-engineering/sprints/{name}.md.

goals -- Sprint goal tracking

1. Load active sprint -- find current sprint from .ai-engineering/sprints/.
2. Check goal progress -- for each goal, assess completion signals (merged PRs, closed issues, spec task status).
3. Report -- traffic-light status per goal: green (on track), yellow (at risk), red (blocked/behind).

Arguments

Quick Reference

/ai-sprint plan --sprint 2026-w12     # plan sprint for week 12
/ai-sprint retro --sprint 2026-w11    # retro on last sprint
/ai-sprint goals                      # check current sprint goals

Storage

• Sprint files: .ai-engineering/sprints/{name}.md
• Naming convention: YYYY-wNN (ISO week) or custom names

$ARGUMENTS