AI Sprint

Sprint

Sprint lifecycle: plan from backlog, run data-driven retros (planned vs shipped), track goals, generate review decks. Bridges spec-level planning and day-to-day delivery, storing each sprint at .ai-engineering/sprints/{name}.md (naming YYYY-wNN ISO week, or custom).

/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
/ai-sprint review --sprint 2026-03         # generate March 2026 review deck
/ai-sprint review --iteration "Sprint 12"  # named iteration review deck

Pre-conditions (MANDATORY)

1. Read manifest.yml work_items section; determine active provider (github | azure_devops).
2. Read .ai-engineering/reference/gather-activity-data.md for canonical git log, PR query, and work-item commands.
3. Provider config: Azure DevOps filters by area_path, auto-detects current iteration_path; GitHub filters by team_label, uses milestones for sprint boundaries.
4. Use all standard and custom fields the platform provides.

Workflow

Principles: §10.4 DRY (single gather-activity-data.md source for all activity queries); §10.1 KISS (each mode is one linear pass).

plan — new sprint planning

1. Review backlog — open specs, GitHub Issues/Projects, prioritized items (priority labels or manual ranking).
2. Assess capacity — count working days; factor known absences/blockers from decision-store.
3. Select items — highest-priority items that fit capacity; apply RICE scores.
4. Estimate effort — size labels (XS/S/M/L/XL); flag items missing estimates.
5. Draft board — planned items grouped by priority:

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

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

### Planned Items
| # | Priority | Size | Item | Spec |
|---|----------|------|------|------|
| 1 | p1 | M | Fix hook installation on Windows | spec-054 |

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

retro — sprint retrospective

1. Load plan — read .ai-engineering/sprints/{name}.md.
2. Collect actuals — via gather-activity-data.md commands: merged PRs, completed spec tasks, commit history for the period.
3. Compare planned vs shipped — completed / carried-over / side quests (unplanned) / descoped.
4. Analyze patterns — estimation accuracy (actual vs size), side-quest ratio (unplanned/total), velocity trend vs prior sprints.
5. Document learnings — what went well, what to change, action items.
6. Output — append retro section to the sprint file.

goals — goal tracking

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

review — review presentation

Branded PowerPoint via python-pptx. NEW script each invocation tailored to current data — never reuse a static template.

1. Determine period — --sprint YYYY-MM (calendar month), --iteration <name> (query provider for dates), or default to current month.
2. Gather data — gather-activity-data.md commands for work items + git activity; quality metrics via pytest --co -q and ruff check . --statistics; compare against manifest.yml thresholds.
3. Generate script — brand constants AI_BG_DARK=#0B1120, AI_ACCENT=#00D4AA, AI_PRIMARY=#1E3A5F; typography JetBrains Mono (headings), Inter (body); layout 16:9, 13.333"×7.5".
4. Slide structure (8-14) — Title → Sprint Overview (KPI cards) → Feature Deep-Dives (one per major spec) → Quality Metrics → Risks & Next Sprint → Q&A. Every slide requires set_notes().
5. Execute — write .ai-engineering/runtime/presentations/generate_sprint_review.py, run it, output sprint-review-YYYY-MM.pptx alongside.

Review common mistakes: reusing an old script verbatim, missing speaker notes, wrong palette, skipping pre-conditions, hardcoding dates.

Examples

User: "lets do the retro for the sprint that just ended"

/ai-sprint retro --sprint 2026-w18

Compares planned vs shipped, surfaces velocity trends, identifies blockers, writes the retro section into .ai-engineering/sprints/2026-w18.md.

Integration

Called by: user directly. Calls: gh project item-list, az boards query, /ai-slides (for review mode). See also: /ai-standup (daily slice), /ai-prose content sprint-review, /ai-board discover.

$ARGUMENTS