trustable-ai: Command for Claude Code

Sprint Completion Workflow

Project: trusted-ai-development-workbench Workflow: Sprint Completion Purpose: Close sprint, archive data, calculate metrics, and prepare for next sprint

Output Formatting Requirements

IMPORTANT: Use actual Unicode emojis in reports, NOT GitHub-style shortcodes.

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Workflow Overview

┌─────────────────────────────────────────────────────────────────────────────┐
│  SPRINT COMPLETION                                                          │
│                                                                             │
│  Step 1: Collect final sprint metrics                                      │
│  Step 2: /scrum-master → Analyze incomplete work (if any)                  │
│  Step 3: Process incomplete items (carry over / backlog)                   │
│  Step 4: Calculate performance metrics                                     │
│  Step 5: Generate completion report                                        │
│  Step 6: Archive sprint data                                               │
│  Step 7: Prepare next sprint                                               │
│                                                                             │
│  Agent command spawns a FRESH CONTEXT WINDOW via Task tool                 │
└─────────────────────────────────────────────────────────────────────────────┘

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Initialize Workflow

current_sprint = input("Sprint to close (e.g., Sprint 1): ")
next_sprint = input("Next sprint name (e.g., Sprint 2): ")

# Load all sprint work items
from pathlib import Path
import yaml

sprint_items = []
work_items_dir = Path(".claude/work-items")
for f in work_items_dir.glob("*.yaml"):
    with open(f) as file:
        item = yaml.safe_load(file)
        if item.get('sprint') == current_sprint:
            sprint_items.append(item)

print(f"📋 Found {len(sprint_items)} items in {current_sprint}")

---

Step 1: Collect Final Sprint Metrics

# Categorize items
completed_states = ['Done', 'Closed', 'Resolved', 'Completed']
completed = [i for i in sprint_items if i['status'] in completed_states]
incomplete = [i for i in sprint_items if i['status'] not in completed_states]

# Calculate metrics
total_points = sum(i.get('story_points', 0) for i in sprint_items)
completed_points = sum(i.get('story_points', 0) for i in completed)
completion_rate = (completed_points / total_points * 100) if total_points > 0 else 0

print(f"📊 Final Sprint Metrics:")
print(f"  Total: {len(sprint_items)} items ({total_points} pts)")
print(f"  ✅ Completed: {len(completed)} ({completed_points} pts)")
print(f"  ⏳ Incomplete: {len(incomplete)}")
print(f"  📈 Completion Rate: {completion_rate:.1f}%")
print(f"  🚀 Velocity: {completed_points} story points")