jira-retro

Jira Retrospective Skill

This skill provides patterns, templates, and best practices for generating insightful retrospective reports from Jira project data.

Overview

Retrospectives help teams reflect on completed work, identify patterns, and continuously improve. This skill focuses on data-driven retrospectives that analyze:

• Completed tickets and delivery metrics
• Time tracking and estimation accuracy
• Blockers and impediments
• Team productivity and patterns

Report Template

Full Retrospective Report

# {Sprint/Project} Retrospective
**Period:** {start_date} to {end_date}
**Project:** {project_name} ({project_key})
**Generated:** {current_date}

---

## Executive Summary

{Brief overview - 2-3 paragraphs summarizing:
 - What was accomplished
 - Key metrics and trends
 - Major wins and challenges}

### Key Metrics
- **Tickets Completed:** {count}
- **Story Points Delivered:** {points} (if tracked)
- **Total Time Logged:** {hours}h
- **Average Resolution Time:** {days} days
- **Blocked Issues:** {count} ({percentage}%)
- **Estimation Accuracy:** {percentage}% within ±20%

### Highlights
- ✅ {Major achievement or success}
- ✅ {Positive trend or improvement}
- ⚠️ {Challenge or area needing attention}

---

## Completed Tickets

### Summary Table
| Ticket | Summary | Type | Assignee | Status | Time Logged | Resolved |
|--------|---------|------|----------|--------|-------------|----------|
| {KEY-1} | {title} | {type} | {name} | {status} | {hours}h | {date} |
| {KEY-2} | {title} | {type} | {name} | {status} | {hours}h | {date} |

**Total Completed:** {count} issues

### Distribution by Type
- **Stories:** {count} ({percentage}%)
- **Bugs:** {count} ({percentage}%)
- **Tasks:** {count} ({percentage}%)
- **Other:** {count} ({percentage}%)

### Distribution by Priority
- **Critical/High:** {count} ({percentage}%)
- **Medium:** {count} ({percentage}%)
- **Low:** {count} ({percentage}%)

---

## Time Analysis

### Time Logged vs Estimated

| Ticket | Summary | Original Estimate | Actual Time | Variance | % Diff | Assignee |
|--------|---------|-------------------|-------------|----------|--------|----------|
| {KEY-1} | {title} | {hours}h | {hours}h | {±hours}h | {±%}% | {name} |
| {KEY-2} | {title} | {hours}h | {hours}h | {±hours}h | {±%}% | {name} |

**Totals:**
- **Original Estimates:** {total}h
- **Actual Time Logged:** {total}h
- **Total Variance:** {±hours}h ({±percentage}%)

### Time by Team Member

| Team Member | Issues Completed | Time Logged | Avg per Issue | % of Total |
|-------------|------------------|-------------|---------------|------------|
| {Name 1} | {count} | {hours}h | {avg}h | {%}% |
| {Name 2} | {count} | {hours}h | {avg}h | {%}% |

### Estimation Accuracy

**Categories:**
- ✅ **Accurate (±20%):** {count} tickets ({percentage}%)
- ⬆️ **Over-estimated (>20%):** {count} tickets ({percentage}%)
- ⬇️ **Under-estimated (>20%):** {count} tickets ({percentage}%)
- ❓ **No estimate:** {count} tickets ({percentage}%)

**Analysis:**
{Insights about estimation patterns:
 - Which types of work are consistently over/under-estimated?
 - Are certain team members more/less accurate?
 - What factors might explain large variances?}

---

## Blocker Analysis

### Blocked Issues Detail

| Ticket | Summary | Blocked By | Date Blocked | Date Unblocked | Days Blocked | Impact |
|--------|---------|------------|--------------|----------------|--------------|--------|
| {KEY-1} | {title} | {reason} | {date} | {date} | {count} | {impact} |
| {KEY-2} | {title} | {reason} | {date} | {date} | {count} | {impact} |

**Summary Statistics:**
- **Total Blocked Issues:** {count} ({percentage}% of all issues)
- **Average Block Duration:** {days} days
- **Longest Block:** {days} days ({ticket_key})
- **Total Days Lost to Blockers:** {count} days

### Common Blocker Categories

1. **{Category 1}** - {count} issues
   - Example: {description}
   - Impact: {impact_description}

2. **{Category 2}** - {count} issues
   - Example: {description}
   - Impact: {impact_description}

3. **{Category 3}** - {count} issues
   - Example: {description}
   - Impact: {impact_description}

**Insights:**
{Analysis of blocking patterns:
 - What are the most common causes of blockers?
 - Are blockers increasing/decreasing over time?
 - Which types of issues get blocked most often?
 - Recommendations for reducing blockers}

---

## Retrospective Insights

### 🌟 What Went Well

**Wins and Successes:**
- {Specific achievement with data/metric}
- {Positive pattern observed}
- {Team strength or improvement}

**Why it worked:**
{Analysis of what contributed to success}

### 🔧 What Could Be Improved

**Challenges and Opportunities:**
- {Challenge 1 with specific example}
  - **Impact:** {how this affected the team/project}
  - **Suggestion:** {specific improvement idea}

- {Challenge 2 with specific example}
  - **Impact:** {how this affected the team/project}
  - **Suggestion:** {specific improvement idea}

- {Challenge 3 with specific example}
  - **Impact:** {how this affected the team/project}
  - **Suggestion:** {specific improvement idea}

### 📋 Action Items

**Immediate Actions (Next Sprint):**
- [ ] {Specific, measurable action item 1}
  - Owner: {name}
  - Target: {date/sprint}

- [ ] {Specific, measurable action item 2}
  - Owner: {name}
  - Target: {date/sprint}

**Long-term Improvements:**
- [ ] {Strategic improvement 1}
  - Owner: {name}
  - Timeline: {timeframe}

### 💡 Team Observations

**Velocity and Throughput:**
{Analysis of team capacity and flow}

**Collaboration Patterns:**
{Observations about teamwork, dependencies, knowledge sharing}

**Process Effectiveness:**
{Insights about workflow, ceremonies, tools}

---

## Appendix

### Methodology

**Data Collection:**
- **Source:** Jira {instance_url}
- **Project:** {project_key}
- **Query Period:** {start_date} to {end_date}
- **Completion Criteria:** Issues with status = "Done" and resolutiondate in range
- **Worklog Filter:** Logs with started date in range

**Metrics Calculated:**
- **Time Logged:** Sum of all worklog timeSpent in period
- **Estimation Variance:** (Actual - Estimate) / Estimate × 100
- **Block Duration:** Days between blocked status change and unblocked status change
- **Resolution Time:** Days from created to resolutiondate

### Data Quality Notes

{Any caveats or limitations:
 - Missing time tracking data
 - Incomplete estimates
 - Status workflow variations
 - Manual adjustments made}

### Raw Data

**JQL Queries Used:**

{List the actual JQL queries used to fetch data}

**Issues Analyzed:** {count}
**Worklogs Processed:** {count}
**Date Range Verified:** {confirmation}

---

*Generated by Claude Code with aai-pm-jira plugin*

---

Metrics Guide

Essential Metrics

Metric	Calculation	Purpose
Tickets Completed	Count of issues with status = Done in period	Throughput
Total Time Logged	Sum of worklog timeSpent in period	Effort tracking
Avg Resolution Time	Average of (resolutiondate - created)	Cycle time
Estimation Variance	(Actual - Estimate) / Estimate	Estimation accuracy
Blocked Issues %	(Blocked issues / Total issues) × 100	Flow efficiency

Advanced Metrics

Metric	Calculation	Purpose
Lead Time	Time from created to done	Full delivery time
Cycle Time	Time from in progress to done	Active work time
Throughput	Issues completed per time period	Team capacity
WIP	Issues in progress at period end	Work in progress
Flow Efficiency	(Active time / Total time) × 100	Process efficiency

---

JQL Patterns for Retrospectives

Basic Queries

# All completed issues in date range (by project)
project = PROJ AND status = Done AND resolutiondate >= "2024-01-01" AND resolutiondate <= "2024-01-31"

# All completed issues in date range (by filter ID)
filter = 12345 AND status = Done AND resolutiondate >= "2024-01-01" AND resolutiondate <= "2024-01-31"

# All completed issues in date range (by filter name)
filter = "My Sprint Issues" AND status = Done AND resolutiondate >= "2024-01-01" AND resolutiondate <= "2024-01-31"

# Issues by sprint
project = PROJ AND sprint = "Sprint 10"

# Issues by assignee
project = PROJ AND assignee = "john.doe" AND status = Done AND resolutiondate >= "2024-01-01"

Advanced Queries

# Issues that were blocked (by project)
project = PROJ AND status changed to "Blocked" during ("2024-01-01", "2024-01-31")

# Issues that were blocked (by filter)
filter = 12345 AND status changed to "Blocked" during ("2024-01-01", "2024-01-31")

# Issues with time logged (by project)
project = PROJ AND timespent > 0 AND updated >= "2024-01-01"

# Issues with time logged (by filter)
filter = 12345 AND timespent > 0 AND updated >= "2024-01-01"

# Issues by type and priority
project = PROJ AND type = Bug AND priority in (High, Critical) AND resolutiondate >= "2024-01-01"

# Issues with estimation variance
project = PROJ AND originalEstimate is not EMPTY AND timespent > 0 AND status = Done

# Combining filter with additional criteria
filter = "Team Backlog" AND assignee = currentUser() AND status = Done AND resolutiondate >= startOfWeek()

---

Best Practices

DO

✅ Focus on data-driven insights

• Use actual metrics, not impressions
• Compare to previous periods when possible
• Identify trends over single data points

✅ Be balanced

• Celebrate wins alongside challenges
• Frame problems as opportunities
• Acknowledge context and external factors

✅ Make it actionable

• Specific, measurable action items
• Assign owners and deadlines
• Follow up on previous actions

✅ Include the team

• Validate findings with team members
• Incorporate qualitative feedback
• Make it collaborative, not top-down

DON'T

❌ Avoid blame or finger-pointing

• Focus on process, not people
• Frame as system issues, not individual failures
• Create psychological safety

❌ Don't overwhelm with data

• Highlight key metrics, not everything
• Use visualizations when helpful
• Keep executive summary concise

❌ Don't ignore context

• Account for holidays, team changes, incidents
• Note external dependencies
• Acknowledge unique circumstances

❌ Don't make assumptions

• Verify data accuracy
• Ask questions when patterns are unclear
• Get team input on interpretations

---

Report Variations

Quick Retro (Light Version)

For shorter retrospectives or weekly reviews:

# Week of {date} - Quick Retro

**Completed:** {count} tickets
**Time Logged:** {hours}h
**Blockers:** {count}

### Wins
- {win}

### Challenges
- {challenge}

### Next Week Focus
- {focus_area}

Epic/Feature Retro

For analyzing a specific epic or feature:

# {Epic Name} Retrospective

**Epic:** {KEY-123}
**Timeline:** {start} to {end}
**Team:** {members}

### Delivery Summary
- **Total Stories:** {count}
- **Total Time:** {hours}h
- **Original Estimate:** {hours}h

### What We Learned
{Insights specific to this epic}

### Technical Debt Identified
{Debt items to address}

Filter-Based Retro

For analyzing issues from a saved filter:

# Retrospective - {Filter Name}
**Period:** {start} to {end}
**Filter:** {filter_name} (ID: {filter_id})

### Filter Scope
{Brief description of what the filter includes}

### Delivery Summary
- **Issues Completed:** {count}
- **Total Time Logged:** {hours}h
- **Estimation Accuracy:** {percentage}%

### Analysis
{Insights specific to the filtered set of issues}

---

Integration Points

After Generation

Share the report:

• Upload to Confluence for team visibility
• Post summary in team chat
• Present in retrospective meeting

Create follow-ups:

• Convert action items to Jira tickets
• Schedule follow-up reviews
• Track improvements over time

Archive for trends:

• Compare with previous retrospectives
• Track metric improvements
• Identify recurring patterns

---

Anti-Patterns

What to Avoid

❌ Data dump without insights

• Raw data alone isn't helpful
• Always interpret and contextualize
• Answer "so what?" for each metric

❌ Focusing only on negatives

• Balance is crucial for morale
• Acknowledge progress and wins
• Frame challenges constructively

❌ Vague action items

• "Communicate better" is not actionable
• Be specific about who, what, when
• Make items measurable

❌ Ignoring time tracking gaps

• Note when data is incomplete
• Don't draw conclusions from bad data
• Highlight data quality issues

❌ One-time analysis

• Retrospectives are most valuable over time
• Track trends across multiple periods
• Learn from patterns, not single events