/feedback

Collect feedback from agents and humans on stories, epics, and sprints for continuous process improvement.

Quick Start

Copy/agileflow:feedback SCOPE=story STORY=US-0042

Parameters

Examples

Story Completion Feedback

Copy/agileflow:feedback SCOPE=story STORY=US-0042

After marking a story done, collect:

• AC clarity rating (1-5)
• Dependency resolution rating (1-5)
• Estimate accuracy rating (1-5)
• Implementation smoothness rating (1-5)
• What went well
• What could be improved

Epic Retrospective Feedback

Copy/agileflow:feedback SCOPE=epic EPIC=EP-0010

After epic completion, gather:

• Success metrics against epic goals
• What went well during execution
• What didn't go well
• Surprises and learnings
• Actions for next epic

Sprint Retrospective Feedback

Copy/agileflow:feedback SCOPE=sprint

At sprint end, collect team feedback:

• Continue (keep doing)
• Stop (no longer useful)
• Start (new practices)
• Experiments to try
• Blockers removed this sprint

Anonymous Feedback

Copy/agileflow:feedback SCOPE=story STORY=US-0045 ANONYMOUS=yes

Collects feedback without attribution for sensitive topics.

Output

Feedback Files

Saves feedback to:

Copydocs/08-project/feedback/<YYYYMMDD>-<story-or-epic-id>.md

Example Story Feedback

Copy## Story Feedback: US-0042
 
**Completed by**: AG-API
**Date**: 2025-12-22
 
### Ratings (1-5)
- AC clarity: 5 (crystal clear)
- Dependencies resolved: 4 (one minor blocker)
- Estimate accuracy: 5 (spot on)
- Implementation smoothness: 4 (smooth)
 
### What Went Well
- Clear acceptance criteria with examples
- All tests passed on first run
- Good documentation
 
### What Could Be Improved
- Database schema migration took longer than expected
 
### Blockers Encountered
- None significant
 
### Learnings/Insights
- JSON schema validation saved hours of debugging

Feedback Types

1. Story Completion Feedback

Ratings for:

• AC Clarity - Were acceptance criteria clear? (1-5)
• Dependencies - Were blockers resolved? (1-5)
• Estimate Accuracy - Was estimation accurate? (1-5)
• Implementation - How smooth was the process? (1-5)
• Testing - Were tests adequate? (1-5)
• Documentation - Was documentation sufficient? (1-5)

Plus open-ended:

• What went well? (2-3 bullets)
• What could be improved? (2-3 bullets)
• Blockers encountered?
• Learnings/insights?

2. Agent Performance Feedback

Tracks effectiveness:

• Stories completed
• Stories blocked
• Average completion time
• Strengths observed
• Areas for improvement
• Recommendations

3. Epic Retrospective

After epic completion:

• Success metrics vs goals
• What went well?
• What didn't go well?
• Surprises and learnings
• Actions for next epic

4. Sprint Retrospective

At sprint end:

• Continue (keep doing)
• Stop (no longer useful)
• Start (new practices)
• Experiments to try
• Blockers removed
• Recurring issues

Metrics Tracked

The feedback system tracks:

Analysis & Insights

Pattern Detection

Auto-detects patterns:

• Unclear AC - Stories with clarity scores under 3 → Improve template
• Poor estimates - Large variance → Revise estimation guide
• Frequent blockers - Stories often blocked → Improve dependency tracking
• Low test coverage - Tests inadequate → Enforce standards earlier

Actionable Improvements

Auto-generates:

• Improvement stories for recurring issues
• Process recommendations for detected problems
• Recognition for wins and high performers
• ADRs for architectural learnings

Retrospective Reports

Generates comprehensive summaries:

• Overall sentiment (improving/declining/stable)
• Top wins with celebration
• Top challenges
• Recommended actions
• Team insights and learnings

Workflow

1. Auto-prompt at trigger points:
   • Story status changes to "done"
   • Epic reaches 100% completion
   • Sprint end date reached
2. Present feedback form with pre-filled context
3. Ask: "Provide feedback now? (YES/NO/LATER)"
4. If YES: Collect ratings and comments interactively
5. Save to feedback directory
6. Analyze patterns across all feedback
7. Suggest improvement stories for issues

Use When

• Story completion - Immediately after marking done
• Epic retrospective - After epic completion
• Sprint end - Gather team learnings
• Process improvement - Monthly review of feedback patterns
• Performance review - Quarterly agent evaluations
• Team retrospectives - Formal sprint ceremonies

Auto-Triggers

Feedback prompts appear:

• When story status changes to "completed"
• When epic reaches 100% completion
• At configured sprint end dates
• Can be manually requested any time

Integration

Feedback flows into:

• Retrospectives - Insights included in sprint retros
• Metrics - Ratings tracked over time
• Story creation - Auto-generate improvement stories
• ADRs - Document learnings as architectural decisions
• Agent profiles - Performance data updates rosters

Related Commands

• /retro - Sprint retrospective analysis
• /metrics - Project metrics and trends
• /update - Stakeholder progress reports
• /status - Current story and team status