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Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
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Read sprint specs and progress to build a complete picture:
Read docs/progress.md → ## Sprint History table for trend data
Read the sprint spec(s) being reviewed: docs/sprints/sprint-N.md — especially the ## Outcome and ## Decisions sections
Git log for commits and their messages
Any blockers or issues encountered (from sprint spec ## Notes and ## Decisions)
2. Metrics Dashboard
2.1. Progress Metrics (single source of truth)
Read docs/progress.md → ## Metrics table and display it directly — sprint-end already computed these values.
Metric
Current
Target
Trend
Status
[Copy all rows from progress.md Metrics table]
Sprint note: [Copy from progress.md if present]
2.2. Sprint-over-Sprint Comparison
Read docs/progress.md → ## Sprint History table. Extract the last 2 rows (current and previous sprint):
Metric
This Sprint
Previous
Δ
Direction
Goal achieved
[✅/❌]
[✅/❌]
—
[streak]
Tasks completed
[X]
[Y]
[+/-]
[↑/↓/→]
Cycle time
[X.Xd]
[Y.Yd]
[+/-]
[↑/↓/→]
Change failure rate
[X%]
[Y%]
[+/-]
[↑/↓/→]
Test coverage Δ
[+X%]
[+Y%]
[+/-]
[↑/↓/→]
Code churn ratio
[0.XX]
[0.YY]
Parse `docs/sessions/.activity-log.jsonl` for story `status-change` events with `story_type` field. Count completed stories by type:
Run `scripts/pm/metrics.sh --churn` and display the high-churn files. Cross-reference with sprint stories — are these files being actively developed, or are they unrelated files being destabilized?
### 2.8. Ground Rule Compliance
### 2.9. Technical Debt Health
Cross-reference high-churn files from 2.6 with debt item Locations. Overlap means debt in actively changing code — it's compounding through every edit.
[+/-]
[↑/↓/→]
AI effectiveness
[0.XX]
[0.YY]
[+/-]
[↑/↓/→]
Sprint satisfaction
[X/5]
[Y/5]
[+/-]
[↑/↓/→]
Also extract from sprint spec (docs/sprints/sprint-N.md → ## Outcome):
Flag any divergence — see the reference file for interpretation guidance. This is the most actionable analysis: leading indicators predict what lagging will show in 1-2 sprints.
2.4. Anti-pattern Detection
Read references/metrics-analysis.md for the four anti-pattern definitions. Check each against Sprint History data:
Perception gap: satisfaction ≥4 AND (CFR or churn rising 2+ sprints)?
Task fragmentation: tasks rising AND cycle time shrinking 2+ sprints?
Weak tests: coverage Δ positive AND CFR rising 2+ sprints?
Maintenance spiral: <50% feature stories AND churn rising?
Flag any detected patterns with specific metric evidence.
2.5. Feature Time Ratio
Type
Count
%
feature
[X]
[X%]
bugfix
[Y]
[Y%]
refactor
[Z]
[Z%]
other
[W]
[W%]
If feature stories are <50% of total, flag: "Less than half of sprint work on features — investigate maintenance burden."
2.6. Hotspot Investigation (conditional)
2.7. Framework Metrics
Run the metrics script for framework-specific data not captured in progress.md:
Active inventory: [N] total — [N] critical / [N] high / [N] medium / [N] low
AI-origin ratio: [N] of [total] active items ([X%]) have origin: ai-generated
Growth trend: Sum debt deltas from sprint outcomes in the review period. Net debt growing, stable, or shrinking?
Resolution velocity: Items resolved since last retro. Average time from Since date to Resolved date.
Stale items: Any active items older than 90 days with no linked story? Flag as neglected.
Interest escalation: Any items whose interest changed from Stable → Growing since last review? These are compounding.
Estimate accuracy: For resolved items, compare Effort actual vs Effort estimate. Systematic bias?
Flag actionable patterns:
Net debt growing 2+ sprints → "Debt accumulating faster than resolution — increase debt allocation in next sprint"
AI-origin >50% → "AI is primary debt source — review quality gates, coding standards enforcement, and AI-specific review depth"
Stale items >3 → "Debt register going stale — items not converting to sprint stories. Check sprint-start debt health check."
3. Retrospective Framework
Use the 4Ls format:
Liked (What went well?)
Consider: smooth processes, good decisions, effective patterns
Learned (What did we discover?)
Consider: technical discoveries, process improvements, gotchas found
Lacked (What was missing?)
Consider: tools, information, clarity, time
Longed For (What do we wish we had?)
Consider: automation, better docs, different approach
4. AI-Assisted Sprint Reflection
Sprint-goal-anchored questions (answer each with evidence, not impressions):
Sprint Goal Effectiveness
Did the sprint goal help scope decisions during story execution? Was it specific enough for AI to use as decision context?
Were there moments where the sprint goal was ignored or forgotten? What caused that?
Should the goal format change for next sprint? (e.g., too vague, too compound, too narrow)
Session & Capacity Accuracy
How many sessions were actually used vs estimated? Were S/M/L sizes accurate?
Which stories took more sessions than sized? Why? (complexity surprise, external dependency, scope change)
Should the session capacity estimate change for next sprint?
AI Execution Quality
Where did AI-generated code create review bottleneck? (time spent reviewing vs writing)
Were any tests weakened or deleted? Were quality gates effective at catching this?
Context management: Did we hit limits? Was the plan-then-execute pattern followed?
TDD discipline: Was test-first maintained, or did time pressure cause shortcuts?
Carry-Over & Churn Analysis
Were carried-over stories goal-critical or non-critical? (Non-critical carry-over = correct prioritization)
What was the sprint churn rate? If >20%, what caused mid-sprint additions?
Were any stories added mid-sprint that should have waited for backlog?
4.5. Architecture Decision Audit
Review whether architectural decisions made during this sprint are properly documented:
Scan commit messages and story plans for technology choices, pattern decisions, or trade-off discussions that aren't captured in docs/adr/
Check for "decisions in chat" — if the team discussed architectural choices during the sprint but didn't create ADRs, flag specific candidates (e.g., "Chose Redis over Memcached for caching — warrants an ADR")
Ground rule candidates — if a pattern was enforced informally during the sprint ("we always do X"), suggest formalizing it as a ground rule backed by an ADR
This prevents the "decisions in Slack" anti-pattern where architectural choices are made but never formalized.
4.6. AI Learnings
Review how AI tools performed during this sprint — what worked, what didn't, and what to update:
Effective patterns: Which prompting strategies, decomposition approaches, or AI workflows produced good results? Document for future sessions and teammates.
Anti-patterns observed: Did AI-generated code introduce specific defect types this sprint? (Check: phantom packages, code duplication, abstraction bypass, tautological tests, over-engineering — see docs/reference/TEAM_WORKFLOW.md → AI-Specific Review Checklist)
Comprehension check: Can every developer explain the AI-generated code they merged? If not, flag specific areas for code walkthroughs. (59% of developers ship AI code they don't fully understand — this is how comprehension debt accumulates.)
Rule updates needed: Should any patterns be added to CLAUDE.md, GROUND_RULES.md, or .claude/rules/ based on this sprint's experience?
Review effectiveness: Did the review layers (pre-commit → CI → AI review → human review) catch issues? Where did defects slip through?
Capture findings in the retrospective summary. Update CLAUDE.md with any new rules or anti-patterns discovered.
5. Action Items
Convert insights into specific actions:
Item
Type
Priority
Owner
Due
[Action]
Process/Tool/Doc
High/Med/Low
Self
[Date]
6. Update CLAUDE.md
If patterns or gotchas were discovered:
Add to CLAUDE.md for future sessions
Update any incorrect conventions
Add new commands if needed
7. Retrospective Summary
## Retrospective - [Date]### Period Covered
[Sprint X / Week of Y]
### Metrics Summary
[Copy the Metrics table from docs/progress.md — single source of truth]
### Key Signals- Leading indicators: [improving/stable/degrading]
- Lagging indicators: [improving/stable/degrading]
- Anti-patterns detected: [list or "none"]
- Feature time ratio: [X]%
### Top 3 Positives1. [Most impactful positive]
2.3.### Top 3 Improvements Needed1. [Most important improvement]
2.3.### Action Items
[List from above]
### Process Adjustments
[Any changes to workflow]
Output the retrospective summary with action items.
