# Retrospective Insight Brief

> Synthesize retrospectives into themes, root causes, decisions, and team improvements.

- Skill: `danielpradilla/retrospective-insight-brief` (Agent Skill)
- Install (CLI): `npx skillmds add danielpradilla/retrospective-insight-brief`
- Raw SKILL.md: https://api.skillmd.com/api/skills/danielpradilla/retrospective-insight-brief/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: danielpradilla (https://skillmd.com/u/danielpradilla)
- Updated: 2026-08-19
- Page: https://skillmd.com/skills/danielpradilla/retrospective-insight-brief

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# Retrospective Insight Brief

Generate a data-grounded retrospective brief that separates facts from feelings, so the team spends retro time on solutions rather than debating what happened.

## Required Inputs

Ask the user for these if not provided:
- **Sprint tickets: planned vs. completed**
- **Carry-over tickets and reasons** (if known)
- **Tickets reopened after closing** (quality signal)
- **Any incidents or unplanned work** (scope creep signal)
- **Sprint velocity vs. historical average** (trend context)

## Process
1. Calculate: completion rate, carry-over rate, unplanned work percentage
2. Identify patterns: which ticket types were most likely to carry over? Which caused blockers?
3. Note any process or communication breakdowns visible in the data
4. Prepare 3 "Start / Stop / Continue" prompts based on the data - not generic, specific to this sprint
5. Suggest 1 concrete experiment for the next sprint based on the biggest friction point
6. **Validate** - Confirm each prompt is specific to this sprint (not a recycled generic prompt), and that the recommended experiment is concrete and measurable

## Output Structure

### Sprint [Number] Retrospective Brief

**By the Numbers:**
- Planned: [n] tickets | Completed: [n] | Carry-over: [n] | Completion rate: [%]
- Unplanned work: [n] tickets ([%] of capacity)
- Velocity: [points] vs. [average] average

**What the Data Suggests:**
[2-3 observations grounded in the numbers above]

**Discussion Prompts:**
- Start: [specific prompt based on this sprint's data]
- Stop: [specific prompt based on this sprint's data]
- Continue: [specific prompt based on this sprint's data]

**Suggested Experiment for Next Sprint:**
[One concrete, testable process change - with a specific success metric]

## Quality Checks

- [ ] Each Start/Stop/Continue prompt names a specific behaviour, not a vague category
- [ ] The recommended experiment is testable in one sprint
- [ ] Carry-over analysis identifies the ticket type or cause, not just the count
- [ ] Data observations don't assign blame - they describe patterns
- [ ] Velocity trend is mentioned in context (is this a one-off or a pattern?)