retrospective
Analyze completed tasks to improve the Ralph system. Saves learnings to living knowledge vault and coordinates insights across 6 ralph-* teammates.
Best use case
retrospective is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Analyze completed tasks to improve the Ralph system. Saves learnings to living knowledge vault and coordinates insights across 6 ralph-* teammates.
Teams using retrospective should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/retrospective/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How retrospective Compares
| Feature / Agent | retrospective | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Analyze completed tasks to improve the Ralph system. Saves learnings to living knowledge vault and coordinates insights across 6 ralph-* teammates.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Skill: Retrospective & Self-Improvement
**ultrathink** - Take a deep breath. We're not here to write code. We're here to make a dent in the universe.
## v2.88 Key Changes (MODEL-AGNOSTIC)
- **Model-agnostic**: Uses model configured in `~/.claude/settings.json` or CLI/env vars
- **No flags required**: Works with the configured default model
- **Flexible**: Works with GLM-5, Claude, Minimax, or any configured model
- **Settings-driven**: Model selection via `ANTHROPIC_DEFAULT_*_MODEL` env vars
## The Vision
Every retrospective should make the system inevitable and better.
## Your Work, Step by Step
1. **Summarize outcomes**: Task, complexity, iterations, models.
2. **Analyze effectiveness**: Routing, clarification, and agents.
3. **Identify gaps**: Missed checks or friction.
4. **Propose improvements**: Concrete, minimal changes.
## Ultrathink Principles in Practice
- **Think Different**: Question the status quo.
- **Obsess Over Details**: Use evidence, not guesses.
- **Plan Like Da Vinci**: Structure feedback before writing.
- **Craft, Don't Code**: Keep recommendations actionable.
- **Iterate Relentlessly**: Apply learnings immediately.
- **Simplify Ruthlessly**: Focus on the few changes that matter.
## Purpose
Analyze completed tasks to improve the Ralph Wiggum system.
## When to Use
MANDATORY after every task completion, before declaring VERIFIED_DONE.
## Analysis Categories
### 1. Routing Effectiveness
- Was the complexity classification accurate?
- Did the chosen model perform well?
- Should routing thresholds change?
## Agent Teams Integration (v2.88)
**Optimal Scenario**: Pure Agent Teams (Native)
This skill uses Pure Agent Teams with native coordination - no custom subagent specialization needed.
### Why Scenario A for This Skill
- Retrospective is primarily analytical and sequential
- Read/Grep tools available to all native agents
- Analysis doesn't require specialized tool restrictions
- Native agent types sufficient for metric gathering
- Lower complexity, faster execution
### Configuration
1. **TeamCreate**: Optional, for simple retrospective tasks
2. **Task**: Use native agent types (no ralph-* needed)
3. **Hooks**: TeammateIdle + TaskCompleted available if needed
4. **Simple**: Minimal setup overhead
### Workflow Pattern
```
TeamCreate (optional)
→ Task(analyze completed work)
→ Native agent gathers metrics
→ Complete with improvement proposals
```
### When This Is Sufficient
- Single-task retrospective analysis
- Simple metric gathering workflows
- No specialized analysis needed
- Quick post-task reviews preferred
### 2. Clarification Quality
- Were the right questions asked?
- Did any missed clarifications cause rework?
- Should question templates be updated?
### 3. Agent Performance
- Which subagents were most useful?
- Any agents that didn't add value?
- New agent patterns needed?
### 4. Quality Gate Effectiveness
- Did gates catch real issues?
- Any false positives/negatives?
- Missing validations?
### 5. Iteration Efficiency
- How many iterations were used?
- Could it have been done faster?
- Any wasted iterations?
## Output Format
```markdown
## 📊 Task Retrospective
### Summary
- Task: [description]
- Complexity: [classified] → [actual]
- Iterations: [used] / [limit]
- Models: [list used]
### What Went Well
- [positive 1]
- [positive 2]
### Improvement Opportunities
1. **[Category]**: [description]
- Current: [what happens now]
- Proposed: [improvement]
- Impact: [low/medium/high]
- Risk: [low/medium/high]
### Proposed Changes
```json
{
"type": "routing_adjustment|clarification_enhancement|agent_behavior|new_command|delegation_update|quality_gate",
"file": "[path to modify]",
"change": "[description]",
"justification": "[why]"
}
```
```
## Improvement Types
| Type | Example |
|------|---------|
| routing_adjustment | Change complexity thresholds |
| clarification_enhancement | Add new question templates |
| agent_behavior | Modify agent instructions |
| new_command | Create new slash command |
| delegation_update | Change model assignments |
| quality_gate | Add/modify validations |Related Skills
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