---
description: Run evolution cycle — judge last workflow, optimize underperforming agents, re-test
---

# /evolve — Pipeline Evolution Command

Runs the automated evolution cycle on the most recent (or specified) workflow.

## Usage

```
/evolve                     # evolve last completed workflow
/evolve --issue 42          # evolve workflow for issue #42
/evolve --agent planner     # focus evolution on one agent
/evolve --dry-run           # show what would change without applying
/evolve --history           # print fitness trend chart
```

## Execution

### Step 1: Judge
```
Task(subagent_type: "pipeline-judge")
→ produces fitness report
```

### Step 2: Decide
```
IF fitness >= 0.85:
  echo "✅ Pipeline healthy (fitness: {score}). No action needed."
  append to fitness-history.jsonl
  EXIT

IF fitness >= 0.70:
  echo "⚠ Pipeline marginal (fitness: {score}). Optimizing weak agents..."
  identify agents with lowest per-agent scores
  Task(subagent_type: "prompt-optimizer", target: weak_agents)

IF fitness < 0.70:
  echo "🔴 Pipeline underperforming (fitness: {score}). Major optimization..."
  Task(subagent_type: "prompt-optimizer", target: all_flagged_agents)
  IF fitness < 0.50:
    Task(subagent_type: "agent-architect", action: "redesign", target: worst_agent)
```

### Step 3: Re-test
```
Re-run the SAME workflow with updated prompts
Task(subagent_type: "pipeline-judge") → fitness_after

IF fitness_after > fitness_before:
  commit prompt changes
  echo "📈 Fitness improved: {before} → {after}"
ELSE:
  revert prompt changes
  echo "📉 No improvement. Reverting."
```

### Step 4: Log
```
Append to .kilo/logs/fitness-history.jsonl:
{
  "ts": "<now>",
  "issue": <N>,
  "workflow": "<type>",
  "fitness_before": <score>,
  "fitness_after": <score>,
  "agents_optimized": ["planner", "requirement-refiner"],
  "tokens_saved": <delta>,
  "time_saved_ms": <delta>
}
```