- Add pipeline-judge agent for objective fitness scoring - Update capability-index.yaml with pipeline-judge, evolution config - Add fitness-evaluation.md workflow for auto-optimization - Update evolution.md command with /evolve CLI - Create .kilo/logs/fitness-history.jsonl for metrics logging - Update AGENTS.md with new workflow state machine - Add 6 new issues to MILESTONE_ISSUES.md for evolution integration - Preserve ideas in agent-evolution/ideas/ Pipeline Judge computes fitness = (test_rate*0.5) + (gates*0.25) + (efficiency*0.25) Auto-triggers prompt-optimizer when fitness < 0.70
1.9 KiB
1.9 KiB
description
| 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>
}