Bridge Coach memory brief with project metrics summaries.

Add Performance Summary block to memory brief, document metrics synthesis in
agent-coach, and add e2e and CLI tests for qualitative plus quantitative briefs.

src/kaizen_agentic/cli.py

@@ -11,7 +11,7 @@ from typing import List, Optional
 
 from .registry import AgentRegistry, AgentCategory
 from .installer import AgentInstaller, ProjectInitializer, InstallationConfig
-from .metrics import MetricsStore, OptimizerStore
+from .metrics import MetricsStore, OptimizerStore, performance_summary_markdown
 from .optimization import OptimizationLoop, MIN_SAMPLES_FOR_RECOMMENDATIONS
 
 
@@ -892,12 +892,21 @@ def memory_brief(agent_name: str, target: str, raw: bool):
     click.echo(f"Sources: {', '.join(sources) if sources else 'none'}")
     click.echo()
 
-    if not sources:
+    metrics_store = MetricsStore(project_root, agent_name)
+    metrics_summary = metrics_store.read_summary()
+    if metrics_summary is None and metrics_store.executions_path.exists():
+        metrics_summary = metrics_store.write_summary()
+
+    if not sources and not metrics_summary:
         click.echo("No agent memory files found in this project.")
         click.echo(f"  Run: kaizen-agentic memory init {agent_name}")
         click.echo("  Then load the coach agent (agents/agent-coach.md) for synthesis.")
         return
 
+    performance_block = performance_summary_markdown(metrics_summary or {})
+    if performance_block:
+        click.echo(performance_block)
+
     # Own memory section
     if own_memory:
         click.echo("### Your Memory")

src/kaizen_agentic/metrics.py

@@ -21,6 +21,36 @@ def _parse_timestamp(value: str) -> datetime:
     return datetime.fromisoformat(normalized)
 
 
+_TREND_ARROWS = {"up": "↑", "down": "↓", "stable": "→", "unknown": "?"}
+
+
+def performance_summary_markdown(summary: Dict[str, Any]) -> str:
+    """Format ADR-004 summary.json as a Coach brief markdown section."""
+    if not summary or summary.get("execution_count", 0) == 0:
+        return ""
+
+    trend = summary.get("trend", {})
+    success_trend = trend.get("success_rate", "unknown")
+    quality_trend = trend.get("quality_score", "unknown")
+
+    lines = [
+        "## Performance Summary",
+        "",
+        f"- Executions: {summary['execution_count']}",
+        (
+            f"- Success rate: {summary['success_rate']:.1%} "
+            f"({_TREND_ARROWS.get(success_trend, '?')} {success_trend})"
+        ),
+        f"- Avg quality: {summary['avg_quality_score']:.2f} "
+        f"({_TREND_ARROWS.get(quality_trend, '?')} {quality_trend})",
+        f"- Avg execution time: {summary['avg_execution_time_s']:.1f}s",
+    ]
+    if summary.get("last_execution"):
+        lines.append(f"- Last execution: {summary['last_execution']}")
+    lines.append("")
+    return "\n".join(lines)
+
+
 def _trend_direction(recent: List[float], prior: List[float]) -> str:
     if not recent:
         return "unknown"