feat(convert): integrate process metrics into JSON and SQLite converters

- add process_metrics function to record peak RSS and CPU time
- update JSON converter to include metrics in completion marker
- modify SQLite converter to utilize new metrics for performance tracking
- enhance storage size updates with locking mechanism for concurrent access

common/pipeline.py

@@ -10,6 +10,7 @@ import logging
 import sys
 from pathlib import Path
 
+import psutil
 import yaml
 
 logger = logging.getLogger(__name__)
@@ -57,3 +58,22 @@ def write_marker(out_root: Path, task_id: str, entries: dict) -> Path:
 def load_lab_config(out_root: Path) -> dict:
 	with open(out_root / "config" / "lab_config.yaml", encoding="utf-8") as fh:
 		return yaml.safe_load(fh)
+
+
+def process_metrics() -> dict:
+	"""Peak RSS and CPU time of the current process.
+
+	Every converter records its own build cost in its marker so the task 10
+	hardware-sizing model can extrapolate per-format RAM/CPU needs, not just
+	disk. peak_wset exists on Windows; the rss fallback covers Linux, where
+	the value is the current (not peak) RSS - good enough for streaming
+	converters whose RSS is flat by design.
+	"""
+	proc = psutil.Process()
+	mem = proc.memory_info()
+	peak = getattr(mem, "peak_wset", 0) or mem.rss
+	cpu = proc.cpu_times()
+	return {
+		"proc_peak_rss_mb": round(peak / 1048576, 1),
+		"proc_cpu_seconds": round(cpu.user + cpu.system, 1),
+	}