feat: add Atomizer HQ multi-agent cluster infrastructure

- 8-agent OpenClaw cluster (Manager, Tech-Lead, Secretary, Auditor,
  Optimizer, Study-Builder, NX-Expert, Webster)
- Orchestration engine: orchestrate.py (sync delegation + handoffs)
- Workflow engine: YAML-defined multi-step pipelines
- Agent workspaces: SOUL.md, AGENTS.md, MEMORY.md per agent
- Shared skills: delegate, orchestrate, atomizer-protocols
- Capability registry (AGENTS_REGISTRY.json)
- Cluster management: cluster.sh, systemd template
- All secrets replaced with env var references

hq/workspaces/technical-lead/backfill_mass.py

@@ -0,0 +1,59 @@
+#!/usr/bin/env python3
+"""
+Backfill mass_kg in results.json for all DOE iterations.
+Reads _temp_mass.txt (key=value format) and patches results.json.
+"""
+import json
+from pathlib import Path
+
+ITER_DIR = Path("/home/papa/atomizer/projects/hydrotech-beam/studies/01_doe_landscape/iterations")
+
+fixed = 0
+skipped = 0
+errors = []
+
+for iter_path in sorted(ITER_DIR.iterdir()):
+    if not iter_path.is_dir() or not iter_path.name.startswith("iter"):
+        continue
+
+    mass_file = iter_path / "_temp_mass.txt"
+    results_file = iter_path / "results.json"
+
+    if not mass_file.exists():
+        errors.append(f"{iter_path.name}: no _temp_mass.txt")
+        continue
+    if not results_file.exists():
+        errors.append(f"{iter_path.name}: no results.json")
+        continue
+
+    # Parse mass
+    content = mass_file.read_text().strip()
+    if '=' in content:
+        content = content.split('=', 1)[1]
+    try:
+        mass_kg = float(content)
+    except ValueError:
+        errors.append(f"{iter_path.name}: unparseable mass: {content!r}")
+        continue
+
+    # Patch results.json
+    with open(results_file) as f:
+        results = json.load(f)
+
+    old_mass = results.get("mass_kg")
+    results["mass_kg"] = mass_kg
+
+    with open(results_file, 'w') as f:
+        json.dump(results, f, indent=2)
+
+    status = "NaN→fixed" if (old_mass is None or old_mass != old_mass) else f"{old_mass}→{mass_kg}"
+    print(f"  {iter_path.name}: mass_kg = {mass_kg:.4f} kg  ({status})")
+    fixed += 1
+
+print(f"\n✅ Backfilled {fixed} iterations")
+if skipped:
+    print(f"⏭️  Skipped {skipped}")
+if errors:
+    print(f"❌ Errors: {len(errors)}")
+    for e in errors:
+        print(f"   {e}")