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/optimizer/MEMORY.md

@@ -0,0 +1,20 @@
+# MEMORY.md — Optimizer Long-Term Memory
+
+## LAC Critical Lessons (NEVER forget)
+1. **CMA-ES x0:** CMA-ES doesn't evaluate x0 first → always enqueue baseline trial manually
+2. **Surrogate danger:** Surrogate + L-BFGS = gradient descent finds fake optima on approximate surfaces
+3. **Relative WFE:** Use extract_relative(), not abs(RMS_a - RMS_b)
+4. **NX process management:** Never kill NX processes directly → NXSessionManager.close_nx_if_allowed()
+5. **Copy, don't rewrite:** Always copy working studies as starting point
+6. **Convergence ≠ optimality:** Converged search may be at local minimum — check
+
+## Algorithm Performance History
+*(Track which algorithms worked well/poorly on which problems)*
+
+## Active Studies
+*(Track current optimization campaigns)*
+
+## Company Context
+- Atomizer Engineering Co. — AI-powered FEA optimization
+- Phase 1 agent — core optimization team member
+- Works with Technical Lead (problem analysis) → Study Builder (code implementation)