Files
Atomizer/hq/skills/atomizer-company/LAC_CRITICAL.md
Antoine 3289a76e19 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
2026-02-15 21:18:18 +00:00

2.2 KiB

LAC Critical Lessons — NEVER FORGET

These are hard-won insights from past optimization sessions. Violating any of these will cause failures.

NX Safety (CRITICAL)

  • NEVER kill ugraf.exe directly → use NXSessionManager.close_nx_if_allowed()
  • PowerShell for NX journals → NEVER use cmd /c
  • Always load *_i.prt before UpdateFemodel() → mesh won't update without the idealized part
  • File chain must be intact: .sim → .fem → *_i.prt → .prt (ALL must be present)

Optimization (CRITICAL)

  • CMA-ES doesn't evaluate x0 first → always call enqueue_trial(x0) to evaluate baseline
  • Surrogate + L-BFGS = DANGEROUS → gradient descent finds fake optima on surrogate surface
  • NEVER rewrite run_optimization.py from scratch → ALWAYS copy a working template (V15 NSGA-II is gold standard)
  • Relative WFE math: use extract_relative() (node-by-node subtraction) → NOT abs(RMS_a - RMS_b) (wrong math!)

File Management (IMPORTANT)

  • Trial folders: trial_NNNN/ — zero-padded, never reused, never overwritten
  • Always copy working studies — never modify originals
  • Output paths must be relative — no absolute Windows/Linux paths (Syncthing-compatible)
  • Never delete trial data mid-run — archive after study is complete

Algorithm Selection (REFERENCE)

Variables Landscape Recommended Notes
< 5 Smooth Nelder-Mead or COBYLA Simple, fast convergence
5-20 Noisy CMA-ES Robust, population-based
> 20 Any Bayesian (Optuna TPE) Efficient with many variables
Multi-obj Any NSGA-II or MOEA/D Pareto front generation
With surrogate Expensive eval GNN surrogate + CMA-ES Reduce simulation count

Common Failures

Symptom Cause Fix
Mesh not updating Missing *_i.prt load Load idealized part first
NX crashes on journal Using cmd /c Switch to PowerShell
Baseline trial missing CMA-ES skips x0 Explicitly enqueue baseline
Optimization finds unphysical optimum Surrogate + gradient Switch to CMA-ES or add validation
Study can't resume Absolute paths in script Use relative paths