Antoine
3289a76e19
- 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
2.8 KiB
2.8 KiB
name, description, version
| name | description | version |
|---|---|---|
| atomizer-protocols | Atomizer Engineering Co. protocols and procedures. Consult when performing operational or technical tasks (studies, optimization, reports, troubleshooting). | 1.1 |
Atomizer Protocols Skill
Your company's operating system. Load QUICK_REF.md when you need the cheatsheet.
When to Load
- When performing a protocol-related task (creating studies, running optimizations, generating reports, etc.)
- NOT every session — these are reference docs, not session context.
Key Files
QUICK_REF.md— 2-page cheatsheet. Start here.protocols/OP_*— Operational protocols (how to do things)protocols/SYS_*— System protocols (technical specifications)
Protocol Lookup
| Need | Read |
|---|---|
| Create a study | OP_01 |
| Run optimization | OP_02 |
| Monitor progress | OP_03 |
| Analyze results | OP_04 |
| Export training data | OP_05 |
| Troubleshoot | OP_06 |
| Disk optimization | OP_07 |
| Generate report | OP_08 |
| Hand off to another agent | OP_09 |
| Start a new project | OP_10 |
| Post-phase learning cycle | OP_11 |
| Choose algorithm | SYS_15 |
| Submit job to Windows | SYS_19 |
| Read/write shared knowledge | SYS_20 |
Protocol Index
Operational (OP_01–OP_10)
| ID | Name | Summary |
|---|---|---|
| OP_01 | Create Study | Study lifecycle from creation through setup |
| OP_02 | Run Optimization | How to launch and manage optimization runs |
| OP_03 | Monitor Progress | Tracking convergence, detecting issues |
| OP_04 | Analyze Results | Post-optimization analysis and interpretation |
| OP_05 | Export Training Data | Preparing data for ML/surrogate models |
| OP_06 | Troubleshoot | Diagnosing and fixing common failures |
| OP_07 | Disk Optimization | Managing disk space during long runs |
| OP_08 | Generate Report | Creating professional deliverables |
| OP_09 | Agent Handoff | How agents pass work to each other |
| OP_10 | Project Intake | How new projects get initialized |
| OP_11 | Digestion | Post-phase learning cycle (store, discard, sort, repair, evolve, self-document) |
System (SYS_10–SYS_20)
| ID | Name | Summary |
|---|---|---|
| SYS_10 | IMSO | Integrated Multi-Scale Optimization |
| SYS_11 | Multi-Objective | Multi-objective optimization setup |
| SYS_12 | Extractor Library | Available extractors and how to use them |
| SYS_13 | Dashboard Tracking | Dashboard integration and monitoring |
| SYS_14 | Neural Acceleration | GNN surrogate models |
| SYS_15 | Method Selector | Algorithm selection guide |
| SYS_16 | Self-Aware Turbo | Adaptive optimization strategies |
| SYS_17 | Study Insights | Learning from study results |
| SYS_18 | Context Engineering | How to maintain context across sessions |
| SYS_19 | Job Queue | Windows execution bridge protocol |
| SYS_20 | Agent Memory | How agents read/write shared knowledge |