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