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
This commit is contained in:
2026-02-15 21:18:18 +00:00
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commit 3289a76e19
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name: Design Review
description: Multi-agent design review pipeline
trigger: manual
inputs:
design_description:
type: text
description: "What is being reviewed"
requirements:
type: text
description: "Requirements to review against"
steps:
- id: technical_review
agent: tech-lead
task: |
Perform a technical review of the following design:
DESIGN: {inputs.design_description}
REQUIREMENTS: {inputs.requirements}
Assess: structural adequacy, thermal performance, manufacturability,
and compliance with requirements. Identify risks and gaps.
timeout: 300
- id: optimization_review
agent: optimizer
task: |
Assess optimization potential for the following design:
DESIGN: {inputs.design_description}
REQUIREMENTS: {inputs.requirements}
Identify: parameters that could be optimized, potential weight/cost savings,
and whether a formal optimization study is warranted.
timeout: 300
# These two run in PARALLEL (no dependency between them)
- id: audit
agent: auditor
task: |
Perform a final quality review combining both the technical and optimization assessments:
TECHNICAL REVIEW:
{technical_review}
OPTIMIZATION REVIEW:
{optimization_review}
Assess completeness, identify conflicts between reviewers, and provide
a consolidated recommendation.
depends_on: [technical_review, optimization_review]
timeout: 180
notifications:
on_complete: "Design review complete"

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name: Material Trade Study
description: Research, evaluate, and audit material options for optical components
trigger: manual
inputs:
materials:
type: list
description: "Materials to compare"
requirements:
type: text
description: "Performance requirements and constraints"
steps:
- id: research
agent: webster
task: |
Research the following materials: {inputs.materials}
For each material, find: CTE (with temperature range), density, Young's modulus,
cost per kg, lead time, availability, and any known issues for optical applications.
Provide sources for all data.
timeout: 180
retries: 2
output: material_data
- id: evaluate
agent: tech-lead
task: |
Evaluate these materials against our requirements:
REQUIREMENTS:
{inputs.requirements}
MATERIAL DATA:
{research}
Provide a recommendation with full rationale. Include a comparison matrix.
depends_on: [research]
timeout: 300
retries: 1
output: technical_assessment
- id: audit
agent: auditor
task: |
Review this material trade study for completeness, methodological rigor,
and potential gaps:
{evaluate}
Check: Are all requirements addressed? Are sources credible?
Are there materials that should have been considered but weren't?
depends_on: [evaluate]
timeout: 180
output: audit_result
notifications:
on_complete: "Workflow complete"
on_failure: "Workflow failed"

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name: Quick Research
description: Fast web research with technical validation
trigger: manual
inputs:
query:
type: text
description: "Research question"
steps:
- id: research
agent: webster
task: "{inputs.query}"
timeout: 120
retries: 1
- id: validate
agent: tech-lead
task: |
Verify these research findings are accurate and relevant for engineering use:
{research}
Flag any concerns about accuracy, missing context, or applicability.
depends_on: [research]
timeout: 180
notifications:
on_complete: "Research complete"