Files
Atomizer/hq/shared/skills/knowledge-base-atomizer-ext.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

6.6 KiB

Knowledge Base — Atomizer Extension

Extension of Mario's shared knowledge-base skill for Atomizer HQ's agentic workflow.

Base skill: /home/papa/clawd/skills/knowledge-base/SKILL.md This file: Atomizer-specific conventions for how agents use the KB system.


Key Differences from Base Skill

Location

  • Base: KB lives in Obsidian vault (/obsidian-vault/2-Projects/<Project>/KB/)
  • Atomizer: KB lives in Atomizer repo (/repos/Atomizer/projects/<project>/kb/)
  • Same structure, different home. Gitea-browseable, git-tracked.

Input Sources

  • Base: Primarily video session exports via CAD-Documenter
  • Atomizer: Mixed sources:
    • CEO input via Slack channels
    • Agent-generated analysis (Tech Lead breakdowns, optimization results)
    • NX model introspection data
    • Automated study results
    • Video sessions (when applicable — uses base skill pipeline)

Contributors

  • Base: Single AI (Mario) processes sessions
  • Atomizer: Multiple agents contribute:
Agent Writes To When
Manager 🎯 _index.md, _history.md, dev/gen-XXX.md After each project phase
Technical Lead 🔧 fea/, components/ (technical sections) During analysis + review
Optimizer (future) fea/results/, components/ (optimization data) After study completion
Study Builder 🏗️ (future) Study configs, introspection data During study setup
CEO (Antoine) Any file via Gitea or Slack input Anytime

Project Structure (Atomizer Standard)

projects/<project-name>/
├── README.md                # Project overview, status, links
├── CONTEXT.md               # Intake requirements, constraints
├── BREAKDOWN.md             # Technical analysis (Tech Lead)
├── DECISIONS.md             # Numbered decision log
│
├── models/                  # Reference NX models (golden copies)
│   ├── *.prt, *.sim, *.fem
│   └── README.md
│
├── kb/                      # Living Knowledge Base
│   ├── _index.md            # Master overview (auto-maintained)
│   ├── _history.md          # Modification log per generation
│   ├── components/          # One file per component
│   ├── materials/           # Material data + cards
│   ├── fea/                 # FEA knowledge
│   │   ├── models/          # Model setup docs
│   │   ├── load-cases/      # BCs, loads, conditions
│   │   └── results/         # Analysis outputs + validation
│   └── dev/                 # Generation documents (gen-XXX.md)
│
├── images/                  # Screenshots, plots, CAD renders
│   ├── components/
│   └── studies/
│
├── studies/                 # Optimization campaigns
│   └── XX_<name>/
│       ├── README.md        # Study goals, findings
│       ├── atomizer_spec.json
│       ├── model/           # Study-specific model copy
│       │   └── CHANGES.md   # Delta from reference model
│       ├── introspection/   # Model discovery for this study
│       └── results/         # Outputs, plots, STUDY_REPORT.md
│
└── deliverables/            # Final client-facing outputs
    ├── FINAL_REPORT.md      # Compiled from KB
    └── RECOMMENDATIONS.md

Agent Workflows

1. Project Intake (Manager)

CEO posts request → Manager creates:
  - CONTEXT.md (from intake data)
  - README.md (project overview)
  - DECISIONS.md (empty template)
  - kb/ structure (initialized)
  - kb/dev/gen-001.md (intake generation)
  → Delegates technical breakdown to Tech Lead

2. Technical Breakdown (Tech Lead)

Manager delegates → Tech Lead produces:
  - BREAKDOWN.md (full analysis)
  - Updates kb/components/ with structural behavior
  - Updates kb/fea/models/ with solver considerations
  - Identifies gaps → listed in kb/_index.md
  → Manager creates gen-002 if substantial new knowledge

3. Model Introspection (Tech Lead / Study Builder)

Before each study:
  - Copy reference models/ → studies/XX/model/
  - Run NX introspection → studies/XX/introspection/
  - Document changes in model/CHANGES.md
  - Update kb/fea/ with any new model knowledge

4. Study Execution (Optimizer / Study Builder)

During/after optimization:
  - Results written to studies/XX/results/
  - STUDY_REPORT.md summarizes findings
  - Key insights feed back into kb/:
    - Component sensitivities → kb/components/
    - FEA validation → kb/fea/results/
    - New generation doc → kb/dev/gen-XXX.md

5. Deliverable Compilation (Reporter / Manager)

When project is complete:
  - Compile kb/ → deliverables/FINAL_REPORT.md
  - Use cad_kb.py cdr patterns for structured output
  - Cross-reference DECISIONS.md for rationale
  - Include key plots from images/ and studies/XX/results/plots/

Generation Conventions

Each major project event creates a new generation document:

Gen Trigger Author
001 Project intake + initial breakdown Manager
002 Gap resolution / model introspection Tech Lead
003 DoE study complete (landscape insights) Manager / Optimizer
004 Optimization complete (best design) Manager / Optimizer
005 Validation / final review Tech Lead

Generation docs go in kb/dev/gen-XXX.md and follow the format:

# Gen XXX — <Title>
**Date:** YYYY-MM-DD
**Sources:** <what triggered this>
**Author:** <agent>

## What Happened
## Key Findings
## KB Entries Created/Updated
## Decisions Made
## Open Items
## Next Steps

Decision Log Conventions

All project decisions go in DECISIONS.md:

## DEC-<PROJECT>-NNN: <Title>
- **Date:** YYYY-MM-DD
- **By:** <agent or person>
- **Decision:** <what was decided>
- **Rationale:** <why>
- **Status:** Proposed | Approved | Superseded by DEC-XXX

Agents MUST check DECISIONS.md before proposing changes that could contradict prior decisions.


Relationship to Base Skill

  • Use base skill CLI (cad_kb.py) when applicable — adapt paths to projects/<name>/kb/
  • Use base skill templates for component files, generation docs
  • Follow base accumulation logic — sessions add, never replace
  • Push general improvements upstream — if we improve KB processing, notify Mario for potential merge into shared skill

Handoff Protocol

When delegating KB-related work between agents, use OP_09 format and specify:

  1. Which KB files to read for context
  2. Which KB files to update with results
  3. What generation number to use
  4. Whether a new gen doc is needed