- 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
6.6 KiB
6.6 KiB
Knowledge Base — Atomizer Extension
Extension of Mario's shared
knowledge-baseskill for Atomizer HQ's agentic workflow.Base skill:
/home/papa/clawd/skills/knowledge-base/SKILL.mdThis 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 toprojects/<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:
- Which KB files to read for context
- Which KB files to update with results
- What generation number to use
- Whether a new gen doc is needed