Architecture

Architecture Decision Framework

"Requirements drive architecture. Trade-offs inform decisions. ADRs capture rationale."

🎯 Selective Reading Rule

Read ONLY files relevant to the request! Check the content map, find what you need.

🔗 Related Skills

Core Principle

"Simplicity is the ultimate sophistication."

• Start simple
• Add complexity ONLY when proven necessary
• You can always add patterns later
• Removing complexity is MUCH harder than adding it

Validation Checklist

Before finalizing architecture:

• Requirements clearly understood
• Constraints identified
• Each decision has trade-off analysis
• Simpler alternatives considered
• ADRs written for significant decisions
• Team expertise matches chosen patterns

AGI Framework Integration

Qdrant Memory Integration

Before executing complex tasks with this skill:

python3 execution/memory_manager.py auto --query "<task summary>"

Decision Tree:

• Cache hit? Use cached response directly — no need to re-process.
• Memory match? Inject context_chunks into your reasoning.
• No match? Proceed normally, then store results:

python3 execution/memory_manager.py store \
  --content "Description of what was decided/solved" \
  --type decision \
  --tags architecture <relevant-tags>

Note: Storing automatically updates both Vector (Qdrant) and Keyword (BM25) indices.

Agent Team Collaboration

• Strategy: This skill communicates via the shared memory system.
• Orchestration: Invoked by orchestrator via intelligent routing.
• Context Sharing: Always read previous agent outputs from memory before starting.

Local LLM Support

When available, use local Ollama models for embedding and lightweight inference:

• Embeddings: nomic-embed-text via Qdrant memory system
• Lightweight analysis: Local models reduce API costs for repetitive patterns