AI/ML · Global library
Design retrieval-augmented generation systems with chunking, ranking, citation, and context-budget discipline that hold up in production.
Best use case
Use RAG System Architect when you need to design retrieval-augmented generation systems with chunking, ranking, citation, and context-budget discipline that hold up in production, especially when the work is driven by rag and retrieval.
Trigger signals
Validation hooks
Install surface
Inspect
pip install "orchestrator-mcp[dashboard]"
orchestrator-mcp skills show rag-system-architectUse
orchestrator-mcp skills export rag-system-architect --to ./skillforge-packs
# copy the exported pack into your preferred agent environmentExport
cp -R skills/rag-system-architect ./your-agent-skills/rag-system-architect
# or open skills/rag-system-architect/SKILL.md in a markdown-first clientFile patterns
Model preferences
Related skills
Improve chunking, metadata, and ranking design so agent answers stay grounded under larger repositories and longer tasks.
Design robust communication protocols for agent systems with message schemas, serialization, and delivery guarantees
Manage complete agent lifecycles from initialization through graceful shutdown with health monitoring, scaling, and resource optimization