Knuckles-Team Ai Research Survey Workflow
CONCEPT:RESEARCH-001
Comprehensive AI research survey combining paper search with data science capabilities for analysis.
Steps
Step 0: ScholarX Agent-Paper Search [skill: scholarx-mcp]
Agent: search-agent
Tools: sx_search, graph_query
Search for recent papers on large language model agents published in 2025-2026
Expected: paper, language, model
Step 1: Data Science Mcp
Agent: analyzer-agent
Tools: graph_analyze, sx_storage
Describe the iris dataset using the describe_dataset tool to verify data science capabilities
Expected: dataset, feature
Step 2: ScholarX KG-RAG Paper Search [skill: scholarx-mcp]
Agent: synthesizer-agent
Tools: graph_analyze, document_tools
Search for papers on knowledge graph reasoning and retrieval augmented generation
Expected: knowledge, graph
Step 3: KG Persistence [depends_on: Step 2]
Agent: synthesizer-agent
Tools: graph_write
Persist workflow results as nodes and edges in the Knowledge Graph. Create appropriate typed nodes with metadata and link to existing domain entities.
Output
- Ai Research Survey results persisted in KG
- Structured report (MD/PDF)
- Audit trail with timestamps and agent attributions
Execution
Run this workflow as a dependency-ordered DAG. Steps with no unmet depends_on run in parallel; dependents run after their prerequisites complete.
- Run first (in parallel): Step 0 — ScholarX Agent-Paper Search; Step 1 — Data Science Mcp; Step 2 — ScholarX KG-RAG Paper Search
- After level 0: Step 3 — KG Persistence
Execution: If graph-os is reachable, offload the whole DAG via graph_orchestrate action=execute_workflow (or the kg-delegate skill) for true parallel/swarm execution. Otherwise execute the steps natively in dependency order: run steps with no unmet depends_on in parallel, then their dependents.