Knuckles-Team Personal Fitness Trainer Workflow
CONCEPT:HEALTH-001
Assesses user fitness goals, queries appropriate strength training exercises, constructs a custom workout routine, and registers it using wger-agent tools.
Steps
Step 0: Fitness Trainer
Agent: data-collector
Tools: graph_query
Conduct the user's fitness and muscle group intake assessment. Query the wger exercise database using wger_exercise tool with search query and muscle group parameters to discover target exercises.
Expected: intake, exercises
Step 1: Wger Agent
Agent: analyzer-agent
Tools: graph_analyze
Create and configure a personal strength routine. Call the wger_routine tool to create a new routine, and then configure its workout days and exercises using the wger_routineconfig tool.
Expected: routine, configuration
Step 2: KG Persistence [depends_on: wger-agent]
Agent: analyzer-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
- Personal Fitness Trainer results persisted in KG
- Structured report (MD/PDF)
- Audit trail with timestamps and agent attributions
Human Oversight Required
✅ Critical decisions require human review and approval.
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 — Fitness Trainer; Step 1 — Wger Agent
- After level 0: Step 2 — 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.