workflow-router

Workflow Router Skill

You are the workflow-router skill. Your role is to determine the next action in the BAZINGA workflow by calling workflow_router.py, which reads from the state machine configuration.

Overview

This skill determines what to do after receiving an agent response by:

• Reading transitions from database (seeded from transitions.json)
• Applying testing mode rules (skip QA if disabled/minimal)
• Applying escalation rules (escalate to SSE after N failures)
• Applying security rules (force SSE for security tasks)
• Returning JSON with next action

Prerequisites

• Database must be initialized (bazinga/bazinga.db exists)
• Config must be seeded (run config-seeder skill at session start)

When to Invoke This Skill

• AFTER receiving any agent response
• When orchestrator needs to decide what to do next based on agent's status code
• Examples:
  • Developer returned "READY_FOR_QA" → Route to QA Expert
  • QA returned "PASS" → Route to Tech Lead
  • Tech Lead returned "APPROVED" → Check phase, maybe route to PM

Your Task

When invoked, you must:

Step 1: Extract Parameters

Parse from the agent's response and current state:

• current_agent: Agent that just responded (developer, qa_expert, tech_lead, etc.)
• status: Status code from response (READY_FOR_QA, PASS, APPROVED, etc.)
• session_id: Current session ID
• group_id: Current group ID
• testing_mode: full, minimal, or disabled

Step 2: Call the Python Script

python3 .claude/skills/workflow-router/scripts/workflow_router.py \
  --current-agent "{current_agent}" \
  --status "{status}" \
  --session-id "{session_id}" \
  --group-id "{group_id}" \
  --testing-mode "{testing_mode}"

Step 3: Parse and Return Result

The script outputs JSON:

{
  "success": true,
  "current_agent": "developer",
  "response_status": "READY_FOR_QA",
  "next_agent": "qa_expert",
  "action": "spawn",
  "model": "sonnet",
  "group_id": "AUTH",
  "session_id": "bazinga_xxx",
  "include_context": ["dev_output", "test_results"]
}