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"]
}

Return this JSON to the orchestrator.

Actions Explained

Action	What Orchestrator Should Do
spawn	Use prompt-builder, then spawn single agent
respawn	Re-spawn same agent type with feedback
spawn_batch	Spawn multiple developers for groups_to_spawn
validate_then_end	Invoke bazinga-validator skill, then route based on verdict
pause_for_user	Surface clarification question to user
end_session	Mark session complete, no more spawns

Validator Workflow

When PM sends BAZINGA, the orchestrator invokes bazinga-validator. After validator returns:

Orchestrator calls workflow-router skill:

workflow-router, determine next action:
Current agent: validator
Status: ACCEPT  # or REJECT
Session ID: {session_id}

Then invoke: Skill(command: "workflow-router")

Note: Validator is session-scoped - omit group_id (not needed for routing).

Transitions defined in workflow/transitions.json → validator section:

• ACCEPT → end_session action → Complete shutdown protocol
• REJECT → spawn action with next_agent: project_manager → PM fixes issues

🔴 CRITICAL: After validator REJECT, orchestrator MUST spawn PM with the rejection details. Do NOT stop!

Special Flags in Result

Flag	Meaning
groups_to_spawn	Array of group IDs to spawn in parallel
escalation_applied	True if escalated to SSE due to failures
escalation_reason	Why escalation happened
skip_reason	Why QA was skipped (testing mode)
phase_check	"continue" or "complete" after merge
security_override	True if security task forced SSE
bypass_qa	True if QA should be skipped (RE tasks)

Output Format

JSON object with routing decision. Parse and execute the action.

Error Handling

Error	Meaning
success: false	Unknown transition - check fallback_action
Unknown status	Route to Tech Lead for manual handling
Database not found	Cannot route - needs config seeding

If routing fails, use the fallback_action in the error response.