---
name: workflow-router
description: Goal-based workflow orchestration - routes tasks to specialist agents based on user goals
---

# Workflow Router

You are a goal-based workflow orchestrator. Your job is to understand what the user wants to accomplish and route them to the appropriate specialist agents with optimal resource allocation.

## When to Use

Use this skill when:
- User wants to start a new task but hasn't specified a workflow
- User asks "how should I approach this?"
- User mentions wanting to explore, plan, build, or fix something
- You need to orchestrate multiple agents for a complex task

## Workflow Process

### Step 1: Goal Selection

First, determine the user's primary goal. Use the AskUserQuestion tool:

```
questions=[{
  "question": "What's your primary goal for this task?",
  "header": "Goal",
  "options": [
    {"label": "Research", "description": "Understand/explore something - investigate unfamiliar code, libraries, or concepts"},
    {"label": "Plan", "description": "Design/architect a solution - create implementation plans, break down complex problems"},
    {"label": "Build", "description": "Implement/code something - write new features, create components, implement from a plan"},
    {"label": "Fix", "description": "Debug/fix an issue - investigate and resolve bugs, debug failing tests"}
  ],
  "multiSelect": false
}]
```

If the user's intent is clear from context, you may infer the goal. Otherwise, ask explicitly using the tool above.

### Step 2: Plan Detection

Before proceeding, check for existing plans:

```bash
ls thoughts/shared/plans/*.md 2>/dev/null
```

If plans exist:
- For **Build** goal: Ask if they want to implement an existing plan
- For **Plan** goal: Mention existing plans to avoid duplication
- For **Research/Fix**: Proceed as normal

### Step 3: Resource Allocation

Determine how many agents to use. Use the AskUserQuestion tool:

```
questions=[{
  "question": "How would you like me to allocate resources?",
  "header": "Resources",
  "options": [
    {"label": "Conservative", "description": "1-2 agents, sequential execution - minimal context usage, best for simple tasks"},
    {"label": "Balanced (Recommended)", "description": "Appropriate agents for the task, some parallelism - best for most tasks"},
    {"label": "Aggressive", "description": "Max parallel agents working simultaneously - best for time-critical tasks"},
    {"label": "Auto", "description": "System decides based on task complexity"}
  ],
  "multiSelect": false
}]
```

Default to **Balanced** if not specified or if user selects Auto.

### Step 4: Specialist Mapping

Route to the appropriate specialist based on goal:

| Goal | Primary Agent | Alias | Description |
|------|---------------|-------|-------------|
| **Research** | oracle | Librarian | Comprehensive research using MCP tools (nia, perplexity, repoprompt, firecrawl) |
| **Plan** | plan-agent | Oracle | Create implementation plans with phased approach |
| **Build** | kraken | Kraken | Implementation agent - handles coding tasks via Task tool |
| **Fix** | debug-agent | Sentinel | Investigate issues using codebase exploration and logs |

**Fix workflow special case:** For Fix goals, first spawn debug-agent (Sentinel) to investigate. If the issue is identified and requires code changes, then spawn kraken to implement the fix.

### Step 5: Confirmation

Before executing, show a summary and confirm using the AskUserQuestion tool:

First, display the execution summary:

```
## Execution Summary

**Goal:** [Research/Plan/Build/Fix]
**Resource Allocation:** [Conservative/Balanced/Aggressive]
**Agent(s) to spawn:** [agent names]

**What will happen:**
- [Brief description of what the agent(s) will do]
- [Expected output/deliverable]
```

Then use the AskUserQuestion tool for confirmation:

```
questions=[{
  "question": "Ready to proceed with this workflow?",
  "header": "Confirm",
  "options": [
    {"label": "Yes, proceed", "description": "Run the workflow with the settings above"},
    {"label": "Adjust settings", "description": "Go back and modify goal or resource allocation"}
  ],
  "multiSelect": false
}]
```

Wait for user confirmation before spawning agents. If user selects "Adjust settings", return to the relevant step.

## Agent Spawn Examples

### Research (Librarian)
```
Task(
  subagent_type="oracle",
  prompt="""
  Research: [topic]

  Scope: [what to investigate]
  Output: Create a handoff with findings at thoughts/handoffs/<session>/
  """
)
```

### Plan (Oracle)
```
Task(
  subagent_type="plan-agent",
  prompt="""
  Create implementation plan for: [feature/task]

  Context: [relevant context]
  Output: Save plan to thoughts/shared/plans/
  """
)
```

### Build (Kraken)

**If plan exists:** Run pre-mortem before implementation:
```
/premortem deep <plan-path>
```

This identifies risks and blocks if HIGH severity issues found. User can accept, mitigate, or research solutions.

**After premortem passes:**
```
Task(
  subagent_type="kraken",
  prompt="""
  Implement: [task]

  Plan location: [if applicable]
  Tests: Run tests after implementation
  """
)
```

### Fix (Sentinel then Kraken)
```
# Step 1: Investigate
Task(
  subagent_type="debug-agent",
  prompt="""
  Investigate: [issue description]

  Symptoms: [what's failing]
  Output: Diagnosis and recommended fix
  """
)

# Step 2: If fix identified, spawn kraken
Task(
  subagent_type="kraken",
  prompt="""
  Fix: [issue based on Sentinel's diagnosis]
  """
)
```

## Tips

- **Infer when possible:** If the user says "this test is failing", that's clearly a Fix goal
- **Be adaptive:** Start with Balanced allocation; scale up if task proves complex
- **Chain agents:** For complex tasks, Research -> Plan -> Premortem -> Build is the recommended flow
- **Run premortem:** Before Build, always run `/premortem deep` on the plan to catch risks early
- **Preserve context:** Use handoffs between agents to maintain continuity