workflow-router
Determines user goals to minimize redundancy and routes tasks to specialist agents based on identified project plans.
Install
mkdir -p .claude/skills/workflow-router && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/4069" && unzip -o skill.zip -d .claude/skills/workflow-router && rm skill.zipInstalls to .claude/skills/workflow-router
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Goal-based workflow orchestration - routes tasks to specialist agents based on user goalsKey capabilities
- →Identify user goals among Research, Plan, Build, or Fix
- →Detect existing project plans to prevent redundancy
- →Allocate resources based on task complexity
- →Route tasks to specialist agents like oracle, kraken, or sentinel
- →Execute pre-mortem analysis for build tasks
- →Confirm workflow settings with users before execution
How it works
The router determines the user's goal, checks for existing plans, selects an appropriate resource allocation level, and routes the task to the corresponding specialist agent.
Inputs & outputs
When to use workflow-router
- →Initiating a new development task
- →Architecting a solution for a complex problem
- →Identifying the next steps for debugging
- →Structuring a build process
About this skill
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:
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 deepon the plan to catch risks early - Preserve context: Use handoffs between agents to maintain continuity
When not to use it
- →When the user has already specified a workflow
- →When the task is trivial and does not require orchestration
Limitations
- →Requires explicit user confirmation before spawning agents
- →Fix workflow requires a two-step process involving the debug-agent and kraken
How it compares
Unlike manual agent selection, this skill automates the mapping of goals to specific specialist agents and enforces a structured pre-execution confirmation process.
Compared to similar skills
workflow-router side by side with the closest alternatives in the catalog.
Try saying
Example prompts that trigger this skill in your AI assistant.
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