Workflow Designer

The Workflow Designer skill helps you design, document, and optimize multi-step AI-powered workflows. It applies process design principles to break down complex tasks into clear, executable sequences that leverage Claude Code's capabilities, external tools, and multi-agent coordination.

This skill guides you through workflow analysis, identifying optimal task decomposition, determining when to use automation versus human input, and documenting workflows in a format that's both human-readable and AI-executable. It helps you think through error handling, branching logic, validation steps, and integration points.

Use this skill when you're tackling complex, multi-step processes that could benefit from AI assistance, or when you need to transform ad-hoc procedures into repeatable, documented workflows.

Core Workflows

Workflow 1: Design New Workflow from Requirements

1. Clarify the goal:
   • What outcome is needed?
   • Who initiates the workflow?
   • What triggers completion?
2. Identify inputs and outputs:
   • What data/context is required to start?
   • What should the workflow produce?
   • What format should outputs take?
3. Map the process:
   • Break into logical phases
   • Identify decision points
   • Map dependencies between steps
   • Note parallel vs sequential operations
4. Design error handling:
   • What can go wrong at each step?
   • How should failures be handled?
   • What requires human intervention?
5. Assign responsibilities:
   • Which steps are AI-executable?
   • Which require human judgment?
   • Which need external tools/APIs?
6. Document the workflow:
   • Step-by-step instructions
   • Decision trees
   • Validation checkpoints
   • Success criteria
7. Test with sample scenario
8. Refine based on results

Workflow 2: Optimize Existing Workflow

1. Analyze current workflow:
   • Map current steps
   • Identify bottlenecks
   • Note repetitive tasks
   • Find error-prone areas
2. Identify optimization opportunities:
   • What can be automated?
   • What can be parallelized?
   • What steps are unnecessary?
   • Where are handoffs inefficient?
3. Redesign with improvements:
   • Consolidate redundant steps
   • Automate repetitive tasks
   • Parallelize independent operations
   • Add validation early
4. Compare before/after:
   • Time savings
   • Error reduction
   • Complexity changes
   • Resource requirements
5. Document changes and rationale
6. Plan migration from old to new
7. Test new workflow thoroughly

Workflow 3: Break Down Complex Task

1. Understand the complex task:
   • What makes it complex?
   • What are the components?
   • What are the constraints?
2. Decompose into subtasks:
   • Identify logical boundaries
   • Group related operations
   • Order by dependencies
3. Define interfaces:
   • Inputs for each subtask
   • Outputs from each subtask
   • Data flow between tasks
4. Assign to appropriate executors:
   • AI agents
   • External tools/MCPs
   • Human review points
5. Add coordination layer:
   • How do subtasks communicate?
   • What monitors overall progress?
   • How are results integrated?
6. Document the decomposition
7. Validate completeness

Workflow 4: Add Error Handling & Resilience

1. Map potential failure points:
   • External API failures
   • Invalid inputs
   • Resource constraints
   • Timeout scenarios
2. Design error handling strategy:
   • Graceful degradation
   • Retry logic with backoff
   • Fallback options
   • Error reporting
3. Add validation checkpoints:
   • Pre-conditions before steps
   • Post-conditions after steps
   • Intermediate result validation
4. Implement recovery mechanisms:
   • State preservation
   • Resume from checkpoint
   • Rollback procedures
5. Document error scenarios:
   • What to do when X fails
   • How to recover
   • When to escalate
6. Test failure scenarios

Quick Reference

Best Practices

• Start with Outcomes: Define success before designing steps

  • What does "done" look like?
  • What artifacts should exist?
  • What state should system be in?

• Keep Steps Atomic: Each step should be single-purpose

  • One clear action
  • One clear output
  • Easy to validate
  • Easy to replace or improve

• Make Dependencies Explicit: Show what relies on what

  • Use directed graphs or numbered dependencies
  • Identify parallelizable operations
  • Note blocking dependencies

• Plan for Failure: Every workflow has edge cases

  • What if API is down?
  • What if input is malformed?
  • What if process takes too long?
  • What if user cancels?

• Add Validation Early: Catch issues before they cascade

  • Validate inputs before processing
  • Check outputs before passing to next step
  • Verify assumptions at decision points

• Document Decision Points: Make branching logic clear

  • What triggers each path?
  • What are the criteria?
  • Who makes the decision (AI vs human)?

• Separate Concerns: Group related operations

  • Data collection phase
  • Processing phase
  • Validation phase
  • Output generation phase

• Make It Resumable: Long workflows should support interruption

  • Save state at checkpoints
  • Enable resume from last checkpoint
  • Track progress explicitly

• Test with Edge Cases: Don't just test happy path

  • Malformed inputs
  • Missing dependencies
  • Timeout scenarios
  • Concurrent execution

Workflow Design Patterns

Sequential Pipeline

Input → Step 1 → Step 2 → Step 3 → Output

Use when: Each step depends on previous step's output Example: Data ingestion → Validation → Transformation → Storage

Parallel Execution

Input → [Step 1, Step 2, Step 3] → Merge → Output

Use when: Independent operations can run concurrently Example: Lint, Test, Type-check → Aggregate results → Report

Conditional Branching

Input → Decision → [Path A | Path B] → Merge → Output

Use when: Different paths based on conditions Example: File type detection → [JSON parser | CSV parser] → Normalize

Iterative Refinement

Input → Process → Validate → [Done? → Output | Refine → Process]

Use when: Output quality improves through iterations Example: Generate code → Review → [Acceptable? → Deploy | Fix issues → Generate]

Multi-Agent Orchestration

Input → Coordinator → [Agent A, Agent B, Agent C] → Synthesizer → Output

Use when: Complex task needs specialized sub-agents Example: Feature request → Planner → [Designer, Developer, Tester] → Integrator → PR

Event-Driven Workflow

Trigger → [Handler 1, Handler 2, Handler N] → Aggregate → Output

Use when: Workflow responds to events/webhooks Example: Git push → [Build, Test, Deploy, Notify] → Status update

Workflow Documentation Template

## Workflow: [Name]

### Purpose
[What this workflow accomplishes]

### Triggers
- [What initiates this workflow]

### Inputs
- [Required inputs]
- [Optional inputs]

### Steps
1. **[Step Name]**
   - Action: [What happens]
   - Owner: [AI | Human | Tool]
   - Input: [What this step receives]
   - Output: [What this step produces]
   - Validation: [How to verify success]
   - On failure: [What to do if this fails]

2. **[Step Name]**
   [...]

### Decision Points
- **[Decision Name]**
  - Condition: [What determines the path]
  - If true: [Path A]
  - If false: [Path B]

### Outputs
- [What the workflow produces]
- [Where outputs are stored/sent]

### Success Criteria
- [How to know the workflow succeeded]

### Error Handling
- [Common failures and responses]

### Estimated Duration
- [How long this typically takes]

### Dependencies
- [External tools/services required]
- [Other workflows this depends on]

Workflow Metrics to Track

When designing workflows, consider measuring:

• Duration: How long does the workflow take?
• Success rate: What percentage complete successfully?
• Bottlenecks: Which steps take longest?
• Failure points: Where do errors occur most?
• Retry rate: How often do steps need retrying?
• Human intervention: How often is manual action needed?
• Resource usage: What's the computational/financial cost?

Common Pitfalls

• Over-engineering: Don't add complexity for problems that haven't occurred
• Under-specifying: Don't assume steps are obvious without documentation
• Ignoring failures: Don't design only for happy path
• Tight coupling: Don't make steps too dependent on implementation details
• No rollback: Don't make destructive operations irreversible
• Missing validation: Don't pass bad data between steps
• Sequential when parallel works: Don't serialize independent operations
• No progress tracking: Don't make long workflows black boxes