ai-workflow-builder

name	ai-workflow-builder
description	Design AI automation workflows with clear inputs, outputs, tools, and implementation steps. TRIGGERS - Use this skill when: - User wants to design an AI automation workflow - User mentions automating a business process with AI - User wants to map out an AI-powered system - User asks how to automate something using AI tools - User mentions n8n, Make, Zapier, or any automation platform

AI Workflow Builder

Overview

Designs complete AI automation workflows from business process to implementation plan. Maps out triggers, steps, AI components, integrations, and expected outcomes.

Workflow

Step 1: Understand the Process

Ask the user:

1. What process do you want to automate?
2. Current state: How is it done manually today?
3. Volume: How often does this happen? (daily, weekly, per client)
4. Time spent: How long does the manual process take?
5. Tools involved: What software/platforms are already in use?
6. Desired outcome: What does "automated" look like?

Step 2: Map the Current Workflow

Document the as-is process:

TRIGGER → Step 1 → Step 2 → ... → OUTPUT

Identify:

• Manual steps (candidate for automation)
• Decision points (needs AI judgment or rules)
• Data sources (where information comes from)
• Handoff points (where humans need to review)
• Bottlenecks (where delays happen)

Step 3: Design the AI Workflow

For each step, determine:

Step	Manual Task	AI Solution	Tool/Platform	Human Review?
1	...	...	...	Yes/No
2	...	...	...	Yes/No

AI Component Selection Guide:

Task Type	Recommended AI Approach
Text generation	LLM (Claude, GPT)
Data extraction	LLM with structured output
Classification	LLM or fine-tuned classifier
Image processing	Vision AI (Claude, GPT-4V)
Voice/transcription	Whisper, Deepgram
Data lookup	RAG or API integration
Decision making	Rule engine + LLM fallback
Scheduling	Calendar API + automation

Step 4: Select Automation Platform

Platform	Best For	Complexity
Zapier	Simple 2-5 step workflows	Low
Make (Integromat)	Visual complex workflows	Medium
n8n	Self-hosted, developer-friendly	Medium-High
Custom code	Full control, unique logic	High
LangChain/CrewAI	Multi-agent AI workflows	High

Step 5: Build the Architecture

Create the workflow diagram:

[TRIGGER]
    ↓
[STEP 1: Input Processing]
    → Tool: [platform/API]
    → AI: [model + prompt purpose]
    → Output: [what this step produces]
    ↓
[STEP 2: AI Processing]
    → Tool: [platform/API]
    → AI: [model + prompt purpose]
    → Output: [what this step produces]
    ↓
[DECISION: Human Review Needed?]
    → YES → [Notify human via Slack/email] → Wait for approval → Continue
    → NO → Continue
    ↓
[STEP 3: Action/Output]
    → Tool: [destination platform]
    → Action: [what happens]
    ↓
[COMPLETE: Log result]

Step 6: Write the Prompts

For each AI step, create:

1. System prompt — role and context
2. Input template — dynamic variables from previous steps
3. Output format — structured JSON or specific format
4. Fallback — what happens if AI fails

Step 7: Implementation Plan

Provide:

1. Phase 1 (Week 1): Set up accounts, connect tools
2. Phase 2 (Week 2): Build core automation flow
3. Phase 3 (Week 3): Add AI components and test
4. Phase 4 (Week 4): Monitor, refine, go live

Output Format

# AI Workflow: [Process Name]

## Summary
- **Process**: [what's being automated]
- **Time saved**: [X hours/week]
- **Tools**: [platforms used]
- **AI models**: [which AI and for what]

## Current vs Automated

| | Manual | Automated |
|--|--------|-----------|
| Time per task | Xm | Xm |
| Tasks/day | X | X |
| Error rate | X% | X% |
| Monthly cost | $X | $X |

## Workflow Architecture

[Visual flow diagram using text/arrows]

## Step-by-Step Breakdown

### Step 1: [Name]
- **Trigger**: [what kicks this off]
- **Input**: [data coming in]
- **Process**: [what happens]
- **AI prompt**: [if applicable]
- **Output**: [what's produced]
- **Tool**: [platform/integration]

[Repeat for each step]

## Prompts & Templates

### [AI Step Name] — System Prompt

[Full prompt text]

### [AI Step Name] — Input Template

[Template with {{variables}}]

## Implementation Plan
[Phased rollout]

## Monitoring & Maintenance
- KPIs to track
- Common failure points
- Escalation triggers

Quality Checklist

• Every manual step has an automated equivalent
• Human review points identified for critical decisions
• AI prompts are specific with structured outputs
• Fallback/error handling defined for each step
• ROI estimate included (time/money saved)
• Implementation is phased and realistic