arela/WORKFLOW_INTEGRATION.md

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# Windsurf Workflow Integration - Complete

**Date:** 2025-11-14  
**Status:** ✅ COMPLETE

## What We Built

Integrated Windsurf's workflow system into Arela to provide structured, repeatable processes for common development tasks.

## Key Achievement

Created our **first workflow**: `/research-driven-decision` - A systematic approach to making evidence-based technical decisions.

## Files Created

### 1. Workflow Definition
**Location:** `.windsurf/workflows/research-driven-decision.md`

**Purpose:** Guides Cascade through the research-driven decision making process

**How to use:**
```
/research-driven-decision
```

**Features:**
- Identifies when research is needed
- Generates structured research prompts
- Guides user through ChatGPT + Gemini research
- Reviews findings together
- Implements with documented rationale
- Creates memory of decision

### 2. Documentation
**Location:** `docs/workflows.md`

**Contents:**
- What are workflows
- How to use them
- How to create new ones
- Best practices
- Integration with Arela rules
- Future workflow ideas

### 3. Template Integration
**Location:** `src/persona/templates/workflows/research-driven-decision.md`

**Purpose:** Automatically copied to new projects during `arela init`

### 4. Code Updates
**Location:** `src/persona/loader.ts`

**Changes:**
- Creates `.windsurf/workflows/` directory during init
- Copies workflow templates to new projects
- Ensures workflows are available immediately

## How It Works

### Workflow Discovery

Windsurf automatically discovers workflows from:
- `.windsurf/workflows/` in current workspace
- `.windsurf/workflows/` in sub-directories
- `.windsurf/workflows/` in parent directories (up to git root)

### Workflow Invocation

In Cascade, simply type:
```
/research-driven-decision
```

Cascade will guide you through the workflow steps.

### Workflow Structure

```markdown
# Workflow Title

**Description:** Brief description

## When to Trigger
[Conditions for using this workflow]

## Workflow Steps
### 1. Step Name
[Instructions]

## Benefits
[Why this workflow is valuable]

## Example
[Real-world example]
```

## Research-Driven Decision Workflow

### When to Use

**Trigger research when:**
- Current implementation seems suboptimal
- Choosing between multiple algorithms/approaches
- Encountering known limitations
- Considering major architectural changes
- Evaluating new technologies

**Don't trigger for:**
- Simple bug fixes
- Obvious implementation errors
- Standard patterns with clear best practices
- Minor refactoring

### The Process

1. **Identify Decision Point**
   - What's the problem?
   - Why is current approach suboptimal?
   - What alternatives exist?

2. **Generate Research Prompt**
   ```markdown
   # Research Request: [Topic]
   
   ## Context
   [Problem description]
   
   ## Our Specific Use Case
   [Requirements, constraints, scale]
   
   ## Research Questions
   1. [Primary question]
   2. [Comparisons needed]
   3. [Edge cases]
   
   ## What We Need
   - Validation/disproval
   - Benchmarks
   - Implementation guidance
   
   ## Success Criteria
   [What makes a solution "good"]
   ```

3. **User Runs Research**
   - Paste prompt into ChatGPT + Gemini
   - Save results to `RESEARCH/` folder
   - Share findings with Cascade

4. **Review Together**
   - Discuss trade-offs
   - Make informed decision
   - Document rationale

5. **Implement**
   - Execute based on research
   - Reference research in code
   - Create ADR if major decision

6. **Document**
   - Create memory
   - Update relevant docs

### Real-World Success: CASCADE-003

**Problem:** Louvain algorithm returning 13 singletons instead of 5 slices

**Research Prompt:**
```markdown
# Deep Research Request: Optimal Community Detection for Codebase Vertical Slice Detection

## Context
Louvain clustering fails on small, dense codebase graphs (10-50 nodes)

## Our Use Case
- Small graphs (10-50 files)
- Dense subgraphs (feature directories)
- Directed edges (imports)
- Expected output: 4-6 feature slices

## Research Questions
1. Is Infomap optimal for this use case?
2. How does it compare to Leiden?
3. How to handle edge cases?
4. What are the hyperparameters?

## What We Need
- Academic evidence for/against Infomap
- Comparative benchmarks
- Practical implementation guidance
```

**Result:** 
- Comprehensive research from Gemini + ChatGPT
- Switched from Louvain to Infomap
- Successful implementation in ~1 hour
- Now detects 6 slices correctly!

**Without research:** Would have wasted time trying to fix Louvain's ΔQ calculation  
**With research:** Immediately pivoted to better algorithm

## Benefits

✅ **Evidence-based decisions** - Not guessing or using outdated knowledge  
✅ **Parallel research** - ChatGPT + Gemini provide multiple perspectives  
✅ **Documented rationale** - Research files serve as ADRs  
✅ **Continuous learning** - Build knowledge base over time  
✅ **Reduced risk** - Avoid costly wrong decisions  

## Integration with Arela

### Complements Rules System

- **Rules** provide persistent context at the prompt level
- **Workflows** provide structured sequences at the trajectory level

Together they enable:
- Consistent decision-making processes
- Repeatable quality gates
- Knowledge sharing across team
- Continuous improvement

### Aligns with Arela Principles

- **Two-Way Door Decisions** - Research helps identify Type 1 vs Type 2
- **Technical Debt Management** - Prevents inadvertent reckless debt
- **ADR Discipline** - Research becomes the ADR foundation
- **Pragmatic Visionary** - Build for users, validate with research

## Future Workflows

Planned workflows for Arela:

### `/contract-validation`
Automate OpenAPI contract validation with Dredd

### `/slice-extraction`
Guide through vertical slice extraction process

### `/architecture-review`
Run comprehensive architecture quality checks

### `/deployment-checklist`
Ensure all pre-deployment steps are complete

### `/security-scan`
Trigger security vulnerability scans

### `/test-generation`
Generate Testcontainers-based integration tests

## Usage Examples

### In New Projects

```bash
# Initialize Arela
arela init

# Workflows automatically created in .windsurf/workflows/
# Ready to use immediately!

# In Cascade:
/research-driven-decision
```

### In Existing Projects

Workflows already exist in `.windsurf/workflows/` and are ready to use.

### Creating Custom Workflows

1. **Via Cascade UI:**
   - Click Customizations icon
   - Navigate to Workflows panel
   - Click + Workflow button

2. **Via Cascade Command:**
   ```
   Create a workflow for [task description]
   ```

3. **Manually:**
   - Create `.windsurf/workflows/my-workflow.md`
   - Follow workflow structure template
   - Use immediately with `/my-workflow`

## Best Practices

### Do's
✅ Use workflows for repetitive processes  
✅ Keep steps clear and actionable  
✅ Include examples and context  
✅ Document when to use vs when not to use  
✅ Reference related Arela rules  

### Don'ts
❌ Don't create workflows for one-off tasks  
❌ Don't make steps too vague  
❌ Don't skip the "when to trigger" section  
❌ Don't exceed 12,000 character limit  

## Technical Implementation

### Directory Structure
```
.windsurf/
├── rules/              # Arela rules (persistent context)
│   ├── arela-cto.md
│   ├── 010-pragmatic-visionary.md
│   └── ...
└── workflows/          # Arela workflows (structured processes)
    └── research-driven-decision.md
```

### Init Process

When running `arela init`:
1. Creates `.windsurf/rules/` directory
2. Creates `.windsurf/workflows/` directory
3. Copies persona and rules based on preset
4. Copies all workflow templates
5. Creates `.arela/tickets/` structure

### Build Process

```bash
npm run build
# Compiles TypeScript
# Copies templates (including workflows) to dist/
```

## Memory Integration

Updated memory: **Research-Driven Decision Making Protocol**

**Location:** Windsurf Memories  
**Tags:** workflow, research, decision_making, best_practices, arela_process

**Contents:**
- When to trigger research
- Research prompt template
- Workflow steps
- Example success (CASCADE-003)
- Benefits and integration

## Documentation

- ✅ Workflow definition: `.windsurf/workflows/research-driven-decision.md`
- ✅ User documentation: `docs/workflows.md`
- ✅ Integration summary: `WORKFLOW_INTEGRATION.md` (this file)
- ✅ Memory updated with workflow reference
- ✅ Code updated to support workflows

## Testing

✅ Build passes  
✅ Workflow file created  
✅ Template copied  
✅ Init process updated  
✅ Documentation complete  

## Next Steps

### Immediate
- Test workflow in real scenario
- Get user feedback
- Refine based on usage

### Future
- Create additional workflows (contract-validation, slice-extraction, etc.)
- Build workflow library
- Share workflows across team
- Integrate with CI/CD

## Resources

- [Windsurf Workflows Documentation](https://docs.windsurf.com/windsurf/cascade/workflows)
- [Arela Workflows Documentation](docs/workflows.md)
- [Research-Driven Decision Workflow](.windsurf/workflows/research-driven-decision.md)
- [Arela Rules Documentation](README.md#rules)

## Summary

**What we achieved:**
- ✅ Integrated Windsurf workflow system into Arela
- ✅ Created first workflow: Research-Driven Decision Making
- ✅ Updated init process to include workflows
- ✅ Documented everything comprehensively
- ✅ Tested and validated

**Impact:**
- Systematic approach to technical decisions
- Evidence-based recommendations
- Repeatable quality processes
- Knowledge sharing across projects
- Reduced risk of wrong decisions

**This is the foundation for a library of Arela workflows that will guide teams through complex development processes!** 🎯