Feature Workflow Generation

Feature Workflow Generation

Purpose

Generate feature-by-feature implementation workflows that map features from features.json to tech-specific commands from the project's tech stack.

When to Use

• After creating features with /planning:add-feature
• When you need a structured workflow for implementing features
• To generate execution roadmaps from specifications

How It Works

Data Sources

1. features.json - List of features with metadata
2. specs/{feature-id}/spec.md - Detailed feature specifications
3. .claude/project.json - Tech stack configuration
4. Airtable - Available commands for the tech stack

Workflow

features.json → Read features
      ↓
specs/ → Read specifications
      ↓
.claude/project.json → Get tech stack
      ↓
Airtable → Query available commands
      ↓
generate-feature-workflow.py → Combine data
      ↓
FEATURE-IMPLEMENTATION-WORKFLOW.md

Script Usage

Basic Usage

cd /path/to/project
python3 scripts/generate-feature-workflow.py

Output Format

{
  "tech_stack": "AI Tech Stack 1",
  "features": [
    {
      "id": "F001",
      "title": "AI chat interface",
      "status": "in-progress",
      "priority": "P0",
      "spec_content": "..."
    }
  ],
  "available_commands": [
    {
      "name": "add-component",
      "description": "Add Next.js component",
      "plugin": "nextjs-frontend",
      "phase": "Implementation"
    }
  ]
}

Feature-to-Command Mapping

Mapping Strategies

1. Keyword Matching

• "Create chat component" → /nextjs-frontend:add-component ChatWindow
• "Add streaming" → /vercel-ai-sdk:add-streaming
• "Setup auth" → /supabase:add-auth

2. Phase-Based Grouping

• Foundation - Infrastructure setup
• Planning - Architecture and specs
• Implementation - Feature building
• Quality - Validation
• Testing - Test execution
• Deployment - Production deployment

3. Dependency Analysis

• Database commands before backend
• Backend before frontend
• Core components before features
• Integration after all components

Workflow Document Structure

Template

# Feature Implementation Workflow

Generated from features.json and {TECH_STACK}

## Feature: {FEATURE_ID} - {FEATURE_TITLE}
**Status**: {STATUS}
**Priority**: {PRIORITY}

### Prerequisites
- [ ] Spec complete: specs/{FEATURE_ID}/spec.md
- [ ] Tasks layered: /iterate:tasks {FEATURE_ID}

### Implementation Steps

#### Layer 0: Infrastructure
- [ ] {COMMAND_1}
- [ ] {COMMAND_2}

#### Layer 1: Core Components
- [ ] {COMMAND_3}
- [ ] {COMMAND_4}

#### Layer 2: Feature Components
- [ ] {COMMAND_5}
- [ ] {COMMAND_6}

#### Layer 3: Integration
- [ ] {COMMAND_7}
- [ ] {COMMAND_8}

### Validation
- [ ] /quality:validate-code {FEATURE_ID}
- [ ] /testing:test {FEATURE_ID}

---

## Feature: {NEXT_FEATURE_ID} - {NEXT_FEATURE_TITLE}
...

Filtering Options

By Feature ID

--feature F001

Only generate workflow for F001

By Priority

--priority P0

Only generate for P0 features

By Status

--status in-progress

Only generate for in-progress features

Split by Feature

--split

Generate separate files:

• F001-WORKFLOW.md
• F002-WORKFLOW.md
• etc.

Integration with Other Commands

Typical Flow

# 1. Create features
/planning:add-feature "AI chat interface"
/planning:add-feature "User dashboard"

# 2. Generate feature workflow
/planning:generate-feature-workflow

# 3. Execute workflows
/iterate:tasks F001
/implementation:execute F001

/iterate:tasks F002
/implementation:execute F002

# 4. Validate
/quality:validate-code F001
/testing:test F001

Error Handling

Missing features.json

Error: "No features found in features.json" Solution: Run /planning:add-feature first

Missing project.json

Error: "Tech stack not found in .claude/project.json" Solution: Run /foundation:detect first

Airtable Access Failure

Error: "AIRTABLE_TOKEN environment variable not set" Solution: Export token:

export MCP_AIRTABLE_TOKEN=your_token_here

Fallback Mode

If Airtable access fails, fall back to filesystem-based command discovery:

# Read commands from .claude/plugins/**/commands/*.md
ls .claude/plugins/*/commands/*.md

Best Practices

1. Update features.json regularly - Keep status current
2. Maintain spec files - Complete specs improve matching
3. Use priority levels - P0 for critical features
4. Generate before implementation - Plan before executing
5. Re-generate after spec changes - Keep workflow current

Differences from Foundation Workflow

Examples

Example 1: AI Chat Application

features.json:

{
  "features": [
    {
      "id": "F001",
      "title": "AI chat interface",
      "priority": "P0",
      "status": "in-progress"
    }
  ]
}

Generated Workflow:

## Feature: F001 - AI chat interface
**Status**: in-progress
**Priority**: P0

### Implementation Steps
- [ ] /iterate:tasks F001
- [ ] /nextjs-frontend:add-component ChatWindow
- [ ] /vercel-ai-sdk:add-streaming
- [ ] /fastapi-backend:add-endpoint "POST /api/chat"
- [ ] /supabase:add-auth
- [ ] /mem0:add-conversation-memory
- [ ] /quality:validate-code F001
- [ ] /testing:test F001

Example 2: Multiple Features with Filtering

# Generate only P0 features
/planning:generate-feature-workflow --priority P0

# Generate only in-progress features
/planning:generate-feature-workflow --status in-progress

# Generate specific feature
/planning:generate-feature-workflow --feature F001

# Generate separate files per feature
/planning:generate-feature-workflow --split

Maintenance

Keeping Workflow Current

# After adding new features
/planning:add-feature "New feature"
/planning:generate-feature-workflow

# After updating specs
vim specs/F001/spec.md
/planning:generate-feature-workflow --feature F001

# After changing priority
# Edit features.json
/planning:generate-feature-workflow

Validation

The workflow includes validation warnings:

• Features without specs
• Specs without implementation tasks
• Commands not available in tech stack
• Missing dependencies

Technical Details

Script: generate-feature-workflow.py

Dependencies:

• requests - HTTP requests to Airtable
• json - JSON parsing
• os - File system operations

Environment Variables:

• AIRTABLE_TOKEN or MCP_AIRTABLE_TOKEN - Required for Airtable access

Exit Codes:

• 0 - Success
• 1 - Error (missing data, API failure)

Airtable Schema

Tables Used:

• Tech Stacks (tblG07GusbRMJ9h1I)
• Plugins (tblVEI2x2xArVx9ID)
• Commands (tblWKaSceuRJrBFC1)

Relationships:

Tech Stack → Plugins → Commands

Related Skills

• workflow-generation (foundation) - Infrastructure workflows
• spec-management (planning) - Spec creation and management
• task-management (iterate) - Task layering and execution
• execution-tracking (implementation) - Progress tracking