aiskillstore/quetrex-development-workflow

Quetrex Development Workflow Skill

Purpose: Bootstrap new Claude Code sessions with complete Quetrex project context and enable efficient issue-driven development.

When to Use:

• At the start of any new Claude Code session working on Quetrex
• When you need to understand what work is pending
• When creating issues for AI agent automation
• When deciding what to work on next

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Quick Reference

Key Commands

# Query pending issues
gh issue list --label "ai-feature" --state open

# Query recent completed work
gh pr list --state merged --limit 10

# Create issue for AI agent
gh issue create --template ai-feature.md --label ai-feature

# Trigger workflow manually
gh workflow run "Quetrex AI Agent Worker" -f issue_number=123

Critical Files

| File | Purpose | |------|---------| | CLAUDE.md | Project context (loaded every session) | | .quetrex/status.yml | Current roadmap position | | docs/PROJECT-CHECKLIST.md | Comprehensive task checklist | | .github/workflows/ai-agent.yml | Agent automation workflow | | .claude/scripts/ai-agent-worker.py | Agent execution script |

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1. What is Quetrex?

Quetrex is a voice-first AI agent control center - a mission control dashboard for managing multiple AI-powered projects.

Core Capabilities

• Voice-driven requirements gathering (OpenAI Realtime API)
• Automatic spec generation (Claude AI)
• Spec approval workflow with versioning
• Automated agent spawning via GitHub Actions
• Real-time monitoring and analytics
• Cost tracking and controls
• Security-first architecture (3-phase model)

Tech Stack

• Frontend: Next.js 15.5, React 19, TypeScript (strict), TailwindCSS, ShadCN UI
• Backend: Next.js API Routes, Drizzle ORM, PostgreSQL
• AI/Voice: Claude Sonnet 4.5, OpenAI Realtime API, Whisper, TTS
• Infrastructure: Vercel Edge Runtime, Docker containers, GitHub Actions

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2. How the Automation Works

Trigger Flow

1. Create GitHub Issue
   └─> Use "AI Feature Request" template
   └─> Add "ai-feature" label

2. GitHub Actions Triggers
   └─> .github/workflows/ai-agent.yml activates
   └─> Runs in secure Docker container

3. Agent Worker Executes
   └─> Fetches issue details
   └─> Loads project context from .quetrex/memory/
   └─> Builds comprehensive prompt
   └─> Executes via Claude Code CLI

4. Implementation Phase
   └─> Agent uses specialized sub-agents:
       - orchestrator (complex features)
       - test-writer (TDD first)
       - implementation (code)
       - code-reviewer (quality)

5. Quality Gates
   └─> PreToolUse: Blocks dangerous commands
   └─> PostToolUse: Validates changes
   └─> Stop: Unbypassable final gate
   └─> Tests, coverage, linting, build

6. Pull Request Created
   └─> Feature branch pushed
   └─> PR with detailed description
   └─> Ready for human review

Constraints Per Issue

• Max execution time: 45 minutes
• Max API calls: 150
• Max file changes: 75
• Tests required: Configurable (currently false)

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3. Creating Effective Issues

Issue Template Location

.github/ISSUE_TEMPLATE/ai-feature.md

What Makes a Good AI-Agent Issue

DO:

• Be specific about what needs to be built
• List acceptance criteria as checkboxes
• Reference existing files/patterns to follow
• Specify testing requirements
• Include priority level

DON'T:

• Be vague ("make it better")
• Combine multiple unrelated tasks
• Skip acceptance criteria
• Forget to add ai-feature label

Example Well-Structured Issue

## Summary
Add cost tracking display to project cards in dashboard

## Description
Each project card should show the current month's API costs
with a small trend indicator (up/down arrow).

## Acceptance Criteria
- [ ] Cost displayed in USD format ($X.XX)
- [ ] Trend arrow shows increase/decrease from last week
- [ ] Tooltip shows breakdown by provider (OpenAI/Anthropic)
- [ ] Updates every 5 minutes via React Query

## Technical Context
**Relevant Files:**
- `src/components/ProjectCard.tsx` - Add cost display
- `src/services/cost-tracker.ts` - Use existing service
- `src/hooks/useDashboard.ts` - Add cost query

**Patterns to Follow:**
- Use existing stats display pattern from dashboard header
- Follow cost formatting from SettingsPanel

## Testing Requirements
- [x] Unit tests required (cost formatting)
- [x] Integration tests required (API integration)
- [ ] E2E tests required
- [ ] Visual regression tests required

## Priority
- [x] P1 - High (needed soon)

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4. Current Project Status

How to Query Live State

# Open issues ready for AI agent
gh issue list --label "ai-feature" --state open --json number,title,labels

# Recently completed work
gh pr list --state merged --limit 5 --json number,title,mergedAt

# Current branch status
git status
git log --oneline -5

Status File Location

.quetrex/status.yml - Maintained snapshot of:

• Current phase
• Active focus areas
• Completion percentages
• Recent milestones

Project Checklist

docs/PROJECT-CHECKLIST.md - Comprehensive tracking:

• Feature completion by category
• Blockers and dependencies
• Priority levels
• Time estimates

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5. Architecture Decisions

Key ADRs to Know

| ADR | Decision | Status | |-----|----------|--------| | ADR-001 | Browser native echo cancellation | Accepted | | ADR-002 | Drizzle ORM for Edge Runtime | Accepted | | ADR-006 | Claude Code CLI over Anthropic SDK | Active |

Security Architecture (3-Phase)

1. Phase 1 (Complete): Docker containerization

   • Read-only filesystem, non-root user
   • Resource limits, capability dropping

2. Phase 2 (In Production): Credential proxy

   • No credentials in container environment
   • Unix socket validation, audit logging

3. Phase 3 (Q1 2026): gVisor migration

   • User-space kernel for maximum isolation

Agent Execution Architecture

We use Claude Code CLI, NOT direct Anthropic SDK.

Reasons:

• Built-in specialized agents (orchestrator, test-writer, code-reviewer)
• Quality hooks (PreToolUse, PostToolUse, Stop)
• Automatic updates from Anthropic
• No maintenance burden for tool execution

See: .claude/docs/ARCHITECTURE-AGENT-WORKER.md

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6. Quality Enforcement

6-Layer Defense System

1. PreToolUse Hook - Blocks dangerous commands
2. PostToolUse Hook - Validates every file change
3. Stop Hook - Unbypassable quality gate
4. TypeScript Strict - No any, no @ts-ignore
5. Test Coverage - 75%+ overall, 90%+ services
6. CI/CD - Prevents merge if any check fails

Test Requirements

Overall:        75%+ (enforced)
src/services/: 90%+ (enforced)
src/utils/:    90%+ (enforced)
Components:    60%+ (enforced)

TDD Workflow (Mandatory)

1. Write test describing behavior
2. Verify test FAILS (red)
3. Write minimal code to pass
4. Verify test PASSES (green)
5. Refactor while keeping green

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7. Development Patterns

File Organization

src/
├── app/           # Next.js App Router pages
├── components/    # React components
├── services/      # Business logic (90%+ coverage)
├── hooks/         # Custom React hooks
├── lib/           # Third-party integrations
├── db/            # Database schema (Drizzle)
└── schemas/       # Zod validation schemas

Naming Conventions

• Components: PascalCase.tsx
• Services: kebab-case.ts
• Hooks: useCamelCase.ts
• Types: Adjacent types.ts or inline

Import Order

1. External packages
2. Internal components (@/components/)
3. Hooks (@/hooks/)
4. Services (@/services/)
5. Types

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8. Common Workflows

Starting a New Feature

# 1. Check what's pending
gh issue list --label "ai-feature" --state open

# 2. If nothing suitable, create new issue
gh issue create --template ai-feature.md

# 3. Add label to trigger automation
gh issue edit <number> --add-label "ai-feature"

# 4. Or work on it directly from here
# (for complex features or when you want more control)

Reviewing AI Agent Work

# Check recent PRs
gh pr list --author "github-actions[bot]" --state open

# Review specific PR
gh pr view <number>
gh pr diff <number>

# Merge if approved
gh pr merge <number> --squash

Debugging Failed Runs

# List recent workflow runs
gh run list --workflow="ai-agent.yml" --limit 5

# View specific run
gh run view <run-id>

# Download logs
gh run download <run-id> -n agent-logs-<issue-number>

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9. Memory System

Location: .quetrex/memory/

| File | Purpose | |------|---------| | patterns.md | Architectural patterns to follow | | project-overview.md | High-level project context | | PHASE_3_EVOLVER.md | Phase 3 documentation | | ARCHITECTURE-INTELLIGENCE-SYSTEM.md | Architecture intelligence |

Status Tracking: .quetrex/status.yml

Updated after each session with:

• Current focus area
• Recent completions
• Pending priorities
• Blockers

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10. Getting Help

Documentation Locations

• Architecture: docs/architecture/
• Features: docs/features/
• Roadmap: docs/roadmap/
• ADRs: docs/decisions/

Key Documents

| Document | Purpose | |----------|---------| | CLAUDE.md | Primary project context | | docs/PROJECT-CHECKLIST.md | Comprehensive task list | | docs/AI-AGENT-AUTOMATION-STATUS.md | Agent system status | | docs/CONTRIBUTING.md | Development standards |

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Session Checklist

When starting a new session:

• [ ] Run /new-context to load this skill and query state
• [ ] Review pending issues (gh issue list --label ai-feature)
• [ ] Check recent PRs for context on recent work
• [ ] Decide: Create issue for agent OR work directly
• [ ] Follow TDD: Write tests FIRST
• [ ] Use specialized agents for complex features
• [ ] Update .quetrex/status.yml before ending session

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Last Updated: 2025-11-26 Created by Glen Barnhardt with help from Claude Code