claude-flow-novice
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Claude Flow Novice - Advanced orchestration platform for multi-agent AI workflows with CFN Loop architecture Includes CodeSearch (hybrid SQLite + pgvector), mem0/memgraph specialists, and all CFN skills.
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# CFN Dev Team Agent Structure
## Overview
The CFN (Claude Flow Novice) development team comprises 23 production agents organized into 4 strategic categories, designed to provide comprehensive software development and workflow management capabilities.
## Directory Structure
### Coordinators
- **Purpose**: CFN Loop orchestration and workflow management
- **Key Agents**:
- `cfn-v3-coordinator`: Primary workflow orchestrator
### Developers
- **Purpose**: Core implementation and creative problem-solving
- **Key Agents**:
- `coder`: Production code implementation
- `backend-dev`: Backend system design and implementation
- `researcher`: Technical research and solution exploration
- `architect`: System design and architectural planning
- `agent-builder`: Agent template and workflow design
### Reviewers
- **Purpose**: Quality assurance and code validation
- **Key Agents**:
- `reviewer`: Code review and quality assessment
- `code-analyzer`: Static code analysis
- `code-quality-validator`: Comprehensive code quality checks
- `security-specialist`: Security vulnerability detection
### Testers
- **Purpose**: Comprehensive testing and validation
- **Key Agents**:
- `tester`: General test strategy and implementation
- `playwright-tester`: Web interaction testing
- `interaction-tester`: User interaction validation
- `production-validator`: Production readiness checks
- `perf-analyzer`: Performance testing and optimization
## Agent Selection Guide
### When to Use Each Category
1. **Coordinators**
- Complex workflow orchestration
- Multi-agent collaboration scenarios
- CFN Loop management
2. **Developers**
- Initial implementation
- Feature development
- Technical problem-solving
- Prototype creation
3. **Reviewers**
- Post-implementation code review
- Quality gate validation
- Security and performance analysis
- Refactoring recommendations
4. **Testers**
- Comprehensive test strategy
- Automated testing
- Production validation
- Performance optimization
## CFN Loop Integration
### Loop Participation Levels
- **Loop 2**: Preliminary design and research
- **Loop 3**: Implementation and initial validation
- **Loop 4**: Advanced testing and production readiness
## Naming Conventions
- All agents follow the `cfn-dev-team` namespace
- Naming format: `category-specific-role`
- Example: `developer-backend-specialist`
## Agent Template Requirements
Each agent MUST include:
1. **Name**: Unique, descriptive identifier
2. **Description**: Clear purpose and capabilities
3. **Tools**: Permitted interaction tools
4. **Model**: Assigned AI model
5. **Capabilities**: Specific functional areas
6. **Lifecycle Hooks**:
- SQLite tracking
- Redis coordination
7. **ACL Level**: Access control level (1-5)
## Multi-Worktree Coordination
### Environment Variables Provided by Coordinator
When coordinators spawn agents in multi-worktree environments, the following environment variables are automatically injected:
```bash
COMPOSE_PROJECT_NAME="cfn-feature-auth" # Unique project name per branch
CFN_REDIS_PORT=6421 # Redis port (6379 + offset)
CFN_POSTGRES_PORT=5474 # Postgres port (5432 + offset)
WORKTREE_BRANCH="feature-auth" # Git branch name
```
### Service Discovery Pattern
Agents use Docker service names for internal communication:
```bash
# Redis connection (within Docker network)
redis-cli -h redis -p 6379
# PostgreSQL connection (within Docker network)
PGHOST=postgres PGPORT=5432 psql -U postgres
# HTTP connections
curl http://orchestrator:3001/health
```
**Important**: Service names are resolved by Docker's internal DNS within the network. Container names (e.g., `cfn-redis-1`) will NOT resolve.
### Multi-Worktree Examples
#### Scenario 1: Feature Development (Feature-Auth Branch)
```bash
# Branch: feature-auth
# Offset: ~42 (calculated from branch name)
# Ports: Redis=6421, Postgres=5474, Orchestrator=3043
# Coordinator injects:
export COMPOSE_PROJECT_NAME="cfn-feature-auth"
export CFN_REDIS_PORT=6421
export CFN_POSTGRES_PORT=5474
# Agent connects to correct worktree services
redis-cli -h redis -p 6421
psql -h postgres -p 5474
```
#### Scenario 2: Bugfix Work (Bugfix-Validation Branch)
```bash
# Branch: bugfix-validation
# Offset: ~78 (calculated from branch name)
# Ports: Redis=6457, Postgres=5510, Orchestrator=3079
# Coordinator injects:
export COMPOSE_PROJECT_NAME="cfn-bugfix-validation"
export CFN_REDIS_PORT=6457
export CFN_POSTGRES_PORT=5510
# Agent connects to correct worktree services
redis-cli -h redis -p 6457
psql -h postgres -p 5510
```
#### Scenario 3: Main Branch (Production Ready)
```bash
# Branch: main/master
# Offset: 0 (main gets priority)
# Ports: Redis=6379, Postgres=5432, Orchestrator=3001
# Coordinator injects:
export COMPOSE_PROJECT_NAME="cfn-main"
export CFN_REDIS_PORT=6379
export CFN_POSTGRES_PORT=5432
# Standard ports - no offset
redis-cli -h redis -p 6379
psql -h postgres -p 5432
```
### Running Multiple Worktrees Simultaneously
Team members can develop in parallel without conflicts:
```bash
# Developer 1: Feature branch
worktree_1/feature-auth$ ./scripts/docker/run-in-worktree.sh up -d
# Ports: Redis=6421, Postgres=5474
# Developer 2: Bugfix branch (same machine)
worktree_2/bugfix-validation$ ./scripts/docker/run-in-worktree.sh up -d
# Ports: Redis=6457, Postgres=5510
# Developer 3: Main branch
worktree_3/main$ ./scripts/docker/run-in-worktree.sh up -d
# Ports: Redis=6379, Postgres=5432
# All three run simultaneously without port conflicts!
```
## Adding New Agents
### Process
1. Use `agent-builder` for initial template creation
2. Follow agent template structure
3. Validate against CFN Loop coordination patterns
4. Submit for team review
5. Integrate into appropriate category
### Validation Checklist
- [ ] Unique name and purpose
- [ ] Defined capabilities
- [ ] Appropriate tool selection
- [ ] Lifecycle hook configuration
- [ ] ACL level assignment
- [ ] Category alignment
## Contributing
Agents are critical to our workflow. Propose new agents or improvements via pull request to the CFN development team.