CFN Docker Loop Orchestration Skill

**Purpose:** Orchestrate container-based CFN Loop execution with agent spawning, loop management, consensus collection, and product owner decision flow.

14 stars

Best use case

CFN Docker Loop Orchestration Skill is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

**Purpose:** Orchestrate container-based CFN Loop execution with agent spawning, loop management, consensus collection, and product owner decision flow.

Teams using CFN Docker Loop Orchestration Skill should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/orchestration/SKILL.md --create-dirs "https://raw.githubusercontent.com/masharratt/claude-flow-novice/main/.claude/cfn-extras/skills/deprecated/cfn-docker-runtime/lib/orchestration/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/orchestration/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How CFN Docker Loop Orchestration Skill Compares

Feature / AgentCFN Docker Loop Orchestration SkillStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

**Purpose:** Orchestrate container-based CFN Loop execution with agent spawning, loop management, consensus collection, and product owner decision flow.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# CFN Docker Loop Orchestration Skill

**Purpose:** Orchestrate container-based CFN Loop execution with agent spawning, loop management, consensus collection, and product owner decision flow.

## Overview

This skill manages the complete CFN Loop execution lifecycle for docker-based agents, coordinating Loop 3 (implementer) and Loop 2 (validator) phases, collecting confidence scores and consensus, and triggering Product Owner decisions.

## Architecture

```bash
CFN Docker V3 Coordinator
    ↓ (task analysis)
CFN Docker Loop Orchestration
    ↓ (context storage)
Redis (State Management)
    ↓ (agent spawning)
CFN Docker Agent Spawning
    ↓ (container execution)
Docker Containers (Agents)
    ↓ (completion signals)
Consensus Collection
    ↓ (decision flow)
Product Owner Decision
```

## Loop Execution Model

### Loop 3: Primary Implementation (Implementers)
- **Purpose**: Create, build, and implement the solution
- **Agents**: 3 specialized implementers based on task requirements
- **Process**: Parallel execution → Confidence reporting → Gate check

### Loop 2: Consensus Validation (Validators)
- **Purpose**: Review and validate Loop 3 work
- **Agents**: 2-4 validators (reviewers, testers, security specialists)
- **Process**: Sequential validation → Consensus collection → Decision

### Loop 4: Product Owner Decision
- **Purpose**: Strategic decision based on consensus and deliverables
- **Agent**: Single product owner with GOAP methodology
- **Process**: Decision analysis → PROCEED/ITERATE/ABORT

## Core Functions

### 1. Task Analysis and Context Setup
Analyze task requirements and prepare execution context:

```bash
# Initialize loop orchestration
cfn-docker-loop-orchestration init \
  --task-id task-authentication \
  --task-description "Implement user authentication system" \
  --mode standard \
  --max-iterations 5
```

### 2. Loop 3 Agent Spawning
Spawn implementer agents based on task requirements:

```bash
# Spawn Loop 3 implementers
cfn-docker-loop-orchestration spawn-loop3 \
  --task-id task-authentication \
  --agents backend-developer,frontend-engineer,security-specialist \
  --context "${TASK_CONTEXT}"
```

### 3. Loop 3 Completion Monitoring
Monitor implementer progress and collect confidence scores:

```bash
# Monitor Loop 3 completion
cfn-docker-loop-orchestration monitor-loop3 \
  --task-id task-authentication \
  --wait-for-complete \
  --gate-threshold 0.75
```

### 4. Gate Check and Decision
Evaluate Loop 3 results and decide next steps:

```bash
# Check Loop 3 gate
cfn-docker-loop-orchestration gate-check \
  --task-id task-authentication \
  --gate-threshold 0.75 \
  --max-iterations 5
```

### 5. Loop 2 Validator Spawning
Spawn validator agents if gate passed:

```bash
# Spawn Loop 2 validators
cfn-docker-loop-orchestration spawn-loop2 \
  --task-id task-authentication \
  --agents reviewer,tester,security-specialist \
  --loop3-work "${WORK_SUMMARY}"
```

### 6. Consensus Collection
Collect and analyze validator consensus:

```bash
# Collect Loop 2 consensus
cfn-docker-loop-orchestration collect-consensus \
  --task-id task-authentication \
  --required-consensus 0.90 \
  --timeout 300
```

### 7. Product Owner Decision
Trigger final decision process:

```bash
# Trigger Product Owner decision
cfn-docker-loop-orchestration trigger-po-decision \
  --task-id task-authentication \
  --consensus-data "${CONSENSUS_RESULTS}"
```

## Loop Management

### Iteration Control
```bash
# Control loop iterations
cfn-docker-loop-orchestration control-iterations \
  --task-id task-authentication \
  --current-iteration 2 \
  --max-iterations 5 \
  --gate-threshold 0.75 \
  --consensus-threshold 0.90
```

### Adaptive Agent Selection
```bash
# Select agents based on iteration and feedback
cfn-docker-loop-orchestration select-adaptive-agents \
  --task-id task-authentication \
  --iteration 2 \
  --previous-feedback "${FEEDBACK_DATA}"
```

### Error Handling and Recovery
```bash
# Handle execution errors
cfn-docker-loop-orchestration handle-errors \
  --task-id task-authentication \
  --error-type agent-failure \
  --failed-agent-id agent-backend-001 \
  --restart-strategy adaptive
```

## Configuration Modes

### MVP Mode (Quick Execution)
```bash
cfn-docker-loop-orchestration execute \
  --task-id task-authentication \
  --mode mvp \
  --max-iterations 3 \
  --gate-threshold 0.70 \
  --consensus-threshold 0.80 \
  --validators 2
```

### Standard Mode (Balanced)
```bash
cfn-docker-loop-orchestration execute \
  --task-id task-authentication \
  --mode standard \
  --max-iterations 10 \
  --gate-threshold 0.75 \
  --consensus-threshold 0.90 \
  --validators 3
```

### Enterprise Mode (Thorough)
```bash
cfn-docker-loop-orchestration execute \
  --task-id task-authentication \
  --mode enterprise \
  --max-iterations 15 \
  --gate-threshold 0.85 \
  --consensus-threshold 0.95 \
  --validators 5
```

## Agent Selection Strategy

### Task-Based Agent Selection
```bash
# Analyze task and select appropriate agents
cfn-docker-loop-orchestration analyze-and-select \
  --task-description "Implement secure user authentication" \
  --output-agent-types
```

### Skill-Based Selection
```bash
# Select agents based on required skills
cfn-docker-loop-orchestration select-by-skills \
  --required-skills "security,backend-development,frontend-development" \
  --agent-count 3
```

### Experience-Based Selection
```bash
# Select agents based on previous performance
cfn-docker-loop-orchestration select-by-experience \
  --task-domain authentication \
  --success-threshold 0.85
```

## Integration with CFN Docker Skills

### Redis Coordination Integration
```bash
# Store loop state in Redis
cfn-docker-redis-coordination store-loop-state \
  --task-id task-authentication \
  --loop-number 3 \
  --iteration 1 \
  --state "in-progress"

# Retrieve loop state
cfn-docker-redis-coordination get-loop-state \
  --task-id task-authentication \
  --loop-number 3
```

### Agent Spawning Integration
```bash
# Spawn agents with MCP configuration
cfn-docker-agent-spawn batch \
  --agent-types backend-developer,frontend-engineer \
  --task-id task-authentication \
  --mcp-auto-select
```

### Skill Selection Integration
```bash
# Configure MCP access for agents
for agent_type in backend-developer frontend-engineer; do
  cfn-docker-skill-mcp-selector configure \
    --agent-type $agent_type \
    --task-id task-authentication
done
```

## Monitoring and Observability

### Loop Progress Monitoring
```bash
# Monitor overall loop progress
cfn-docker-loop-orchestration monitor-progress \
  --task-id task-authentication \
  --real-time \
  --include-agents

# Monitor specific loop
cfn-docker-loop-orchestration monitor-loop \
  --task-id task-authentication \
  --loop-number 3 \
  --detailed
```

### Performance Metrics
```bash
# Collect performance metrics
cfn-docker-loop-orchestration metrics \
  --task-id task-authentication \
  --include-timing \
  --include-resource-usage \
  --include-confidence-trends
```

### Agent Performance Tracking
```bash
# Track agent performance across loops
cfn-docker-loop-orchestration agent-performance \
  --task-id task-authentication \
  --agent-id agent-backend-001 \
  --all-iterations
```

## Decision Flow Logic

### Gate Check Algorithm
1. **Collect Confidence Scores**: Gather all Loop 3 confidence scores
2. **Calculate Average**: Compute mean confidence score
3. **Check Threshold**: Compare against gate threshold
4. **Decision Logic**:
   - `confidence >= threshold` → Proceed to Loop 2
   - `confidence < threshold` → Iterate Loop 3 (if iterations remaining)

### Consensus Validation Algorithm
1. **Collect Validator Feedback**: Gather all Loop 2 validator responses
2. **Calculate Consensus**: Compute average confidence and agreement level
3. **Check Threshold**: Compare against consensus threshold
4. **Decision Logic**:
   - `consensus >= threshold` → Trigger Product Owner
   - `consensus < threshold` → Iterate (if iterations remaining)

### Product Owner Decision Triggers
1. **High Consensus (≥0.95)**: AUTO-COMPLETE
2. **Good Consensus (≥threshold)**: PROCEED to Product Owner
3. **Low Consensus (<threshold)**: ITERATE with specific feedback

## Error Handling Strategies

### Agent Failure Handling
```bash
# Handle agent container failure
cfn-docker-loop-orchestration handle-agent-failure \
  --task-id task-authentication \
  --failed-agent-id agent-backend-001 \
  --strategy respawn \
  --backup-agent-type backend-developer
```

### Timeout Handling
```bash
# Handle loop timeouts
cfn-docker-loop-orchestration handle-timeout \
  --task-id task-authentication \
  --loop-number 3 \
  --timeout-extension 300
```

### MCP Server Failures
```bash
# Handle MCP server connectivity issues
cfn-docker-loop-orchestration handle-mcp-failure \
  --task-id task-authentication \
  --failed-mcp playwright \
  --fallback direct-tools
```

## Performance Optimization

### Parallel Execution
- **Loop 3**: Spawn all implementers simultaneously
- **Loop 2**: Sequential validator execution to prevent conflicts
- **Resource Management**: Balance concurrent execution with resource limits

### Adaptive Iteration
- **Smart Iteration**: Only iterate when specific issues identified
- **Agent Specialization**: Select different specialists based on feedback
- **Context Preservation**: Maintain context across iterations

### Resource Optimization
- **Container Reuse**: Reuse containers where possible
- **MCP Server Optimization**: Share MCP servers across compatible agents
- **Memory Management**: Optimize memory usage across loop phases

## Quality Assurance

### Deliverable Validation
```bash
# Validate required deliverables created
cfn-docker-loop-orchestration validate-deliverables \
  --task-id task-authentication \
  --required-files "auth-service.js,login.html" \
  --acceptance-criteria "${CRITERIA}"
```

### Code Quality Checks
```bash
# Run automated quality checks
cfn-docker-loop-orchestration quality-check \
  --task-id task-authentication \
  --include-linting \
  --include-security-scan \
  --include-tests
```

### Integration Testing
```bash
# Run integration tests for complete solution
cfn-docker-loop-orchestration integration-test \
  --task-id task-authentication \
  --test-suite authentication-tests
```

## Best Practices

### Loop Design
- **Clear Success Criteria**: Define measurable success criteria for each loop
- **Appropriate Thresholds**: Set realistic confidence and consensus thresholds
- **Iteration Limits**: Establish maximum iteration limits to prevent infinite loops

### Agent Selection
- **Task-Appropriate Skills**: Select agents with relevant domain expertise
- **Diverse Perspectives**: Include different agent types for comprehensive coverage
- **Performance History**: Consider past agent performance when selecting

### Error Recovery
- **Graceful Degradation**: Implement fallback mechanisms for failures
- **State Persistence**: Maintain state for recovery from interruptions
- **Monitoring**: Comprehensive monitoring for early issue detection

## Configuration

### Environment Variables
```bash
# Loop configuration
CFN_DOCKER_MAX_ITERATIONS=10
CFN_DOCKER_GATE_THRESHOLD=0.75
CFN_DOCKER_CONSENSUS_THRESHOLD=0.90
CFN_DOCKER_LOOP_TIMEOUT=600

# Agent configuration
CFN_DOCKER_MAX_CONCURRENT_AGENTS=5
CFN_DOCKER_AGENT_TIMEOUT=300
CFN_DOCKER_CONTAINER_MEMORY_LIMIT=1g

# Decision configuration
CFN_DOCKER_AUTO_COMPLETE_THRESHOLD=0.95
CFN_DOCKER_FORCE_ITERATION_THRESHOLD=0.60
```

### Loop Configuration File
```json
{
  "loopConfig": {
    "maxIterations": 10,
    "gateThreshold": 0.75,
    "consensusThreshold": 0.90,
    "loopTimeouts": {
      "loop3": 600,
      "loop2": 300,
      "productOwner": 180
    }
  },
  "agentConfig": {
    "maxConcurrentAgents": 5,
    "defaultMemoryLimit": "1g",
    "defaultCpuLimit": 1.0,
    "restartPolicy": "on-failure"
  },
  "decisionConfig": {
    "autoCompleteThreshold": 0.95,
    "forceIterationThreshold": 0.60,
    "enableAdaptiveSelection": true
  }
}
```

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