CFN Docker Loop Orchestration Skill
**Purpose:** Orchestrate container-based CFN Loop execution with agent spawning, loop management, consensus collection, and product owner decision flow.
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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/orchestration/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How CFN Docker Loop Orchestration Skill Compares
| Feature / Agent | CFN Docker Loop Orchestration Skill | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/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
}
}
```Related Skills
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cfn-loop-orchestration-v2
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