smithery.ai

loopic-integration

Skill for coordinating with autonomous loopic agents (monitoring, support, deployment, customization, migration)

First seen Mar 25, 2026

Installation

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Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 11,295 B
  • docs SUMMARY.md 138 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 1 installs

SKILL.md

Loopic Agent Integration Skill

This skill helps AI assistants coordinate with the autonomous agent loops running in the orchestrator.

Overview

The n8n-infrastructure project uses autonomous loopic agents that run continuously in background loops:

  • Monitoring Loop (30s): Health checks, anomaly detection
  • Optimization Loop (5min): Performance tuning
  • Predictive Loop (1min): Problem prediction
  • Learning Loop (1h): Pattern learning
  • Maintenance Loop (24h): Cleanup, backups

These agents are always running and handle most issues automatically. This skill helps you work with them, not against them.


Autonomous Agents in the System

Located in agents/orchestrator/src/agents/:

  1. SupportAgent (support.agent.ts)

- Auto-healing when health checks fail - Error rate monitoring - Automatic recovery attempts

  1. DeploymentAgent (deployment.agent.ts)

- Automated workflow deployment - Multi-tenant management - Validation and rollback

  1. CustomizationAgent (customization.agent.ts)

- Workflow personalization - Template management

  1. MigrationAgent (migration.agent.ts)

- Data migration support - Workflow import/export


When to Use This Skill

Check agent status BEFORE manual intervention when:

  • User reports an issue that might already be handled
  • Planning a system change that could conflict with auto-healing
  • Investigating performance or reliability issues
  • Wanting to understand what the system is doing automatically

Capabilities

1. Check Orchestrator Status

When to use: Before any manual intervention, to see if agents are handling it.

Instructions:

# Check if orchestrator is running
docker ps | Select-String "miconsul-orchestrator"

# If orchestrator is in this project (not all deployments have it running yet):
# View orchestrator logs
docker logs miconsul-orchestrator --tail 50

# Check for recent agent activity
docker logs miconsul-orchestrator --tail 100 | Select-String "SupportAgent|DeploymentAgent|CustomizationAgent"

Expected Output:

  • If orchestrator running: Logs showing agent activities and loop executions
  • If not running: Empty result (agents are code artifacts, not yet deployed in all environments)

What to do:

  • If agents detected issue: Wait for auto-healing, monitor progress
  • ⚠️ If no agent activity: Proceed with manual fix, but log what you did
  • 🔴 If orchestrator not running: This is development/early deployment phase, proceed with manual operations

2. Check Event Bus Activity

When to use: To see what events the system is processing.

Instructions:

# If Redis is running (Event Bus):
docker ps | Select-String "redis"

# View Redis activity (if orchestrator is deployed):
docker logs miconsul-redis --tail 50

Event Types to Look For:

  • HEALTHCHECKFAILED - SupportAgent will auto-heal
  • ANOMALY_DETECTED - System is investigating
  • OPTIMIZATION_SUGGESTED - System improving performance
  • ERRORRATEINCREASING - Predictive alert

What to do:

  • If events show agents are active → Let them work, monitor progress
  • If no recent events → System is healthy or agents not deployed yet (check agents/orchestrator/dist)

3. Review Agent Loop Metrics

When to use: Understanding system health and autonomous performance.

Instructions:

# If orchestrator dashboard is available (future feature):
# curl http://localhost:3000/dashboard

# For now, check agent code to understand what loops do:
Get-ChildItem "agents\orchestrator\src\agents" -Filter "*.agent.ts" | Select-Object Name

# Read specific agent logic:
Get-Content "agents\orchestrator\src\agents\support.agent.ts"

Understanding the Loops (from docs/sistemabucleagentico.md):

Loop Interval Purpose
Monitoring 30s Detect failures, anomalies
Optimization 5min Improve performance
Predictive 1min Predict future issues
Learning 1h Learn patterns, best practices
Maintenance 24h Cleanup, optimize DB

4. Coordinate with Auto-Healing

When to use: User reports issue, need to check if auto-healing is in progress.

Instructions:

# Check recent n8n logs for recovery attempts
docker logs n8n-v2 --tail 100 | Select-String "restart|recovery|auto-heal"

# Check if SupportAgent triggered recovery (if orchestrator running):
docker logs miconsul-orchestrator --tail 50 | Select-String "SupportAgent.*recovery"

# Check Docker restart count (indicates auto-healing attempts):
docker inspect n8n-v2 --format='{{.RestartCount}}'

Decision Tree:

Is there an error?
  ├─ Yes → Check auto-healing logs
  │   ├─ Auto-healing in progress? 
  │   │   ├─ Yes → Wait 2-5 minutes, monitor
  │   │   └─ No → Proceed with manual fix
  │   └─ Auto-healing failed?
  │       └─ Escalate or manual intervention
  └─ No → All good, no action needed

5. Understanding Tenant Context

When to use: Multi-tenant operations, understanding which client is affected.

Instructions:

# View tenant configuration (if exists):
Get-Content "agents\orchestrator\config\tenants.json" -ErrorAction SilentlyContinue | ConvertFrom-Json

# Or check environment for tenant info:
Get-Content ".env" | Select-String "TENANT"

Tenant Features (from agent code):

  • autoHealing: Whether SupportAgent auto-restarts on failure
  • autoOptimize: Whether OptimizationAgent applies fixes automatically
  • autoUpdate: Whether system applies safe updates

Note: Current deployment may not have full multi-tenant setup yet. Check IMPLEMENTATION_STATUS.md for current phase.


6. Agent Deployment Status

When to use: Confirming which agents are actually running vs just code artifacts.

Instructions:

# Check if orchestrator container exists:
docker ps -a | Select-String "orchestrator"

# Check docker-compose for orchestrator service:
Get-Content "docker-compose.yml" | Select-String "orchestrator" -Context 5

# Check implementation status:
Get-Content "IMPLEMENTATION_STATUS.md" | Select-String "agent|orchestrator" -Context 2

Current State (as of 2026-01-16):

  • Agents exist as code artifacts in agents/orchestrator/
  • May not be deployed as running containers yet (check docker-compose.yml)
  • Documentation exists in docs/sistemabucleagentico.md
  • Follows a phased implementation approach

7. Manual Override Guidelines

When to use: You need to do manual operation despite autonomous agents.

Rules for Manual Intervention:

  1. Always check first: Run diagnostics to see if agents are handling it
  2. Wait for auto-healing: Give agents 5 minutes to resolve before manual fix
  3. Log your actions: Document what you did so agents can learn from it
  4. Coordinate with loops: Don't restart during a monitoring cycle (30s window)
  5. Update Event Bus: If you fix something, emit an event so agents know

Example - Safe Manual Restart:

# 1. Check if auto-healing is active
docker logs n8n-v2 --tail 20 | Select-String "restart"

# 2. If no auto-healing in last 2 minutes, safe to proceed
# 3. Perform restart
docker-compose restart n8n

# 4. Monitor to ensure agents see the change
Start-Sleep -Seconds 30
.\monitor-n8n.ps1

8. Reading Agent Source Code

When to use: Understanding what agents can do automatically.

Key Files:

agents/orchestrator/src/
├── agents/
│   ├── support.agent.ts        # Auto-healing, error handling
│   ├── deployment.agent.ts     # Workflow deployment
│   ├── customization.agent.ts  # Personalization
│   └── migration.agent.ts      # Data migration
├── events/
│   ├── event-bus.service.ts    # Redis Pub/Sub
│   └── event-types.enum.ts     # All event types
└── loops/
    └── (future: monitoring.loop.ts, etc.)

Reading Agent Code:

# View SupportAgent capabilities:
Get-Content "agents\orchestrator\src\agents\support.agent.ts"

# See what events it listens to:
Get-Content "agents\orchestrator\src\agents\support.agent.ts" | Select-String "eventBus.on"

# Check available event types:
Get-Content "agents\orchestrator\src\events\event-types.enum.ts"

Workflow for AI Assistants

Standard Troubleshooting Flow

graph TD
    A[User Reports Issue] --> B{Check Orchestrator}
    B -->|Running| C[Check Agent Logs]
    B -->|Not Running| D[Use Manual n8n-management Skill]
    C --> E{Auto-Healing Active?}
    E -->|Yes| F[Wait 5min, Monitor]
    E -->|No| G[Check Event Bus]
    G --> H{Recent Events?}
    H -->|Yes| I[Agents are aware, wait]
    H -->|No| D
    F --> J{Issue Resolved?}
    J -->|Yes| K[Report Success]
    J -->|No| D

When to Choose Manual vs Autonomous

Scenario Autonomous Manual
n8n container down ✅ SupportAgent auto-heals ⚠️ If fails after 5min
Workflow JSON invalid ❌ Not auto-detected yet ✅ Use n8n-management
High error rate ✅ Monitoring Loop alerts 🔄 Support investigates
New workflow deploy ✅ DeploymentAgent ✅ Or manual via UI
Credential expiring ✅ Predictive Loop warns ✅ Manual renewal needed
Performance slow ✅ Optimization Loop fixes ⚠️ If persists

Expected Behavior

Healthy System

  • Monitoring Loop running every 30s
  • No critical events in last hour
  • Auto-healing only triggered occasionally
  • Optimization suggestions applied automatically

System Under Stress

  • Frequent HEALTHCHECKFAILED events
  • SupportAgent actively recovering
  • Error rate increasing alerts
  • Manual intervention may be needed

Development/Early Phase

  • Orchestrator may not be deployed yet
  • Agents are code-only (not running)
  • Use manual skills from n8n-management
  • Document issues for future agent training

Best Practices

  1. Check Before Acting: Always check agent status before manual intervention
  2. Respect Auto-Healing: Give agents time to resolve issues (5min window)
  3. Coordinate Timing: Don't interrupt during monitoring loops (30s cycles)
  4. Log Manual Actions: Document what you did for agent learning
  5. Monitor Aftermath: Verify agents see your changes and adjust

Future Enhancements

As the orchestrator develops:

  • Dashboard at http://localhost:3000/dashboard (planned)
  • WebSocket for real-time event streaming
  • Agent performance metrics
  • ML-based prediction improvements

References

  • docs/sistemabucleagentico.md - Full autonomous system design
  • docs/agentesmcpresumen.md - Agent summaries and use cases
  • agents/orchestrator/ - Agent source code
  • IMPLEMENTATION_STATUS.md - Current deployment phase

Last Updated: 2026-01-16 Version: 1.0 Agent Deployment Phase: Check IMPLEMENTATION_STATUS.md for current status