N8n Automation

n8n Automation Skill

This skill provides expertise in building automation workflows using n8n, specifically focused on SkySpark integration and multi-agent systems for building analytics.

Core Capabilities

SkySpark Multi-Agent Architecture

• Gatekeeper agent with specialist routing
• Automated spark triage and prioritization
• HVAC specialist for mechanical system analysis
• Energy calculation agent for savings estimates
• Automated report generation workflows

n8n Workflow Development

• REST API integrations with building systems
• Multi-agent coordination patterns
• Error handling and logging strategies
• Scheduled automation and notifications

SkySpark Integration

• Haystack REST API connectivity
• ZINC and JSON data format handling
• Spark and trend data extraction
• Authentication and security patterns

Available Scripts

• scripts/fastapi_tool_server.py: Template for FastAPI tool servers
• scripts/n8n_workflow_templates/: Common workflow patterns
• scripts/skyspark_mock_data.py: Generate test data for development

Reference Materials

-

eferences/multi_agent_patterns.md: Proven multi-agent architectures

eferences/skyspark_api.md: SkySpark Haystack API documentation

eferences/n8n_best_practices.md: Workflow development guidelines

eferences/roadmap.md: 5-phase project roadmap (Foundation → Production)

Asset Templates

• ssets/workflow_exports/: n8n workflow export files
• ssets/api_schemas/: OpenAPI schemas for tool servers
• ssets/mock_data/: Sample SkySpark data for testing

Current Project: SkySpark Multi-Agent System

5-Phase Roadmap

1. Foundation: FastAPI tool server + basic agent interaction
2. Mock Integration: Agent logic with simulated SkySpark data
3. Real SkySpark: Live Haystack API connection
4. Multi-Agent: Full specialist coordination
5. Production: Error handling, scheduling, notifications

Multi-Agent Pattern

User Request → Gatekeeper Agent (routes by intent) ↓ ┌───────────┼───────────┬──────────────┐ ↓ ↓ ↓ ↓ Triage HVAC Energy Report Agent Specialist Calc Agent Generator

Agent Architecture for Future RL Orchestration

Stateless Agent Design Principles

All SkySpark agents should be designed with future dynamic orchestration in mind:

Statelessness Requirements:

• Agents receive complete diagnostic context as input (no persistent memory)
• All necessary context passed in, new state returned out
• Enables any agent to follow any agent (maximum routing flexibility)
• Simplifies debugging and testing

Standardized I/O Schema:

Input: DiagnosticState object containing:

• alert_context: Current alert/issue details (Haystack tags, spark data)
• prior_actions: Array of prior agent outputs/reasoning
• available_tools: List of tool endpoints and capabilities
• token_budget_remaining: Tokens available for continued diagnosis
• confidence_score: Current diagnostic confidence (0.0-1.0)
• metadata: Alert severity, facility info, timestamps

Output: AgentResponse object containing:

• updated_state: Refreshed DiagnosticState with new findings
• agent_findings: This agent's analysis, calculations, or actions
• recommended_next_agents: Suggested routing (even if not used in Phase 0-1)
• token_cost: Tokens consumed by this agent call
• confidence_delta: Change in diagnostic confidence (+/- 0.0-1.0)
• actions_taken: List of tools called or queries executed

Role-Based Architecture: Separate agent identity (role prompts) from action logic:

Tool-Use Agents:

• SkySpark_Query_Agent: Execute Axon queries against SkySpark database
• Python_Analysis_Agent: Statistical analysis, calculations, trending
• File_Reader_Agent: Parse equipment specs, CSV logs, JSON configs
• Weather_Data_Agent: Fetch external weather data via API
• Documentation_Agent: Search equipment manuals, ASHRAE standards

Reasoning Agents:

• Diagnostic_Planner_Agent: Decompose complex issues into sub-diagnostics
• Root_Cause_Analyzer_Agent: Synthesize data to identify probable causes
• Validation_Agent: Verify diagnostic logic and calculations
• Reflection_Agent: Assess diagnostic trajectory, propose refinements
• Summary_Agent: Generate concise diagnostic reports
• Resolution_Agent: Recommend corrective actions
• Modification_Agent: Correct errors in prior reasoning

Orchestration Evolution Roadmap

Phase 0-1: Static n8n Workflows (Current)

• Hardcoded agent routing via n8n nodes is acceptable
• BUT design workflows to call centralized routing functions
• Log every routing decision with full context
• Focus: Get agents working with standardized I/O

Phase 2-3: Rule-Based Orchestrator

• Implement agent_router function (Python/FastAPI endpoint)
• Route based on: alert type/severity, confidence score, prior sequence
• Manually tune rules based on logged diagnostic patterns
• A/B test against static workflows
• Focus: Data-driven routing without ML complexity

Phase 4-5: RL-Based Orchestrator

• Replace rule-based router with learned policy
• Train on logged diagnostic trajectories (minimum 200 episodes)
• Optimize for: R = accuracy - λ × token_cost - β × time_penalty
• Enable online learning from production outcomes
• Focus: Continuous optimization and adaptation

Instrumentation & Logging Requirements

Critical: Start logging from Day 1, even with static workflows

Log every diagnostic episode with schema:

{
  "episode_id": "alert_123456_2025-12-02",
  "facility_name": "Building XYZ",
  "alert_context": {
    "alert_type": "High AHU static pressure",
    "severity": "warning",
    "haystack_refs": [...],
    "triggered_at": "2025-12-02T10:15:00Z"
  },
  "agent_sequence": [
    {
      "timestamp": "2025-12-02T10:30:00Z",
      "agent_type": "SkySpark_Query_Agent",
      "input_state": {...},
      "output_state": {...},
      "tokens_used": 1500,
      "duration_ms": 3400,
      "tools_called": ["read_trends", "eval_axon"]
    },
    ...
  ],
  "final_outcome": {
    "diagnostic_correct": true,
    "root_cause_identified": "Filter 80% loaded",
    "resolution_actions": ["Schedule filter replacement"],
    "total_tokens": 8500,
    "total_duration_ms": 15200,
    "human_intervention_required": false,
    "human_feedback": "Diagnosis accurate, action appropriate"
  }
}

Storage Strategy:

• Store in n8n database OR dedicated TimescaleDB
• Retain minimum 200 episodes per alert type for RL training
• Enable easy export for offline analysis and model training
• Version control state schemas alongside agents

What to Log:

• Complete agent call sequence per diagnostic episode
• Full input/output states at each step
• Token consumption per agent (measure via API or estimate)
• Human validation/corrections (critical for reward labels)
• Final diagnostic outcome and resolution effectiveness
• Time to resolution relative to SLA thresholds

Reward Function Design

Preliminary specification for future RL training:

R = accuracy_score - λ × token_cost - β × time_penalty

Where:

• accuracy_score ∈ [0, 1] - Validated against human review OR resolution outcome
• token_cost = tokens_used / max_token_budget - Normalized efficiency metric
• time_penalty = max(0, (time_to_resolve / sla_threshold) - 1) - Timeliness factor
• λ = 0.1 - Efficiency weight (tunable based on production costs)
• β = 0.05 - Timeliness weight (varies by alert severity)

Accuracy Validation:

• Binary: Did diagnostic identify correct root cause? (0 or 1)
• Graduated: Partial credit for narrowing to subsystem (0.0-1.0 scale)
• Ground truth: FM confirmation or system behavior post-resolution

Efficiency Considerations:

• Critical alerts: Prioritize accuracy over token cost (λ → 0)
• Informational alerts: Prioritize efficiency (λ → 0.2)
• Balance prevents both over-analysis and premature conclusions

Timeliness Thresholds:

• Critical: 15 min SLA → high β penalty if exceeded
• Warning: 60 min SLA → moderate β penalty
• Info: 4 hour SLA → minimal β penalty

SkySpark Agent Taxonomy

Agents aligned with Puppeteer research categories:

Tool-Use Agents (External data access):

1. SkySpark_Query_Agent - Axon query execution
2. Python_Analysis_Agent - Statistical calculations, trending
3. File_Reader_Agent - Equipment specs, logs, configs
4. Weather_Data_Agent - External weather APIs
5. Documentation_Agent - Manuals, standards, knowledge base

Reasoning Agents (Internal cognition):

1. Diagnostic_Planner_Agent - Task decomposition
2. Root_Cause_Analyzer_Agent - Data synthesis
3. Validation_Agent - Logic verification
4. Reflection_Agent - Meta-reasoning
5. Summary_Agent - Report generation
6. Resolution_Agent - Action recommendation
7. Modification_Agent - Error correction

Critical Don'ts

Avoid these pitfalls:

• ❌ Don't jump to RL before having logged data (minimum 200 episodes)
• ❌ Don't hardcode agent sequences into prompts (keep routing external)
• ❌ Don't skip instrumentation "for now" (you'll never add it later)
• ❌ Don't design agents that assume specific predecessors
• ❌ Don't sacrifice debuggability for sophistication
• ❌ Don't optimize prematurely (get working system first)
• ❌ Don't force-fit RL where rules work fine

Do these instead:

• ✅ Keep current n8n approach for Phase 0-1 (it's pragmatic)
• ✅ Design agents with standardized I/O from day 1
• ✅ Start logging immediately, even with static workflows
• ✅ Validate every agent output initially (build ground truth dataset)
• ✅ Build tooling to visualize agent sequences (aid debugging)
• ✅ Document reward function assumptions (evolve with data)
• ✅ Plan human override mechanisms (safety net)

Success Metrics by Phase

Phase 1 (Static n8n + Logging):

• 5+ agents implemented with standardized I/O
• 100% of diagnostic episodes logged with schema
• Static workflow resolves 70%+ of test alerts correctly
• Average 8-12k tokens per diagnostic episode

Phase 2 (Rule-Based Router):

• Rule-based router handles 80%+ of alert types
• 10% reduction in average tokens vs. static workflow
• Diagnostic accuracy maintained or improved (≥70%)
• Routing decisions explainable to FM stakeholders

Phase 3 (RL Orchestrator):

• RL policy trained on 200+ episodes
• Matches or exceeds rule-based accuracy
• 20%+ token reduction vs. static baseline
• Cyclic reasoning patterns emerge for complex diagnostics

Phase 4 (Production RL):

• Online learning maintains 90%+ diagnostic accuracy
• 30%+ token reduction vs. baseline
• Handles novel alert types through adaptation
• Human intervention required <10% of cases

Integration with Existing Architecture

Compatible with current project structure:

• Project instructions: Project-Specific-Instructions/SkySpark-n8n-Workflow.md
• n8n workflows: n8n/
• Agent design aligns with FastAPI tool server architecture
• State schema accommodates SkySpark Haystack data model
• Reward function maps to ComEd program requirements

Backward Compatibility:

• Phase 0-1 static workflows remain fully functional
• Standardized I/O is additive, not breaking
• Logging infrastructure runs alongside existing logic
• Can defer RL indefinitely if rule-based routing suffices

Usage Examples

• "Build n8n workflow to triage SkySpark alerts"
• "Create FastAPI server for energy calculations"
• "Set up multi-agent routing for HVAC diagnostics"
• "Design workflow for automated building system reports"
• "Integrate n8n with SkySpark Haystack API"
• "Design stateless agent with standardized I/O for future RL"
• "Set up diagnostic episode logging infrastructure"
• "Implement rule-based agent router for Phase 2"