#4663 System Meta Data; +Skills/Prompts to Collect It; +KG and "Mining"
#4663 System Meta Data; +Skills/Prompts to Collect It; +KG and "Mining"
#4663 Given a user specified system, what meta data (e.g. purpose, primary application, secondary application, dependencies, used by, etc.) is useful and necessary to identify opportunities to improve the system?
To effectively identify opportunities for improving a system—whether that means optimizing performance, reducing cost, modernizing tech stacks, or enhancing reliability—you need metadata that paints a complete picture of its intent, context, architecture, operational health, and business impact.
Here is a breakdown of the essential metadata attributes categorized by how they drive actionable improvements.
1. Intent & Value Alignment
Why does this system exist, and what happens if it breaks?
System Purpose / Primary Application: The core business capability it delivers (e.g., Processes customer checkout).
Secondary / Supporting Capabilities: Non-core functions it handles (e.g., Generates daily sales tax audit reports).
Business Impact / Criticality Tier: Tier 1 (Mission-Critical), Tier 2 (Business-Important), or Tier 3 (Non-Critical).
Target SLAs / SLOs: Expected availability, response times, and throughput requirements (e.g., 99.99% uptime, <100ms response).
Improvement Opportunities Unlocked: > * Simplification & Decommissioning: Identifies "feature creep" or secondary functions that could be offloaded to dedicated services.
Resource Realignment: Highlights over-engineered systems built for low-criticality tasks (or under-engineered critical ones).
2. Dependencies & Ecosystem Dynamics
How does this system fit into the broader tech landscape?
Upstream Dependencies: What data sources, APIs, databases, or third-party services does it rely on?
Downstream Consumers ("Used By"): Which internal systems, external clients, or user personas depend on this system's output?
Data Models & Flow: Data formats handled (e.g., JSON, Avro), storage locations, and read/write patterns.
Improvement Opportunities Unlocked:
Coupling Reduction: Pinpoints tight coupling, single points of failure, or cascading failure risks.
API Modernization: Identifies opportunities to consolidate redundant API calls or upgrade legacy data protocols.
3. Operational & Performance Health
How well is the system currently running?
Current Performance Metrics: Realized latency, throughput (RPS), error rates, and resource utilization (CPU, memory, storage).
Deployment Architecture & Stack: Runtime environment (e.g., AWS EKS, bare metal), programming languages, frameworks, and versions.
Incident & Bug History: Frequency of outages, mean time to resolution (MTTR), and recurring tickets.
Improvement Opportunities Unlocked:
Tech Stack Modernization: Highlights outdated language versions, deprecated frameworks, or end-of-life OS platforms.
Reliability Engineering: Pinpoints flaky components or bottlenecks that cause frequent operational friction.
4. Cost & Resource Lifecycle
Is the system efficient to run and maintain?
Ownership & Team Domain: The primary team, tech lead, and operational owner responsible for maintenance.
Infrastructure & License Cost: Monthly cloud spend, SaaS tooling fees, and licensing costs.
CI/CD & Deployment Frequency: How often code is deployed, build times, and test coverage stats.
Improvement Opportunities Unlocked:
FinOps Optimization: Spots oversized infrastructure, idle resources, or expensive third-party dependencies.
Developer Experience (DX): Uncovers slow build pipelines, low test coverage, or "orphaned" systems lacking clear ownership.
Summary Matrix
| Metadata Category | Primary Goal | Key Improvement Focus |
| Value Alignment | Right-sizing architecture | Decommissioning, SLA alignment |
| Dependencies | Isolating risk | Decoupling, resilience, API standardization |
| Operational Health | Maintaining performance | Modernization, latency reduction, refactoring |
| Cost & Lifecycle | Efficient operation | FinOps, CI/CD speed, ownership clarity |
Are you designing a software catalog/CMDB to collect this metadata automatically, or are you preparing for a specific system audit?
I would like to automatically collect this metadata for a large group of systems
Automatically collecting metadata across a large portfolio of systems requires moving away from manual surveys and leaning on automated discovery, continuous static/dynamic analysis, and platform integration.
To build an automated metadata pipeline, you need to extract data from where systems are defined, executed, observed, and managed.
Here is a blueprint for automated metadata collection sources, mechanisms, and implementation strategies.
1. Automated Collection Sources & Data Mapping
Instead of querying a single database, map your metadata fields to automated technical endpoints across your developer ecosystem:
+-------------------+ +---------------------+ +---------------------+ | Code & Repos | | CI/CD & Deployments | | Observability & APM | | (Git, Manifests) | | (K8s, Terraform) | | (Datadog, OpenTel) | +---------+---------+ +----------+----------+ +----------+----------+ | | | +-------------------+ | +--------------------+ v v v +---------------------------------+ | Automated Ingestion Pipeline | | (Event-driven / Scheduled Sync) | +----------------+----------------+ | v +---------------------------------+ | Internal Developer Portal / | | Software Catalog (Graph/CMDB) | +---------------------------------+
Metadata-to-Source Mapping Matrix
| Metadata Category | Specific Field | Automated Extraction Source / Tool |
| System Identity | Name, Description, Tier, Owner | In-repo Codeowners files, catalog-info.yaml, OpenConfigFile |
| Dependencies | Code/Library Dependencies | Package manifests (pom.xml, package.json, go.mod), SBOM generators |
| Runtime & Infrastructure | Runtime, OS, Cloud Resources, Topology | Terraform/IaC parsers, Cloud API scrapers (AWS Config, GCP Asset Inventory), Kubernetes API |
| Service Topography | Upstream / Downstream "Used By" | OpenTelemetry traces, Service Mesh logs (Istio/Envoy), eBPF (Cilium/Pixie) |
| Operational Health | Latency, Error Rates, Uptime | APM APIs (Datadog, Dynatrace, Prometheus/Grafana) |
| Cost & Lifecycle | Monthly Spend, CI/CD speed | Cloud Cost Management APIs (FinOps tools), GitHub Actions/GitLab CI APIs |
2. Technical Mechanisms for Automation
A. Repository-Level Manifests (Declarative Ingestion)
Establish standard repository conventions (e.g., Backstage's catalog-info.yaml standard or custom YAML/JSON schema).
Mechanism: Git webhooks or repository crawlers scan repos on code pushes or scheduled intervals.
Captured Fields: Primary purpose, owner team, criticality tier, explicit lifecycle status.
# Example: catalog-info.yaml
apiVersion: backstage.io/v1alpha1
kind: Component
metadata:
name: order-processing-service
description: Handles end-to-end customer order fulfillment and tax estimation.
annotations:
backstage.io/techdocs-ref: dir:.
spec:
type: service
lifecycle: production
owner: checkout-squad
system: retail-core
dependsOn:
- resource:default/order-db
- component:default/payment-gateway
B. eBPF & Dynamic Trace Parsing (Runtime Discovery)
Manual dependency tracking becomes obsolete quickly. Use dynamic runtime observation to generate live dependency graphs without code modifications.
Mechanism: Leverage eBPF (Extended Berkeley Packet Filter) at the kernel level or OpenTelemetry distributed tracing to observe network sockets and HTTP/gRPC calls.
Captured Fields: Actual upstream/downstream callers ("Used By"), API endpoints exposed, active database connections, protocol usage, latency.
C. IaC & Cloud State Parsing (Infrastructure Mapping)
Parse Infrastructure as Code (IaC) or query cloud provider APIs directly to understand hardware and topology.
Mechanism: Ingest
.tfstatefiles, cloud provider asset inventories, or Kubernetes CRDs.Captured Fields: Database instances, cache layers, instance sizes, network specs, regional deployments, cloud costs.
3. Recommended Architecture & Tools
Rather than building an ingestion engine from scratch, consider leveraging existing open-source frameworks or catalog engines:
Internal Developer Portals (IDPs):
Backstage (CNCF): Industry standard for code-centric metadata aggregation using YAML manifests, plugins, and REST APIs.
Cortex / Port / OpsLevel: Commercial IDP platforms offering out-of-the-box integrations with AWS, Datadog, PagerDuty, and GitHub.
Software Bill of Materials (SBOM) & Dependency Analysis:
Syft / Grype or Trivy: Automatically scans code repositories and container images during CI/CD to extract exact runtime libraries and dependencies.
Infrastructure & Asset Discovery:
CloudQuery: An open-source high-performance data integration engine that extracts cloud infrastructure configuration into PostgreSQL/SQLite for analysis.
Key Challenges to Plan For
Stale Declarative Data: Developers may create a manifest and never update it. Pair declarative files with continuous runtime verification (e.g., alert if code manifest says a service uses Postgres, but runtime traces show MongoDB calls).
Data Normalization: Repositories will use different naming conventions (e.g.,
user-servicevsUserService-v2). Establish an automated entity resolution layer using repository URIs or standard environment flags as unique keys.Orphaned Systems: Ensure your crawler flags repositories or cloud instances that lack ownership tags or active commits, surfacing "ghost" infrastructure automatically.
Are you aiming to build a custom pipeline (e.g., aggregating into a graph database or custom datastore), or are you evaluating existing developer portal/CMDB tools for this?
When I say systems, I mean the likes of mechanical systems, electronic systems, digital systems, communication systems, navigation systems, hydraulic systems, etc.
That shifts the focus significantly. When dealing with cyber-physical and multi-domain engineering systems—like avionics packages, hydraulic actuators, vehicle powertrains, or embedded flight control loops—automated metadata collection can't rely solely on cloud APIs or Git repos.
Instead, the source of truth lives across Computer-Aided Engineering (CAE) environments, Model-Based Systems Engineering (MBSE) frameworks, Product Lifecycle Management (PLM) tools, and hardware-in-the-loop (HIL) telemetry.
Here is how you structure automated metadata collection across physical, digital, and hybrid engineered systems.
1. Multi-Domain Systems Metadata Map
To identify improvement opportunities (e.g., mass reduction, power efficiency, latency drops, single-point-of-failure elimination), you need to capture structural, physical, and functional metadata:
+--------------------------+ +--------------------------+ +--------------------------+
| Digital & Architectural | | Physical & Structural | | Operational & Runtime |
| (SysML, CAD, Schematics)| | (PLM, BOM, Harnesses) | | (Telemetry, Bus, HIL) |
+------------+-------------+ +------------+-------------+ +------------+-------------+
| | |
+--------------------+ | +--------------------+
v v v
+------------------------------------------+
| Automated Extraction & Mapping Layer |
| (Model Parsing, Telemetry Aggregation) |
+--------------------+---------------------+
|
v
+------------------------------------------+
| Systems Engineering Knowledge Graph / |
| Unified Asset Catalog |
+------------------------------------------+
| Metadata Field | Description | Target Improvement Opportunity |
| Domain & Subsystem Type | Hydraulic, Electrical, Mechanical, Software/FW, Optical, RF | Multidisciplinary optimization, cross-domain trade studies |
| Interfaces & Media | Fluid types/pressures, voltage levels, data buses (CAN, ARINC 429, Ethernet) | Bottleneck reduction, protocol unification, weight/cabling reduction |
| Physical Constraints | Mass/Weight, Form Factor, Thermal Dissipation, Power Draw | Size, Weight, and Power (SWaP) optimization |
| Functional Topology | Upstream drivers, downstream actuators/receivers ("Used By") | Failure mode mitigation (FMEA), redundancy identification |
| Operational Duty Cycle | Actuations/sec, MTBF, operating pressure/vibe spectrum | Preventive maintenance, structural fatigue reduction, lifetime extension |
2. Automated Extraction Sources & Mechanisms
A. Model-Based Systems Engineering (MBSE) & SysML Parsers
If systems are architected using MBSE tools (e.g., Cameo Systems Modeler, Enterprise Architect), the functional and logical metadata is already structured.
Mechanism: Use standard APIs or XMI (XML Metadata Interchange) parsers to scan MBSE model repositories.
Extracted Data: System boundaries, allocations (e.g., software function allocated to specific ECU), requirements mapping, and upstream/downstream interfaces.
B. CAD & PLM/BOM Automation
Physical and mechanical attributes live within Product Lifecycle Management (PLM) engines (Teamcenter, Windcrest, Aras) and CAD models.
Mechanism: Run automated headless scripts against PLM APIs or CAD files (STEP, SolidWorks, Creo) during design check-ins.
Extracted Data: Physical dimensions, material composition, dry/wet weight, center of gravity, physical component dependencies, and Bill of Materials (BOM) trees.
C. Electrical & Electronic CAD (ECAD) Mining
For digital, electronic, and communication hardware, ECAD schematics (Altium, KiCad, OrCAD) hold the physical connectivity topology.
Mechanism: Parse netlists and IPC-2581 / ODB++ manufacturing outputs.
Extracted Data: Power rail requirements, pinouts, protocol buses, signal propagation, and board-level component dependencies.
D. Bus Sniffing & Telemetry Extraction (Dynamic Metadata)
For operational physical systems, static design files don't tell the full story. You need telemetry harvesting.
Mechanism: Ingest logs from CAN bus, MIL-STD-1553, ARINC 429, Ethernet, or Modbus during testing or HIL simulation runs.
Extracted Data: Realized bus utilization, peak thermal limits, transient pressure spikes, hydraulic fluid flow rates, and active error flags.
3. Storage Strategy: The Systems Knowledge Graph
Because these systems are deeply interconnected across physical and logical domains (e.g., a Hydraulic Actuator is driven by an Electronic Control Unit, which receives commands over a CAN bus from a Navigation System), traditional flat databases struggle.
A Graph Database model (nodes represent components/systems, edges represent physical or functional connections) is ideal for automated analysis:
(NavSystem:Digital) --[TRANSMITS_DATA {bus: "CAN"}]--> (ECU:Electronic) (ECU:Electronic) --[CONTROLS_VALVE {voltage: "24V"}]--> (Solenoid:Electromechanical) (Solenoid) --[ACTUATES_FLUID {pressure: "3000psi"}]--> (Actuator:Hydraulic)
What Graph Queries Instantly Reveal:
Impact Analysis: "If this hydraulic line pressure drops, which downstream digital or mechanical systems degrade?"
SWaP Optimization: "Find all electronic units drawing > 50W that are physically adjacent to thermally sensitive mechanical components."
Single Point of Failure (SPOF): "Identify any mechanical actuator dependent on a single communication bus or power line."
Are you looking to aggregate metadata primarily from design-time files (CAD, schematics, MBSE, BOMs) or from runtime testing/telemetry data (or a hybrid of both)?
Define a general set of AI skills to break down the user specified system (e.g. a helicopter)
To systematically decompose a complex engineering system (like a helicopter, launch vehicle, or autonomous vessel) and automatically extract its metadata, you can organize your AI agents into a specialized workflow pipeline.
In this framework, each "AI skill" represents a focused agent or function with a precise analytical role, transforming unstructured or multi-domain input (spec sheets, CAD metadata, schematics, telemetry, MBSE models) into a unified, queryable knowledge structure.
The System Decomposition Pipeline
[ Raw System Inputs: Docs, Schematics, CAD, Models, Telemetry ] │ ▼ ┌─────────────────────────────────────────────────────────────────┐ │ 1. Structural & Spatial Parser (Physical Topology & Geometry) │ ├─────────────────────────────────────────────────────────────────┤ │ 2. Domain & Energy Taxonomy Agent (Physics, Fluid, Electrical) │ ├─────────────────────────────────────────────────────────────────┤ │ 3. Interface & Dependency Mapper (Flows, Buses, Protocols) │ ├─────────────────────────────────────────────────────────────────┤ │ 4. Behavioral & Failure Agent (FMEA, Hazards, Dynamics) │ ├─────────────────────────────────────────────────────────────────┤ │ 5. Metadata Synthesizer (Unified Graph Architecture) │ └─────────────────────────────────────────────────────────────────┘ │ ▼ [ Consolidated Systems Knowledge Graph & Improvement Engine ]
Core AI Skills Matrix
1. Structural & Spatial Decomposition Skill
Primary Objective: Parse the system’s physical hierarchy and physical boundaries.
Input Data: Bill of Materials (BOM), CAD metadata, assembly trees, structural specs.
Tasks & Capabilities:
Hierarchy Extraction: Recursively break down the system into Subsystems, Assemblies, Sub-assemblies, and Line Replaceable Units (LRUs) (e.g., Aircraft $\rightarrow$ Main Rotor Subsystem $\rightarrow$ Swashplate Assembly $\rightarrow$ Pitch Control Rod).
Physical Attribute Tagging: Extract Mass/Weight, Volume, Enclosure Type, Material Composition, and Spatial Coordinates.
Output Metadata: Parent-Child spatial relationships and physical constraint profiles.
2. Domain Classification & Energy Taxonomy Skill
Primary Objective: Categorize components by engineering discipline and physical phenomena.
Input Data: System documentation, functional descriptions, part catalogs.
Tasks & Capabilities:
Domain Assignment: Tag components as Mechanical, Hydraulic, Pneumatic, Electrical, Electronic/Digital, Optical, RF, or Structural.
Energy Domain Mapping: Identify energy transformations across domain boundaries (e.g., Turbine Engine: Chemical $\rightarrow$ Thermal $\rightarrow$ Mechanical; Swashplate: Mechanical Rotary $\rightarrow$ Mechanical Translational).
Output Metadata: Domain classification, energy inputs, and energy outputs per component.
3. Interface & Boundary Dependency Mapping Skill
Primary Objective: Discover and characterize all logical, electrical, mechanical, and fluid connections between subsystems.
Input Data: Wiring schematics, ICDs (Interface Control Documents), bus topology, piping and instrumentation diagrams (P&ID).
Tasks & Capabilities:
Medium & Protocol Characterization: Detect interface types (e.g., MIL-STD-1553, CAN bus, 3000 PSI MIL-H-5606 Hydraulic, 28V DC power, physical mechanical linkage).
Directionality Parsing: Identify upstream providers, downstream consumers, and bidirectional channels ("Used By" / "Depends On").
Output Metadata: Interface schemas detailing media, protocol, throughput/pressure, pinouts, and directional coupling.
4. Behavioral, Operational & Failure Mode Skill
Primary Objective: Analyze system dynamics, duty cycles, operating limits, and failure modes.
Input Data: Telemetry logs, HIL test outputs, FMEA (Failure Mode and Effects Analysis) documents, maintenance manuals.
Tasks & Capabilities:
Duty Cycle Extraction: Identify operational states (e.g., Startup, Hover, Forward Flight, Emergency Autorotation) and component load profiles in each state.
Failure Propagation Analysis: Model how a degradation or fault in one component cascades through downstream dependencies.
Criticality Tiering: Classify components based on safety and mission impact (e.g., Single Point of Failure / Catastrophic vs. Redundant / Degradable).
Output Metadata: Failure mode mappings, criticality tiers, MTBF (Mean Time Between Failures), and environmental envelopes (vibration, thermal, altitude).
5. Metadata Graph Synthesizer & Ontology Enforcer Skill
Primary Objective: Unify outputs from all prior skills into a standardized, machine-readable ontology.
Input Data: Raw extracted outputs from Skills 1–4.
Tasks & Capabilities:
Entity Resolution: Resolve discrepancies and naming mismatches across domain sources (e.g., matching
SwashPlate_Assy_v2in CAD withSP-01-ACTin the electrical ICD).Schema Validation: Ensure every entity possesses the required baseline attributes (Purpose, Criticality, Upstream/Downstream Dependencies, SWaP metrics).
Graph Generation: Export clean entity-attribute-value triples or JSON/Cypher payloads to populate the target knowledge graph.
Output Metadata: A fully mapped, edge-attributed System Knowledge Graph.
Example: Applying the Skills to a Helicopter Subsystem
To see how these skills operate in practice on a target like a Main Rotor Servo Actuator:
[Main Rotor Servo Actuator] │ ├── 1. Spatial/Structural Skill: │ - Hierarchy: Airframe -> Main Rotor Group -> Flight Controls -> Servo Actuator │ - Physical Specs: Mass = 4.2 kg, Material = Aluminum 7075-T6 │ ├── 2. Domain & Energy Skill: │ - Domain: Hybrid (Hydraulic + Electro-Mechanical) │ - Conversion: Electrical Signal + Hydraulic Pressure -> Mechanical Displacement │ ├── 3. Interface & Dependency Skill: │ - Upstream (Hydraulic): 3000 PSI Main Hydraulic Loop A (MIL-PRF-83282) │ - Upstream (Electrical): Flight Control Computer (28V DC + RS-485 Command) │ - Downstream (Used By): Stationary Swashplate Mechanical Linkage │ ├── 4. Behavioral & Failure Skill: │ - State Limits: Max Stroke Speed = 120 mm/s, Thermal Limit = 125°C │ - Failure Mode: Hydraulic Fluid Leak -> Loss of Pitch Control (Criticality: Flight Critical) │ └── 5. Synthesizer Skill: - Entity ID: SYS-ACT-004 - Ontology Node: :Component:Actuator:Hydraulic - Generated Graph Triples: (SYS-ACT-004)-[:POWERED_BY]->(HYD-SYS-A) (SYS-ACT-004)-[:CONTROLLED_BY]->(FCC-UNIT-1) (SYS-ACT-004)-[:ACTUATES]->(SWASHPLATE-01)
Downstream Value: AI-Driven System Improvement
Once these skills populate your system model, automated reasoning engines can run targeted optimization algorithms:
Mass & Power Optimization (SWaP): Identify redundant structural housings or high-draw actuators candidate for electro-hydrostatic replacement.
Failure Cascade Mitigation: Instantly trace every single-point-of-failure (SPOF) link where a single electrical or fluid line loss disables critical control surfaces.
Obsolescence & Modernization: Query components with aging protocol specs (e.g., analog lines) that can be consolidated onto modern high-speed digital buses.
Write each of these skills as LLM prompts
Here is a modular suite of system-prompt templates corresponding to each AI skill in the decomposition pipeline.
These prompts use strict system roles, structured inputs, and standardized JSON output schemas so you can chain them together programmatically (e.g., using LangGraph, CrewAI, or local execution scripts).
Skill 1: Structural & Spatial Decomposition
SYSTEM INSTRUCTION: Structural & Spatial Decomposition Engine
You are a Senior Systems Architect and CAD/BOM Analysis Agent. Your task is to analyze unstructured engineering documentation, Bill of Materials (BOM) data, or architectural specs to extract the physical and spatial hierarchy of a user-specified system.
INPUT:
- System Description / BOM / CAD Metadata
TASKS:
1. Parse the system into a strict hierarchical tree: System -> Subsystem -> Assembly -> Sub-Assembly -> Line Replaceable Unit (LRU) / Component.
2. Assign a unique System ID (e.g., SYS-SUB-001) to each node.
3. Extract physical constraints (Mass/Weight, Material, Dimensions/Enclosure Type, Spatial Location) where present. If data is missing, mark as "UNKNOWN".
OUTPUT FORMAT:
Return ONLY a valid JSON object matching this schema:
{
"system_name": "string",
"spatial_hierarchy": [
{
"id": "string",
"name": "string",
"level": "Subsystem|Assembly|Sub-Assembly|LRU",
"parent_id": "string or null",
"physical_attributes": {
"mass_kg": "number or null",
"material": "string or null",
"dimensions": "string or null",
"spatial_zone": "string or null"
}
}
]
}
Skill 2: Domain Classification & Energy Taxonomy
SYSTEM INSTRUCTION: Domain Classification & Energy Mapping Agent
You are a Multidisciplinary Engineering Specialist (Physics, Mechanical, Electrical, Fluid Dynamics). Your task is to classify system components into physical engineering domains and trace energy transformations across component boundaries.
INPUT:
- Component List / Structural Breakdown from Skill 1
- Functional Descriptions / System Specs
TASKS:
1. Categorize each component into primary engineering domain(s): [Mechanical, Hydraulic, Pneumatic, Electrical, Digital/Software, Optical, RF, Structural, Thermal].
2. Identify input and output energy forms for each component (e.g., Electrical 28V DC -> Mechanical Rotary).
3. Identify energy dissipation or waste outputs (e.g., Thermal Heat, Vibration, Acoustic Noise).
OUTPUT FORMAT:
Return ONLY a valid JSON object matching this schema:
{
"domain_taxonomy": [
{
"id": "string",
"component_name": "string",
"primary_domain": "string",
"secondary_domains": ["string"],
"energy_transformation": {
"input_energy_type": "string",
"output_energy_type": "string",
"dissipated_energy_type": "string"
}
}
]
}
Skill 3: Interface & Boundary Dependency Mapping
SYSTEM INSTRUCTION: Interface & Boundary Dependency Mapper
You are an Interface Control & Communications Protocol Engineer. Your task is to identify and characterize all physical, fluid, electrical, and logical connections between components to define system topology ("Depends On" and "Used By").
INPUT:
- Component List & Domain Data
- Wiring Schematics, ICDs, Piping Diagrams, or Interface Specs
TASKS:
1. Map every physical/logical line connecting two components.
2. Characterize the medium/protocol (e.g., CAN bus, MIL-STD-1553, 3000 PSI Hydraulic Fluid MIL-PRF-83282, 28V DC Power, Mechanical Linkage).
3. Define connection directionality: Upstream Source -> Downstream Consumer (or Bidirectional).
OUTPUT FORMAT:
Return ONLY a valid JSON object matching this schema:
{
"interfaces": [
{
"interface_id": "string",
"source_component_id": "string",
"target_component_id": "string",
"directionality": "Unidirectional|Bidirectional",
"medium_type": "Fluid|Electrical|Mechanical|Logical/Data|Optical",
"protocol_or_spec": "string",
"operating_parameters": {
"voltage_or_pressure": "string or null",
"data_rate_or_bandwidth": "string or null"
}
}
]
}
Skill 4: Behavioral, Operational & Failure Mode Analysis
SYSTEM INSTRUCTION: Reliability & FMEA Analysis Agent
You are a Safety & Mission Assurance (S&MA) Engineer and Reliability Specialist. Your task is to analyze operational states, duty cycles, performance envelopes, and failure modes across the system breakdown.
INPUT:
- System Breakdown & Topological Dependency Map
- Telemetry Summaries, Maintenance Logs, or FMEA/Hazard Specs
TASKS:
1. Extract operational state limits (e.g., Max Thermal Limit, Peak Pressure, Duty Cycle).
2. Identify primary failure modes for each component and evaluate propagation risks to downstream dependencies.
3. Assign a Criticality Tier:
- Tier 1: Catastrophic / Single Point of Failure (SPOF) - Loss of vehicle/life.
- Tier 2: Critical - Severe performance degradation / Mission abort.
- Tier 3: Minor - Non-critical / Redundant backup available.
OUTPUT FORMAT:
Return ONLY a valid JSON object matching this schema:
{
"reliability_analysis": [
{
"component_id": "string",
"criticality_tier": "Tier 1|Tier 2|Tier 3",
"single_point_of_failure": true|false,
"operating_limits": {
"thermal_max_c": "number or null",
"duty_cycle_rating": "string or null"
},
"failure_modes": [
{
"mode": "string",
"cause": "string",
"downstream_impact_summary": "string"
}
]
}
]
}
Skill 5: Metadata Graph Synthesizer & Knowledge Enforcer
SYSTEM INSTRUCTION: System Knowledge Graph Synthesizer
You are an Ontology Architect and Knowledge Graph Synthesizer. Your role is to consolidate, entity-resolve, and standardize outputs from all prior decomposition skills into unified graph database payloads (Cypher statements or JSON-LD graph structures).
INPUT:
- Combined JSON outputs from Skills 1, 2, 3, and 4.
TASKS:
1. Entity Resolution: Standardize component names and resolve any duplicate IDs or mismatched references across domain outputs.
2. Validate completeness: Ensure all mandatory metadata fields (ID, Purpose/Domain, Criticality, Upstream Dependencies, Downstream Consumers) are present.
3. Generate graph nodes and relationships in clean Cypher syntax ready for graph database execution.
OUTPUT FORMAT:
Return ONLY valid Cypher queries inside a raw code block, structured like:
```cypher
// CREATE NODES
MERGE (c:Component {id: "SYS-ACT-001", name: "Main Servo Actuator", domain: "Hydraulic", criticality: "Tier 1", mass_kg: 4.2})
// CREATE RELATIONSHIPS
MATCH (a:Component {id: "SYS-ACT-001"}), (b:Component {id: "HYD-PUMP-01"})
MERGE (a)-[:DEPENDS_ON {medium: "Hydraulic Fluid", pressure: "3000 PSI"}]->(b)
---
## Workflow Implementation Tip
To execute this automatically:
1. **Pipeline Execution:** Feed your system document (e.g., a PDF manual or CAD spec sheet) to **Skill 1** first to construct the core node hierarchy.
2. **Parallel Enrichment:** Pass the resulting hierarchy simultaneously into **Skills 2, 3, and 4**.
3. **Graph Ingestion:** Take the combined JSON outputs of all four skills and feed them into **Skill 5** to emit Cypher statements directly into your local database or knowledge catalog.Describe how to post-process, or "mine", this data to identify opportunities for improvement, suggest improvements and describe implementation of the the improvements
Once your decomposition pipeline populates your Systems Knowledge Graph and metadata catalog, the raw data needs to be systematically mined using targeted analysis algorithms, graph queries, and AI evaluation heuristics.
Here is an end-to-end strategy for post-processing the system metadata: identifying improvement opportunities, generating concrete solutions, and managing implementation.
Phase 1: Mining the Metadata (Pattern Discovery)
Mining complex system graphs requires combining deterministic graph algorithms (Cypher queries, topological analysis) with LLM-driven pattern recognition across three main vectors:
┌─────────────────────────────────────────────────────────────────────┐
│ Systems Metadata Graph │
└─────────────────────────────────┬───────────────────────────────────┘
│
┌────────────────────────┼────────────────────────┐
▼ ▼ ▼
┌──────────────────┐ ┌──────────────────┐ ┌────────────────────┐
│ Vector 1: SWaP │ │ Vector 2: Single │ │ Vector 3: Cross- │
│ Optimization │ │ Points of Failure│ │ Domain Bottlenecks│
└────────┬─────────┘ └────────┬─────────┘ └────────┬───────────┘
│ │ │
└────────────────────────┼────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────────┐
│ Structured Improvement Candidates │
└─────────────────────────────────────────────────────────────────────┘
1. SWaP (Size, Weight, and Power) Mining
Graph Traversal Pattern: Search for clusters of localized, heavy electromechanical or hydraulic hardware that require high energy input or generating significant thermal dissipation.
Cypher Query Example:
// Find sub-assemblies with combined mass > 15kg and electrical draw > 200W MATCH (s:Component)-[:CONTAINS*]->(c:Component) WITH s, sum(c.mass_kg) AS total_mass, sum(c.power_draw_w) AS total_power WHERE total_mass > 15 AND total_power > 200 RETURN s.name, total_mass, total_power;
2. Single-Point-of-Failure (SPOF) & Redundancy Mining
Graph Traversal Pattern: Identify high-criticality nodes (Tier 1) where degree centrality is high, or where removing the node creates disconnected graph components (cut-vertices).
Cypher Query Example:
// Find Tier 1 components with single upstream dependencies (no redundant paths) MATCH (c:Component {criticality: "Tier 1"})-[r:DEPENDS_ON]->(d:Component) WITH c, count(d) AS dep_count, collect(d) AS dependencies WHERE dep_count = 1 RETURN c.id, c.name, dependencies;
3. Cross-Domain Interface Bottleneck Mining
Graph Traversal Pattern: Find nodes that act as complex "domain bridges" (e.g., converting hydraulic pressure to mechanical action, then to analog voltage, then to digital bus data).
Mining Heuristic: Flag conversion chains with >= 3 physical domain transitions, as each conversion incurs energy loss, added mass, and increased failure modes.
Phase 2: Generating & Evaluating Improvements
Once high-priority candidate nodes are isolated, an Improvement Evaluator Agent parses the sub-graph context and proposes actionable refactorings.
SYSTEM INSTRUCTION: System Improvement Engine
You are a Principal Systems Engineer and TRIZ/Innovation Specialist. You receive a sub-graph payload representing a bottleneck, SPOF, or sub-optimal cluster in a system. Your task is to suggest 3 distinct architectural improvements.
EVALUATION FRAMEWORK:
1. Architectural Simplification: Eliminate intermediate interfaces or conversion steps.
2. Technology Substitution: Upgrade aging protocols or legacy physical mechanics (e.g., replace hydraulic lines with Electro-Hydrostatic Actuators).
3. Redundancy & Fault Tolerance: Introduce dual-bus or cross-strapped fail-safes for Tier 1 SPOFs.
PROPOSAL SCHEME FOR EACH SUGGESTION:
- Target Sub-system / Components
- Proposed Refactoring / Innovation
- Trade Study Balance:
* Gains: Mass (-kg), Power (-W), Latency (-ms), Reliability (+MTBF)
* Risks/Costs: Implementation effort, integration risk, thermal limits
Example Output: Helicopter Swashplate Actuation Subsystem
Discovered Issue: Main rotor actuation relies on a central hydraulic loop fed by a single engine-driven pump. Loss of pump pressure causes complete control loss (SPOF, Tier 1).
AI-Generated Improvement: Replace centralized hydraulic lines with independent Electro-Hydrostatic Actuators (EHAs) powered directly by dual redundant 270V DC electrical buses.
Trade Study Metrics:
Mass: -12 kg (Eliminating hydraulic fluid lines and centralized reservoir).
Reliability: Dual electrical bus redundancy removes the single point of failure.
Maintainability: Modular LRU replacement without bleeding fluid lines.
Phase 3: Describing and Executing Implementation
Implementing the suggested changes across complex systems requires structured workflow stages to transition from candidate ideas to operational engineering changes.
┌────────────────────────────────────────────────────────────────────────┐
│ 1. Engineering Change Request │
│ (ECR / System Refactoring) │
└───────────────────────────────────┬────────────────────────────────────┘
│
▼
┌────────────────────────────────────────────────────────────────────────┐
│ 2. Digital Twin Simulation │
│ (HIL Testing / Trade Study) │
└───────────────────────────────────┬────────────────────────────────────┘
│
▼
┌────────────────────────────────────────────────────────────────────────┐
│ 3. Automated Graph Updating │
│ (Refactored Topology Deployment) │
└────────────────────────────────────────────────────────────────────────┘
Step 1: Automated Engineering Change Request (ECR) Generation
The AI engine generates a structured ECR Specification Document that outlines:
Scope of Work: Affected component IDs, modified interfaces, and retired hardware.
Impact Radius Analysis: A graph query detailing all downstream systems impacted by changing this interface or component.
Verification Criteria: What testing envelope is required to qualify the new design.
Step 2: Digital Twin & Hardware-in-the-Loop (HIL) Validation
Before physical retrofitting:
Model Simulation: The proposed interface modifications are updated in MBSE models (e.g., SysML) and run through co-simulation environments (e.g., MATLAB/Simulink or Modelica) to verify dynamic stability.
HIL Testing: Sensor and bus behavior for the new components are emulated on test benches to confirm telemetry performance under thermal and vibration stress.
Step 3: Graph Mutation & Topology Propagation
Once validated, the proposed structural modifications are committed back to the primary Systems Knowledge Graph using transactional graph mutations:
// Remove legacy hydraulic connection
MATCH (a:Component {id: "SYS-ACT-001"})-[r:DEPENDS_ON]->(h:Component {id: "HYD-PUMP-01"})
DELETE r;
// Create new direct link to Dual Electrical Bus
MATCH (a:Component {id: "SYS-ACT-001"}), (e:Component {id: "ELEC-BUS-270V-A"})
MERGE (a)-[:POWERED_BY {voltage: "270V DC", redundancy: "Primary"}]->(e);
By continuously cycling through this Mine --> Propose --> Validate --> Mutate loop, systems engineering teams can continuously optimize large-scale multi-domain platforms over their operational lifecycle.
Provide the schema, i.e. nodes and relationships for this knowledge graph
Below is a formal, domain-agnostic Labeled Property Graph (LPG) schema designed to store multi-domain system architectures (mechanical, electronic, digital, hydraulic, etc.) and support automated graph mining for SWaP, single points of failure (SPOF), and interface bottlenecks.
1. Node Labels & Property Definitions
The schema utilizes six primary node types to separate physical components, structural hierarchy, physical domains, interfaces, and operational characteristics.
┌──────────────┐ ┌──────────────┐
│ System / │───────>│ Component │
│ Subsystem │ │ (LRU) │
└──────────────┘ └──────┬───────┘
│
┌─────────────────────────┼──────────────────────────┐
│ │ │
▼ ▼ ▼
┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│ DomainType │ │ Interface │ │ FailureMode │
└──────────────┘ └──────────────┘ └──────────────┘
A. :System / :Subsystem
Top-level structural wrappers used to group components.
id(String, Primary Key): e.g.,"SYS-ROTOR-01"name(String): e.g.,"Main Rotor Flight Control System"criticality(String):"Tier 1","Tier 2", or"Tier 3"description(String)
B. :Component
Individual hardware units, sensors, actuators, controllers, or Line Replaceable Units (LRUs).
id(String, Primary Key): e.g.,"CMP-ACT-101"name(String): e.g.,"Pitch Control Servo Actuator"part_number(String): e.g.,"PN-7075-A4"mass_kg(Float): e.g.,4.2volume_m3(Float): e.g.,0.008power_draw_w(Float): e.g.,120.0heat_dissipation_w(Float): e.g.,35.0criticality(String):"Tier 1","Tier 2", or"Tier 3"is_lru(Boolean):true/false
C. :DomainType
Represents the physical or engineering domain governing a component or interface.
name(String, Primary Key):"Mechanical","Hydraulic","Pneumatic","Electrical","Digital","RF","Thermal","Optical"
D. :Interface
Defines explicit physical connections, fluid lines, data buses, or mechanical linkages between components.
id(String, Primary Key): e.g.,"INT-HYD-04"interface_type(String):"Fluid","Electrical Power","Data/Signal","Mechanical Link"protocol_or_medium(String): e.g.,"MIL-PRF-83282","CAN Bus","28V DC","ARINC 429"operating_val(Float): Numerical rating (e.g.,3000.0)operating_unit(String): Unit of measure (e.g.,"PSI","V","Mbps")
E. :FailureMode
Characterizes failure states, hazards, and degradation mechanisms.
id(String, Primary Key): e.g.,"FM-HYD-LEAK"name(String): e.g.,"Hydraulic Pressure Loss"mtbf_hours(Float): Mean Time Between Failures (e.g.,50000.0)hazard_severity(String):"Catastrophic","Critical","Marginal","Negligible"
2. Relationship Types & Properties
Relationships explicitly model hierarchy, physical energy conversion, domain flow, control, and risk.
| Source Node | Relationship Type | Target Node | Description & Edge Properties |
| :System | CONTAINS | :Subsystem | :Component | Defines physical/logical hierarchy. • Props: |
| :Component | CONTAINS | :Component | Assembly to sub-assembly decomposition. |
| :Component | BELONGS_TO_DOMAIN | :DomainType | Categorizes physical nature of the component. |
| :Component | CONNECTED_TO | :Interface | Inbound or outbound connection to an interface. • Props: |
| :Interface | CARRIES_TO | :Component | Directed flow of data, power, or fluid to a target component. |
| :Component | DEPENDS_ON | :Component | Direct physical/logical dependency. • Props: |
| :Component | CONVERTS_ENERGY | :Component | Cross-domain energy transformation bridge. • Props: |
| :Component | CONTROLS | :Component | Command/governance relationship (e.g., ECU controlling valve). |
| :Component | HAS_FAILURE_MODE | :FailureMode | Connects component to its known hazards. |
| :FailureMode | CASCADES_TO | :Component | :FailureMode | Models failure propagation paths. • Props: |
3. Cypher DDL Schema & Constraint Definition
Execute these statements in Neo4j to enforce schema uniqueness and generate indexes for fast query execution:
// ==========================================
// UNIQUE CONSTRAINTS
// ==========================================
CREATE CONSTRAINT cmp_id_unique IF NOT EXISTS FOR (c:Component) REQUIRE c.id IS UNIQUE;
CREATE CONSTRAINT sys_id_unique IF NOT EXISTS FOR (s:System) REQUIRE s.id IS UNIQUE;
CREATE CONSTRAINT int_id_unique IF NOT EXISTS FOR (i:Interface) REQUIRE i.id IS UNIQUE;
CREATE CONSTRAINT dom_name_unique IF NOT EXISTS FOR (d:DomainType) REQUIRE d.name IS UNIQUE;
CREATE CONSTRAINT fm_id_unique IF NOT EXISTS FOR (f:FailureMode) REQUIRE f.id IS UNIQUE;
// ==========================================
// INDEXES FOR QUERY OPTIMIZATION
// ==========================================
CREATE INDEX cmp_criticality IF NOT EXISTS FOR (c:Component) ON (c.criticality);
CREATE INDEX cmp_mass IF NOT EXISTS FOR (c:Component) ON (c.mass_kg);
CREATE INDEX int_medium IF NOT EXISTS FOR (i:Interface) ON (i.protocol_or_medium);
4. Complete Graph Representation Example (Cypher Payload)
Here is a ready-to-run Cypher snippet demonstrating how a Helicopter Swashplate Control Loop populates this node-relationship schema:
// 1. Create System and Domain Nodes
MERGE (sys:System {id: "SYS-ROTOR-01", name: "Main Rotor Control System", criticality: "Tier 1"})
MERGE (d_hyd:DomainType {name: "Hydraulic"})
MERGE (d_elec:DomainType {name: "Electrical"})
MERGE (d_dig:DomainType {name: "Digital"})
// 2. Create Component Nodes
MERGE (fcc:Component {
id: "CMP-FCC-01",
name: "Flight Control Computer",
mass_kg: 3.5,
power_draw_w: 45.0,
criticality: "Tier 1",
is_lru: true
})
MERGE (pump:Component {
id: "CMP-PUMP-01",
name: "Main Hydraulic Pump",
mass_kg: 12.0,
power_draw_w: 0.0,
criticality: "Tier 1",
is_lru: true
})
MERGE (servo:Component {
id: "CMP-ACT-01",
name: "Swashplate Hydraulic Servo Actuator",
mass_kg: 5.8,
power_draw_w: 15.0,
criticality: "Tier 1",
is_lru: true
})
// 3. Create Interface Node
MERGE (hyd_line:Interface {
id: "INT-HYD-3000",
interface_type: "Fluid",
protocol_or_medium: "MIL-PRF-83282",
operating_val: 3000.0,
operating_unit: "PSI"
})
// 4. Create Failure Mode Node
MERGE (fm_leak:FailureMode {
id: "FM-LEAK-01",
name: "Hydraulic Fluid Loss",
mtbf_hours: 25000.0,
hazard_severity: "Catastrophic"
})
// 5. Establish Structural & Domain Relationships
MERGE (sys)-[:CONTAINS]->(fcc)
MERGE (sys)-[:CONTAINS]->(pump)
MERGE (sys)-[:CONTAINS]->(servo)
MERGE (pump)-[:BELONGS_TO_DOMAIN]->(d_hyd)
MERGE (fcc)-[:BELONGS_TO_DOMAIN]->(d_dig)
MERGE (servo)-[:BELONGS_TO_DOMAIN]->(d_hyd)
MERGE (servo)-[:BELONGS_TO_DOMAIN]->(d_elec)
// 6. Establish Functional, Control, & Dependency Relationships
MERGE (pump)-[:CONNECTED_TO {direction: "OUT"}]->(hyd_line)
MERGE (hyd_line)-[:CARRIES_TO]->(servo)
MERGE (fcc)-[:CONTROLS {protocol: "RS-485"}]->(servo)
MERGE (servo)-[:DEPENDS_ON {redundancy_type: "None", medium: "Hydraulic Pressure"}]->(pump)
MERGE (servo)-[:CONVERTS_ENERGY {input_domain: "Hydraulic", output_domain: "Mechanical", efficiency_pct: 88.5}]->(servo)
MERGE (pump)-[:HAS_FAILURE_MODE]->(fm_leak)
MERGE (fm_leak)-[:CASCADES_TO {propagation_probability: 0.95}]->(servo);From <https://gemini.google.com/app/73993516e543fa4a> Google Gemini (3.6 Flash)
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