# Kart Rc Dataobservability Agents Signal Factory Core > Signal Factory Core - Skill: `tomevault-io/kart-rc-dataobservability-agents-signal-factory-core` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/kart-rc-dataobservability-agents-signal-factory-core` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/kart-rc-dataobservability-agents-signal-factory-core/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/kart-rc-dataobservability-agents-signal-factory-core --- # Signal Factory Core The Signal Factory Core is the central processing hub that transforms raw telemetry into actionable signals, maintains the knowledge graph in Neptune, and powers the RCA Copilot with pre-computed incident context. ## Architecture Overview ``` ┌─────────────────────────────────────────────────────────────────┐ │ SIGNAL FACTORY CORE │ ├─────────────────────────────────────────────────────────────────┤ │ │ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────────────┐ │ │ │ Signal │───▶│ Event Bus │───▶│ Signal Engines │ │ │ │ Router │ │ (Kinesis/ │ │ ┌─────┐ ┌─────┐ │ │ │ │ │ │ MSK) │ │ │Fresh│ │ Vol │ │ │ │ └─────────────┘ └─────────────┘ │ └─────┘ └─────┘ │ │ │ │ │ ┌─────┐ ┌─────┐ │ │ │ │ Normalization │ │Drift│ │ DQ │ │ │ │ │ + Correlation │ └─────┘ └─────┘ │ │ │ │ + URN Resolution │ ┌─────┐ ┌─────┐ │ │ │ ▼ │ │Anom │ │Cost │ │ │ │ ┌─────────────┐ │ └─────┘ └─────┘ │ │ │ │ Canonical │ └─────────────────────┘ │ │ │ Signal │ │ │ │ │ Event │ ▼ │ │ └─────────────┘ ┌───────────────────┐ │ │ │ State & Graph │ │ │ │ ┌─────┐ ┌─────┐ │ │ │ │ │DDB │ │Nept │ │ │ │ │ └─────┘ └─────┘ │ │ │ └───────────────────┘ │ └─────────────────────────────────────────────────────────────────┘ ``` ## Signal Router ### Responsibilities 1. **Normalization**: Convert raw inputs into canonical Signal Events 2. **URN Resolution**: Standardize asset identifiers 3. **Correlation Extraction**: Extract trace_id, handoff_ids, lineage refs 4. **Schema Resolution**: Bind events to schema versions 5. **Routing**: Direct signals to appropriate engines ### Canonical Signal Event Schema ```json { "event_id": "sig-2026-01-04-001", "event_type": "SignalEvent", "timestamp": "2026-01-04T10:00:00Z", "source": { "type": "kafka", "asset_urn": "urn:kafka:prod:msk:orders_enriched", "service_urn": "urn:svc:prod:commerce:orders-enricher" }, "correlation": { "trace_id": "abc123", "span_id": "def456", "handoff_ids": { "kafka_offset": 12345, "partition": 3 }, "lineage_ref": "map-orders-enriched-v7" }, "payload": { "signal_type": "volume", "value": 1523, "unit": "messages_per_minute" }, "schema": { "id": "schema-v42", "version": 42, "registry": "confluent" } } ``` ## Signal Engines ### Freshness Engine Detects stale data based on expected arrival times. ```python class FreshnessEngine: async def process(self, event: SignalEvent) -> SignalState: asset = await self.get_asset(event.source.asset_urn) sla = asset.contracts.freshness.max_delay_minutes last_update = await self.get_last_update(asset.urn) delay = (event.timestamp - last_update).minutes if delay > sla: return SignalState( asset_urn=asset.urn, signal_type="freshness", status="BREACH", severity=self.calculate_severity(delay, sla), evidence=FreshnessEvidence( expected_delay=sla, actual_delay=delay, last_update=last_update ) ) return SignalState(status="OK") ``` ### Volume Engine Detects anomalies in data volume. ```python class VolumeEngine: async def process(self, event: SignalEvent) -> SignalState: asset = await self.get_asset(event.source.asset_urn) baseline = await self.get_baseline(asset.urn, window="7d") current = event.payload.value z_score = (current - baseline.mean) / baseline.stddev if abs(z_score) > 3: # 3-sigma anomaly return SignalState( status="ANOMALY", evidence=VolumeEvidence( expected=baseline.mean, actual=current, z_score=z_score ) ) ``` ### Schema Drift Engine Detects breaking schema changes. ```python class DriftEngine: async def process(self, event: SignalEvent) -> SignalState: current = await self.get_schema(event.schema.id) previous = await self.get_schema(f"schema-v{event.schema.version - 1}") compatibility = await self.check_compatibility(previous, current) if not compatibility.is_backward: return SignalState( status="DRIFT", evidence=DriftEvidence( breaking_changes=compatibility.changes, affected_consumers=await self.get_consumers(event.source.asset_urn) ) ) ``` ### Contract Engine Validates data against contracts. ### DQ Engine Processes Deequ results and quality metrics. ### Anomaly Engine ML-based anomaly detection across all metrics. ### Cost Engine Tracks and allocates compute costs. ## Neptune Graph Schema ### Node Types ```gremlin // Asset nodes g.addV('Asset') .property('urn', 'urn:kafka:prod:msk:orders_enriched') .property('type', 'kafka-topic') .property('tier', 1) .property('owner', 'orders-team') // Service nodes g.addV('Service') .property('urn', 'urn:svc:prod:commerce:orders-enricher') .property('language', 'java') .property('framework', 'spring-boot') // Incident nodes g.addV('Incident') .property('id', 'INC-2026-01-04-001') .property('status', 'ACTIVE') .property('severity', 'P1') // Evidence nodes g.addV('Evidence') .property('type', 'schema_drift') .property('confidence', 0.95) ``` ### Edge Types ```gremlin // Lineage edges g.V(service).addE('PRODUCES').to(topic) g.V(service).addE('CONSUMES').from(topic) // Incident edges g.V(incident).addE('AFFECTS').to(asset) g.V(incident).addE('SUPPORTED_BY').to(evidence) // Schema edges g.V(schema_v42).addE('SUPERSEDES').to(schema_v41) ``` ## DynamoDB State Tables ### SignalState Table ```json { "pk": "urn:kafka:prod:msk:orders_enriched", "sk": "signal#freshness", "status": "OK", "last_value": 1523, "last_update": "2026-01-04T10:00:00Z", "ttl": 1704448800 } ``` ### IncidentContextCache Table ```json { "pk": "INC-2026-01-04-001", "incident_id": "INC-2026-01-04-001", "primary_asset": "urn:kafka:prod:msk:orders_enriched", "top_evidence": ["schema_drift_v42", "volume_drop_10am"], "blast_radius": ["consumer-a", "consumer-b", "delta-table"], "timeline": [...], "updated_at": "2026-01-04T10:05:00Z" } ``` ## Scripts - `scripts/signal_router.py`: Main routing and normalization - `scripts/freshness_engine.py`: Freshness signal processing - `scripts/volume_engine.py`: Volume anomaly detection - `scripts/drift_engine.py`: Schema drift detection - `scripts/graph_writer.py`: Neptune graph operations - `scripts/state_manager.py`: DynamoDB state operations ## References - `references/signal-schema.json`: Canonical signal event schema - `references/graph-schema.md`: Neptune graph model - `references/engine-configs/`: Per-engine configuration ## Configuration ```yaml signal_factory: router: input_topic: "raw-telemetry" output_topic: "canonical-signals" engines: freshness: enabled: true check_interval_seconds: 60 volume: enabled: true baseline_window_days: 7 drift: enabled: true storage: neptune: endpoint: "wss://neptune.us-east-1.amazonaws.com:8182/gremlin" dynamodb: signal_state_table: "SignalState" incident_cache_table: "IncidentContextCache" ``` --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/kart-rc) — claim your Tome and manage your conversions.