- seeds/ontology.yaml: 59 entity types (5 abstract, is-a hierarchy), 46 relationship types with cardinality, 6 lifecycles with terminal states and named precondition checks - seeds/inventory.yaml: 110 entities / 142 relationships translated from legacy inventory.yaml (fleet, services, ingress, storage, governance, archaeology); thin spots marked for backfill - seeds/policy.yaml: 4 risk classes, 27 approval rules (hierarchy-aware, per-entity overrides), autonomy kill-switch off (cold start) - api/openapi.yaml: full v1 REST contract (40 paths), RFC 9457 errors, cursor pagination, idempotency, ETag/If-Match, scopes; redocly-clean - docs/adr/0001-0010: initial architecture decision records - scripts/validate-seeds.py: Phase 0 gate — hierarchy, lifecycles, endpoints, cardinality, policy cross-refs (0 errors) - plan: layer CHECK gains 'meta' (root type), cardinality gains 'many-to-one' Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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ADR 0002 — PostgreSQL + TimescaleDB as the only datastore
Status: accepted (2026-07-07) · Plan: rev 3
Context
The OS needs a graph (entities/relationships), operational tables (signals/executions/approvals), a learning corpus, time-series metrics, audit and event logs. Alternatives: dedicated graph DB (Neo4j), dedicated TSDB (Prometheus/VictoriaMetrics), or one Postgres.
Decision
One PostgreSQL 16 instance with the TimescaleDB extension (timescale/timescaledb:2-pg16). Graph traversal via recursive CTEs (cycle-safe blast_radius); time-series via hypertables + continuous aggregates + retention policies; events via table + LISTEN/NOTIFY.
Consequences
- One backup/restore/DR story, one connection pool, transactional consistency between graph and operational writes (e.g. event emission in the same transaction as state change).
- Postgres is the accepted SPOF — mitigated by daily pg_dump + WAL PITR + off-host copies + monthly restore drills; streaming replication is the future path if needed.
- Homelab graph scale (hundreds of nodes) is far below where a dedicated graph DB pays for itself.