Use when the user proposes — or the agent detects — a deviation from constitutional defaults that requires an Architecture Decision Record. Triggered by proposals to extract microservices, drop foreign keys, denormalize without measured evidence, store transactional truth in Neo4j, skip Row Level Security, skip the transactional outbox, run destructive migrations, use database-per-service, or any explicit override of a constitutional principle. Drafts a structured ADR with context, decision, consequences, alternatives rejected, migration path, validation criteria, and constitutional sections affected — and refuses to proceed with the underlying work until the ADR is at least Proposed status.

Use when designing or reviewing database architecture for high-demand multi-tenant commerce platforms — including PostgreSQL schema design, foreign keys, indexes, JSONB usage, multi-tenant isolation with Row Level Security, transactional outbox, Neo4j GraphRAG projections, event sourcing decisions, audit logging, partitioning, expand/contract migrations, and product/inventory/order modeling for restaurants, boutiques, drugstores, retailers, distributors, grocery, hardware, or appliance businesses. Triggered by any request to design tables, design schemas, create migrations, model products/variants/inventory/orders/payments, choose between monolith and microservices, choose between PostgreSQL and Neo4j as source of truth, model multi-tenant data, design event flows, or review an ER diagram.

Use when reviewing or designing how Neo4j and GraphRAG interact with PostgreSQL transactional truth — including any feature involving recommendations, semantic product search, ingredient relationships, substitution suggestions, complementary products, AI-assisted discovery, vector search combined with graph traversal, or any proposal that puts orders, inventory, payments, prices, or tenant access rules into Neo4j. Evaluates architectural proposals for data boundary violations, identifies sync pattern errors between Neo4j and PostgreSQL, produces structured design review feedback with severity-ranked findings, counter-proposals with Mermaid diagrams, eventing changes, and re-projection plans. Triggered by mentions of GraphRAG, Neo4j, knowledge graph, recommendations engine, semantic search, vector + graph hybrid search, AI product discovery, or any design that crosses the PostgreSQL ↔ Neo4j boundary.

Creates Postgres triggers that fire database webhooks to Edge Functions or external endpoints on INSERT/UPDATE/DELETE events. Configures pg_net for HTTP callouts and payload serialization. Use when implementing event-driven workflows, database webhooks, trigger-based notifications, or automated pipelines on table changes.

Inspects database health by analyzing slow queries, identifying bottlenecks, and checking Postgres performance indicators. Use when diagnosing database issues, checking slow queries, inspecting DB health, finding performance bottlenecks, or troubleshooting Supabase Postgres problems.

Creates Postgres INSERT triggers that fire Edge Functions for document chunking and embedding generation. Configures the ingestion pipeline from raw document insert to vector storage. Use when building RAG ingestion, embed-on-insert pipelines, database-driven document ingestion, or automated embedding workflows.

Creates Postgres functions to validate storage path payloads and prevent directory traversal. Enforces tenant-safe file paths via storage RLS bucket policies. Use when configuring Supabase storage buckets, writing storage RLS policies, or implementing tenant-scoped file uploads.

Sets up Prometheus scrape jobs targeting the Supabase metrics endpoint and configures log drain pipelines. Establishes monitoring dashboards and alerting baselines. Use when configuring Supabase monitoring, setting up Prometheus metrics, implementing log drains, or establishing observability for production Supabase deployments.

Creates RLS policies enforcing tenant_id isolation on shared-schema tables. Verifies ALTER TABLE ENABLE ROW LEVEL SECURITY before policy creation. Requires tenant_id column and custom-access-token-hook JWT claims. Use when implementing multi-tenant data isolation, tenant-safe queries, shared schema RLS, or tenant_id policy creation.

Scaffolds a complete A2A agent specifically configured for Gemini Enterprise compatibility, including the JSON-RPC root path and a health check.

Use when working with Schema Registry for Apache Kafka, Confluent Platform, or Confluent Cloud. Covers schema management (Avro, Protobuf, JSON Schema), compatibility modes, schema evolution, REST API, serializer/deserializer configuration, Kafka Connect converters, and Flink SQL integration with Schema Registry. Trigger this skill whenever the user mentions schema registry, schema evolution, Avro/Protobuf/JSON Schema serialization with Kafka, subject naming strategies, compatibility checking, or Flink SQL with Confluent formats (avro-confluent). Also trigger when users ask about data contracts, schema validation, or serializer/deserializer configuration for Kafka producers and consumers.

Optimizes RLS policies by enforcing SELECT-wrapped auth.uid() calls, mandatory B-Tree/GIN indexes on policy-referenced columns, and SECURITY DEFINER encapsulation of deep JOINs. Use when optimizing RLS performance, fixing policy full table scans, wrapping auth.uid in SELECT, or indexing columns used in RLS policies.

Configures private Realtime channels with RLS-backed authorization on the realtime.messages table. Enforces tenant-scoped Presence and Broadcast security. Use when implementing private realtime channels, realtime authorization, presence security, broadcast security, or securing the realtime messages table.

Use when the user asks to write unit tests, generate a test suite, or migrate/convert Jest or Vitest tests to Themis in Node.js/TypeScript repos. Produces Themis-native tests, runs validation commands, and applies Themis migration workflows.

Generates a Mermaid flowchart diagram of dbt model lineage using MCP tools, manifest.json, or direct code parsing as fallbacks. Use when visualizing dbt model lineage and dependencies as a Mermaid diagram in markdown format.

Use when a user needs help triaging dbt-core to dbt v2 migration errors. Runs dbt-autofix first, then classifies remaining errors into actionable categories (auto-fixable, guided fixes, needs input, blocked).

Use when migrating a dbt project from one data platform or data warehouse to another (e.g., Snowflake to Databricks, Databricks to Snowflake) using dbt Fusion's real-time compilation to identify and fix SQL dialect differences.

Creates PL/pgSQL RPC implementing Reciprocal Rank Fusion (score = 1/(k+rank)) to fuse semantic pgvector results with full-text tsvector results. Use when implementing hybrid search, RRF search, semantic plus keyword search, exact SKU and conceptual queries, or vector and full-text fusion.