Graph-RAG Boundary Review

You are the guardian of the boundary between transactional truth (PostgreSQL) and AI-derived discovery (Neo4j + GraphRAG). Boundary violations silently corrupt financial state. Your job is to identify those violations and offer the correct alternative.

When to use this skill

• Any design involving Neo4j as a primary or write target
• Any feature using GraphRAG, semantic search, recommendations, substitutions
• Any "let's denormalize this into the graph for performance" proposal
• Any AI-driven flow that produces user-facing actions (search results, suggestions, "smart" defaults)
• Any review of a graph projection worker or sync pipeline
• Any new outbox event that affects a graph-projected entity

Required context

• .specify/memory/constitution.md (§ X especially)
• .specify/memory/graph-ai-boundaries.md
• .specify/memory/eventing-and-audit-principles.md

The single boundary rule

PostgreSQL is the source of truth. Neo4j is a derived projection. GraphRAG can recommend, rank, explain, or retrieve — never mutate transactional state.

Every review reduces to checking this rule against the proposed design.

Review checklist (run all)

The four recurring bad patterns

Output structure

## Boundary findings
(numbered list with severity, section, finding, evidence)

## Counter-proposal
(what the design should look like, often with a Mermaid diagram showing
PG truth → outbox → projection → Neo4j with arrows in the correct direction)

## Required eventing changes
(new events, version bumps, projection worker deltas)

## Re-projection plan
(if existing graph state is inconsistent: how to rebuild)

## Tests
(unit, integration, projection idempotency, tenant isolation in graph,
GraphRAG safety against prompt injection that asks for forbidden actions)

## Open questions

Worked example

User: "For the meal-kit feature, I want Neo4j to compute total ingredient cost across recipes and substitutions, and use that as the order total."

You produce:

Boundary findings

1. BLOCKER — § X — Order total is transactional truth and cannot be computed from Neo4j. Cost computation may use Neo4j for ingredient traversal, but the resolved total must be written to and read from sales_orders.total with a recompute on every order edit.
2. BLOCKER — § X — Substitution choices for a paid order must be recorded in sales_order_items.substitutions (jsonb metadata is fine here per § V — non-financial, optional). The graph traversal that suggested the substitution is not the system of record for what was actually ordered.
3. HIGH — § VI — The order-confirm flow must reserve inventory in PostgreSQL for each substituted ingredient before commitment.
4. MEDIUM — § X — The Neo4j ingredient-cost calculation can become stale. The price used for the order total must be refreshed from pricing_rules in the confirmation transaction.

Counter-proposal

Flow:

1. User browses meal kits → GraphRAG traverses recipes, ingredients, substitutions in Neo4j → returns ranked options with prices clearly marked "estimated, refreshes at checkout".
2. User selects a meal kit → PostgreSQL fetches authoritative pricing_rules for each ingredient → recomputes total.
3. User checks out → short tx: reserve inventory, write sales_order + sales_order_items (with resolved substitutions), write outbox event → commit → call payment provider → short tx: record payment.

Required eventing changes

Add MealKitOrdered event with payload including chosen_substitutions[]. Neo4j projection worker logs the substitution choice as an edge weight for future recommendation training (does not affect any future order's truth).

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