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Data · Version 1.2.0 · Reviewed 2026-08-02

Neo4j Graph Model Advisor

Make data systems more correct and operable for graph schema design and cypher optimization with evidence, explicit trade-offs, and a verification plan.

4 method steps 6 documented failure modes 5 diagnostic checks 7 quality gates

Designs graph models and Cypher queries around traversal patterns while controlling supernodes, relationship direction, indexes, and path explosion.

₹99 one-time

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What this skill helps you do

  • Graph schema design
  • Cypher optimization
  • Supernode mitigation

How Neo4j Graph Model Advisor works

You provide

Prompts, model versions, evaluation data, and failures

It inspects

Failure class and context sufficiency for graph schema design

It decides

A cypher optimization change with one variable moved

You verify

Pass rate per case class against a pinned baseline

What it checks first

Neo4j Graph Model Advisor designs graph models and Cypher queries around traversal patterns while controlling supernodes, relationship direction, indexes, and path explosion. Use it when the work involves Graph schema design, Cypher optimization, Supernode mitigation.

  1. Whether the failure is systematic across a class of inputs or random, which separates a capability gap from a sampling issue.
  2. Whether evaluation data overlaps training or prompt-development data, which invalidates the measurement.
  3. Token distribution of inputs and outputs, since cost and latency are driven by the tail, not the mean.
  4. Whether the system has a defined behavior for low confidence, or always produces an answer.
  5. Version pinning across model, prompt, retrieval, and tools, because an unpinned component makes regressions unattributable.

Failure modes it recognizes

  • Silent quality regression after a provider updates a model behind an unversioned alias.
  • Evaluation overfitting where the prompt was tuned on the same examples used to score it.
  • Cost and latency dominated by a small number of very long inputs that were never in the test set.
  • Tool-calling loops where the model retries a failing tool without a bounded attempt budget.
  • Confident fabrication when context is insufficient because no refusal path was defined.
  • Distribution shift where production inputs diverge from the evaluation set over time.

Answers it will reject

  • Judging quality by reading a few outputs, which cannot detect a regression of a few percent.
  • Using a larger model to fix a problem caused by missing context, paying more for the same failure.
  • Fine-tuning before exhausting prompting and retrieval, which is slower to iterate and harder to reverse.
  • Using an LLM judge without validating the judge against human labels on the same rubric.

Decision rules it applies

  • Establish a labeled evaluation set and a baseline before changing anything; without a baseline there is no improvement, only change.
  • Pin every version and change one component at a time.
  • Define and test the refusal path explicitly; a system that cannot say "I do not know" will fabricate.
  • Budget latency and cost on p95 token counts, not averages.

Evidence it asks for

  • Score per input class (easy, hard, adversarial, no-answer) so aggregate scores cannot hide a broken class.
  • Log model version, prompt version, and retrieval version on every request for regression attribution.
  • Track p50 and p95 tokens and cost per successful task, not per call.

The method inside

  1. Map the artifact, actors, boundaries, and invariants relevant to graph schema design.
  2. Trace concrete failure or abuse paths for cypher optimization; do not report checklist items without a mechanism.
  3. Prioritize supernode mitigation findings by impact, likelihood, confidence, and cost of correction.
  4. Recommend the smallest defensible change, then define how an independent reviewer can verify it.

Deliverables

  • Graph schema design assessment
  • Cypher optimization decision and action plan
  • Supernode mitigation verification checklist

Evidence requirements

  • Schema, access patterns, query plans, or event contracts
  • Volume, cardinality, retention, and freshness
  • Consistency, latency, and migration constraints

Quality gates

  • Every material claim traces to supplied evidence or is labeled as a hypothesis.
  • The response follows the declared deliverable contract.
  • No execution, access, measurement, or verification is invented.
  • Secrets and personal data are redacted rather than repeated.
  • The user receives a concrete independent verification step.
  • The relevant failure modes in this domain were considered rather than only the reported symptom.
  • No listed anti-pattern was recommended as a solution.

Example task

Input

Model users, organizations, and permissions so we can answer whether one user can access a resource through nested groups.

Expected output

Represent membership and grants as directed relationships and bound traversal depth. Avoid one universal permissions node: it becomes a supernode and forces broad expansion on every authorization query...

Boundaries and compatibility

Ideal for

  • Graph schema design: produce a decision or artifact grounded in supplied evidence.
  • Cypher optimization: produce a decision or artifact grounded in supplied evidence.
  • Supernode mitigation: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Recommending indexes without a workload
  • Treating eventual consistency as universally acceptable

Agent compatibility

  • GitHub Copilot custom agents
  • Claude Agent Skills / SKILL.md
  • Any instruction-following chat model

Tool policy: Advisory by default. No tools are assumed. If the host provides tools, use read-only evidence gathering unless the user explicitly approves a scoped write or execution action.