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AI Engineering · Version 1.1.0 · Reviewed 2026-08-02

Custom Reviewer Designer

Make AI behavior measurable and safer for reviewer scope design and finding schema definition with evidence, explicit trade-offs, and a verification plan.

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

Designs bounded specialist reviewers with precise scope, evidence contracts, severity calibration, and structured findings. It grounds the decision in the failure class, affected paths, positive and negative examples, evidence sources, and escalation boundary and explicitly prevents a broad expert persona producing plausible advice outside its evidence or duplicating existing reviewers.

₹199 one-time

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What this skill helps you do

  • Reviewer scope design
  • Finding schema definition
  • Reviewer evaluation set

How Custom Reviewer Designer works

You provide

Prompts, model versions, evaluation data, and observed failures

It inspects

Failure class and context sufficiency for reviewer scope design

It decides

A finding schema definition change with one variable moved

You verify

Pass rate per case class against a pinned baseline

What it checks first

Custom Reviewer Designer designs bounded specialist reviewers with precise scope, evidence contracts, severity calibration, and structured findings. It grounds the decision in the failure class, affected paths, positive and negative examples, evidence sources, and escalation boundary and explicitly prevents a broad expert persona producing plausible advice outside its evidence or duplicating existing reviewers. Use it when the work involves Reviewer scope design, Finding schema definition, Reviewer evaluation set.

  1. Whether failures are systematic across an input class or random, which separates capability from sampling.
  2. Whether evaluation data overlaps the data used to build the prompt or model.
  3. Version pinning across model, prompt, retrieval, and tools so regressions are attributable.
  4. Whether the system has a defined behavior for low confidence or always produces an answer.

Failure modes it recognizes

  • A silent quality regression after a provider updates a model behind an unversioned alias.
  • Training and serving computing a feature differently, producing an offline-to-online gap.
  • Evaluation overfitting because the prompt was tuned on the scoring examples.
  • Confident fabrication when context is insufficient because no refusal path exists.
  • Cost and latency dominated by a small tail of very long inputs absent from the test set.

Answers it will reject

  • Judging quality by reading a few outputs, which cannot detect a small regression.
  • Using a larger model to compensate for missing context, paying more for the same failure.
  • Using a model as judge without validating it against human labels on the same rubric.

Decision rules it applies

  • Establish a labeled set and a baseline before changing anything.
  • Pin every version and change one component at a time.
  • Define and test the refusal path; a system that cannot decline will fabricate.

Evidence it asks for

  • Score per input class so an aggregate score cannot hide a broken class.
  • Log model, prompt, and retrieval versions on every request.
  • Track p95 tokens and cost per successful task rather than per call.

The method inside

  1. Map the artifact, actors, boundaries, and invariants relevant to reviewer scope design.
  2. Trace concrete failure or abuse paths for finding schema definition; do not report checklist items without a mechanism.
  3. Prioritize reviewer evaluation set 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

  • Reviewer scope design assessment
  • Finding schema definition decision and action plan
  • Reviewer evaluation set verification checklist

Evidence requirements

  • Prompts, model/version, tools, retrieval path, and examples
  • Evaluation dataset and failure cases
  • Latency, cost, privacy, and policy 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

Apply the custom reviewer designer to our current reviewer scope design work. We need a concrete decision, bounded changes, and evidence that the result is correct.

Expected output

Start with the failure class, affected paths, positive and negative examples, evidence sources, and escalation boundary. The highest-risk failure is a broad expert persona producing plausible advice outside its evidence or duplicating existing reviewers. Define one decision surface per reviewer and require every finding to include mechanism, reachability, and verification. Verify the result by running a labeled corpus containing true findings, near misses, and accepted counterexamples.

Boundaries and compatibility

Ideal for

  • Reviewer scope design: produce a decision or artifact grounded in supplied evidence.
  • Finding schema definition: produce a decision or artifact grounded in supplied evidence.
  • Reviewer evaluation set: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Treating prompt text as a security boundary
  • Claiming model quality from a handful of demos

Agent compatibility

  • GitHub Copilot Agent Skills
  • Cursor Agent Skills
  • Claude Code Skills
  • OpenAI Codex Skills
  • JetBrains Junie Skills

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.