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Code Quality · Version 1.2.0 · Reviewed 2026-08-02

Implementation Review Agent

Make a defensible decision about spec-to-code review and implementation completeness check with evidence, explicit trade-offs, and a verification plan.

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

Reviews completed implementation against its requirements, design decisions, repository conventions, and validation evidence. It grounds the decision in the approved specification, design, implementation diff, tests, build output, migrations, and documented deviations and explicitly prevents reviewing code quality alone while a requirement is missing or an unapproved design change alters the contract.

₹199 one-time

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SkillVault packages this skill in the open Agent Skills format for five leading coding tools.

What this skill helps you do

  • Spec-to-code review
  • Implementation completeness check
  • Design conformance assessment

How Implementation Review Agent works

You provide

The code, its invariants, and how failures currently surface

It inspects

Error paths and lifetime handling for spec-to-code review

It decides

A implementation completeness check change that makes invalid states unrepresentable

You verify

A deliberately invalid input fails clearly at the boundary

What it checks first

Implementation Review Skill reviews completed implementation against its requirements, design decisions, repository conventions, and validation evidence. It grounds the decision in the approved specification, design, implementation diff, tests, build output, migrations, and documented deviations and explicitly prevents reviewing code quality alone while a requirement is missing or an unapproved design change alters the contract. Use it when the work involves Spec-to-code review, Implementation completeness check, Design conformance assessment.

  1. Whether errors are handled where they can be resolved or merely passed upward with less context.
  2. Whether types make invalid states unrepresentable or merely document intent.
  3. Ownership and lifetime of resources, and whether every path releases what it acquired.
  4. Whether abstractions hide complexity or relocate it somewhere harder to inspect.

Failure modes it recognizes

  • A caught exception logged and swallowed, allowing execution to continue with invalid state.
  • Error types collapsed into a single generic type, losing the ability to handle cases differently.
  • Nullable fields encoding several distinct meanings, forcing every caller to guess.
  • A helper abstraction with one caller, which adds indirection without removing duplication.
  • Silent coercion masking a type mismatch until it surfaces as corrupt data.

Answers it will reject

  • Rewriting for elegance without a behavioral test suite, which converts known code into unknown risk.
  • Adding a lint rule to enforce a pattern nobody has justified.
  • Treating warnings as noise, which trains the team to ignore the one that matters.

Decision rules it applies

  • Fail fast on invalid state rather than continuing with a defaulted value.
  • Encode invariants in types and constraints where the language allows it.
  • Prefer local clarity over global cleverness; the reader is the constraint.

Evidence it asks for

  • Confirm each error path is exercised by a test rather than assumed correct.
  • Check that a deliberately invalid input produces a clear failure at the boundary.
  • Compare behavior before and after refactoring with characterization tests.

The method inside

  1. Map the artifact, actors, boundaries, and invariants relevant to spec-to-code review.
  2. Trace concrete failure or abuse paths for implementation completeness check; do not report checklist items without a mechanism.
  3. Prioritize design conformance assessment 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

  • Spec-to-code review assessment
  • Implementation completeness check decision and action plan
  • Design conformance assessment verification checklist

Evidence requirements

  • Functional and quality requirements
  • Scale, latency, consistency, cost, and compliance constraints
  • Current topology and alternatives considered

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 implementation review agent to our current spec-to-code review work. We need a concrete decision, bounded changes, and evidence that the result is correct.

Expected output

Start with the approved specification, design, implementation diff, tests, build output, migrations, and documented deviations. The highest-risk failure is reviewing code quality alone while a requirement is missing or an unapproved design change alters the contract. Check behavioral completeness first, then correctness, operability, maintainability, and evidence quality. Verify the result by tracing each requirement to implementation and tests and confirming every deviation has accountable approval.

Boundaries and compatibility

Ideal for

  • Spec-to-code review: produce a decision or artifact grounded in supplied evidence.
  • Implementation completeness check: produce a decision or artifact grounded in supplied evidence.
  • Design conformance assessment: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Producing a generic reference architecture without requirements
  • Hiding material trade-offs behind best-practice language

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.