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Productivity · Version 1.2.0 · Reviewed 2026-08-02

Code Review Batch Planner

Turn engineering context into a reliable artifact for large diff decomposition and reviewer batch assignment with evidence, explicit trade-offs, and a verification plan.

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

Partitions a large change into coherent reviewer batches that preserve dependency context and minimize duplicate reading. It grounds the decision in changed files, dependency graph, ownership, generated boundaries, semantic change groups, and reviewer capacity and explicitly prevents splitting by file count so a contract change and its consumers land in different batches without shared context.

₹199 one-time

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What this skill helps you do

  • Large diff decomposition
  • Reviewer batch assignment
  • Cross-batch dependency mapping

How Code Review Batch Planner works

You provide

Repository access, entry points, and the target change

It inspects

Real execution path for large diff decomposition, not naming

It decides

A reviewer batch assignment explanation with safe change seams

You verify

Claims confirmed against code paths or a test

What it checks first

Code Review Batch Planner partitions a large change into coherent reviewer batches that preserve dependency context and minimize duplicate reading. It grounds the decision in changed files, dependency graph, ownership, generated boundaries, semantic change groups, and reviewer capacity and explicitly prevents splitting by file count so a contract change and its consumers land in different batches without shared context. Use it when the work involves Large diff decomposition, Reviewer batch assignment, Cross-batch dependency mapping.

  1. The entry points and the data flow between them, which is the fastest way to build an accurate mental model.
  2. Where behavior is actually decided, rather than where it appears to be configured.
  3. Which parts change frequently, since those carry the most current knowledge and the most risk.
  4. The seams where a change can be made safely without a wide blast radius.

Failure modes it recognizes

  • A mental model built from naming conventions that no longer match behavior.
  • Hidden coupling through global state, events, or reflection that static reading misses.
  • Dead code that appears authoritative and misleads the reader.
  • Documentation that describes an intended design the code no longer implements.

Answers it will reject

  • Explaining what code does line by line rather than what it is responsible for and why.
  • Trusting comments and documentation over the executing code path.
  • Recommending a refactor before the current behavior is understood and covered by tests.

Decision rules it applies

  • Trace one real request end to end before generalizing about the architecture.
  • Verify a claim about behavior against the code path or a test, and label unverified claims.
  • Identify the smallest safe change point rather than the theoretically correct structure.

Evidence it asks for

  • Follow a concrete input through the system and name each file and function it reaches.
  • Use call hierarchies and references rather than text search alone to establish coupling.
  • Confirm behavior with an executable test before changing it.

The method inside

  1. Map the artifact, actors, boundaries, and invariants relevant to large diff decomposition.
  2. Trace concrete failure or abuse paths for reviewer batch assignment; do not report checklist items without a mechanism.
  3. Prioritize cross-batch dependency mapping 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

  • Large diff decomposition assessment
  • Reviewer batch assignment decision and action plan
  • Cross-batch dependency mapping verification checklist

Evidence requirements

  • Source code, discussion, notes, or existing artifact
  • Audience, decision, and acceptance criteria
  • Repository conventions and 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 code review batch planner to our current large diff decomposition work. We need a concrete decision, bounded changes, and evidence that the result is correct.

Expected output

Start with changed files, dependency graph, ownership, generated boundaries, semantic change groups, and reviewer capacity. The highest-risk failure is splitting by file count so a contract change and its consumers land in different batches without shared context. Batch by decision boundary and keep cross-cutting invariants visible to every affected reviewer. Verify the result by confirming every changed file appears once and every dependency-spanning risk has an explicit integration review.

Boundaries and compatibility

Ideal for

  • Large diff decomposition: produce a decision or artifact grounded in supplied evidence.
  • Reviewer batch assignment: produce a decision or artifact grounded in supplied evidence.
  • Cross-batch dependency mapping: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Inventing repository behavior or decisions
  • Replacing review by the accountable owner

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.