codebase-consistency-reviewer

name

codebase-consistency-reviewer

description

Use when asked to review a PR, commit, commit range, branch, patch, or pasted diff for duplicated functionality, reinvented wheels, not-invented-here patterns, parallel abstractions, inconsistent naming, inconsistent settings/APIs/schemas/metrics/errors/logs, redundant config keys, non-standard implementations, or places where new code should reuse, generalize, extend, or align with existing codebase patterns and user-facing conventions. Triggers on phrases like "review for duplication", "consistency review", "is this already implemented", "does this match our conventions", "reinventing the wheel".

Codebase Consistency Reviewer

Purpose

This is not a correctness or bug review. The lens is:

• Is this functionality already implemented somewhere else, possibly under another name?
• Is there an established helper, abstraction, API, setting, schema, or naming pattern to reuse?
• Does the new code follow the standard mechanism this codebase uses for this kind of job?
• Do new user-facing names/settings/fields/metrics/errors/logs match existing conventions?
• Should the change generalize an existing implementation instead of paralleling it?
• If direct reuse isn't appropriate, should it borrow concepts from related code?

Report actionable consistency risks. Skip weak similarities.

Inputs

Accept any of: PR number, PR URL, commit SHA, commit range, branch name, local patch/diff text, pasted changed files, repo path or URL. Ask for missing input only when the review cannot proceed.

Workflow

1. Intake & capability detection

Detect what's available before planning:

• Local repo? (git, file reads, rg/grep)
• GitHub access? (gh CLI, GitHub MCP, web fetch)
• Subagents available for parallel lookup?
• Single language/framework or many?

If repo access exists, retrieve: the diff, changed files with surrounding context, relevant imports, touched public/semi-public APIs, touched settings/config schemas, touched CLI flags / env vars / table schemas / protocol fields / user-facing names, related tests, related docs, and the PR description, linked issues, and commit messages (the author may have justified deliberate divergence — weight stated intent before flagging).

If repo access is unavailable, work from the supplied material and state the limitation in Scope.

The main reviewer infers language/framework/conventions from the changed files. Do not assume — let the codebase tell you what "standard mechanism" means here.

2. Identify review targets

Extract concept-bearing and convention-bearing changes — not every changed line.

Cluster related changes into one target when they cooperate to do one job (a lock spread across methods + a thread + a state field is one target, not four).

Skip trivial changes: getters/setters, one-line delegations, plumbing, parameter forwarding, mechanical refactors, formatting, trivial renames, bug fixes that introduce no new concept, local helpers too small to plausibly have a canonical version.

Prioritize targets that:

• create or change a contract surface (API, setting, flag, schema field, table column, metric, log field, error, persisted/wire format, documented behavior, user-visible name)
• implement a recognizable responsibility (lifecycle, retry, timeout, caching, scheduling, locking, validation, parsing, formatting, lookup, conversion, matching, resource management)
• introduce an abstraction, helper, registry entry, extension point, or composition pattern others may copy
• look like they bypass or recreate a standard codebase mechanism
• overlap semantically with concepts that may already exist under another name
• establish a name/API/setting/test/operational convention that should match precedent

Pay special attention to "small" changes that encode durable conventions: a new column, config key, enum value, metric name, log attribute, error code, or any name users/operators/developers/downstream systems will see.

For each selected target, separate:

• the semantic concept introduced
• the literal identifiers in the new code
• likely existing names/synonyms elsewhere
• the contract surface involved, if any
• ownership/layering boundaries that may affect reuse
• what kind of precedent would help: direct reuse / reusable abstraction / naming-API precedent / setting-config precedent / schema precedent / standard-mechanism precedent / related design precedent

Filter aggressively on triviality, then investigate every remaining target. Do not artificially cap target count.

See references/review-rubric.md for target-selection principles, clustering rule, triviality filter, severity definitions, finding categories, and false-positive guidance.

3. Build search briefs

For each target, create a concise brief. The target is the concept, not the names; new code may use the wrong name. Search both the new identifiers and the underlying concept.

Brief contents and concrete query-generation tactics (synonyms, lifecycle pairs, broader/narrower terms, domain vocabulary scraped from nearby files): see references/search-strategy.md.

4. Delegate lookup investigations to subagents

For each non-trivial target, dispatch a separate subagent. Run them in parallel when subagent tooling supports it; otherwise sequentially (note this in the Search Appendix).

Each subagent receives only: the target's search brief, the relevant diff excerpt, and enough surrounding context. They search for evidence and return references and evidence, not final recommendations.

Subagent instructions and the compact report format: see references/lookup-worker-instructions.md.

5. Search strategy (no vector search assumed)

"Semantic search" here means: drive lexical tools (rg/grep) with semantically-generated queries, then read candidates and judge semantic match by reasoning over the code. Names are weak signals — open the file before believing the match.

Search local-first (same file → adjacent files → module → tests → shared utilities → global). Use tests and docs as precedent sources (they reveal canonical constructors, fixtures, expected errors, user-facing terminology). Look explicitly for bypassed standard mechanisms (registries, factories, config schemas, error classes, logging/metrics helpers, lifecycle hooks, DI, test utilities, naming/schema conventions).

Full strategy: references/search-strategy.md.

6. Validate findings (do not trust subagents blindly)

For each candidate finding, the main reviewer compares new code and precedent on: semantic purpose, behavior, inputs/outputs, side effects, lifecycle, error semantics, performance/concurrency constraints, ownership, layering, dependency direction, public-API implications, compatibility, test coverage, and whether the precedent is current vs. legacy/deprecated/special-purpose. Check whether the PR description already justifies divergence.

Promote a target to a finding only when there's a concrete, actionable recommendation or a meaningful consistency risk. Park inconclusive or non-actionable matches in "Checked but not flagged".

Do not:

• recommend reuse that creates bad coupling or violates ownership/layering
• flag superficial name similarity as duplication
• ignore naming/setting/schema/API consistency just because implementations differ
• recommend generalization for its own sake
• penalize deliberate divergence the PR description already explains

7. Produce the report

Use the structure in references/output-template.md:

• Scope — inputs, changed files, capabilities used, parallel vs sequential, language/framework, limitations
• Executive Summary — most important findings with counts by category
• Findings — each with Severity, Category, Changed code refs, Existing precedent refs, Analysis, Recommendation, optional patch sketch, Confidence
• Checked But Not Flagged — significant targets investigated without raising a finding
• Search Appendix — targets, subagents (parallel or sequential), key anchors/query expansions, files/tests/docs inspected, uncertainty, limitations

Sections may be omitted when empty. Small reviews may collapse the Search Appendix to one paragraph.

Style

• Practical, specific, codebase-aware. Concrete recommendations over abstract advice.
• Don't overclaim. "Conceptually similar" stays "conceptually similar."
• Local conventions govern local code; global conventions govern shared/public surfaces.
• User-facing knobs (settings, names, APIs, schema fields, metrics, logs, errors, persisted formats) get extra attention — those inconsistencies are expensive to fix later.
• Keep the final report focused on actionable risks. Don't dump every weak similarity from search.

References

• references/review-rubric.md — target selection, clustering, triviality, severity, categories, FP guidance
• references/search-strategy.md — briefs, query generation, lexical-driven semantic search, layering, anchors
• references/lookup-worker-instructions.md — subagent dispatch and report format
• references/output-template.md — final report structure and finding format