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Claude Prompts for Code Review

Battle-tested Claude prompts for security, performance, readability, and architecture reviews—with copy-paste templates for PRs.

Claude excels at long-context code review when you give it a clear scope, repository conventions, and severity rubric. These prompts are written for pull-request review, pre-merge audits, and learning sessions—not for rubber-stamping every line.

Paste a PR diff or file bundle, or attach context via upload on prmptly.dev before running reviews. For auto-structured review prompts, try Generate a prompt.

How to brief Claude for code review

Start with:

  • **Scope:** files or commits in scope; what is out of scope (generated files, vendored deps)
  • **Standards:** language version, framework, style guide links
  • **Risk profile:** auth, payments, PII, infra—tells the model where to spend attention
  • **Output contract:** severity tags, file:line references, suggested patch format

Without that contract, you get generic advice (“consider adding tests”) instead of actionable findings.

Universal PR review prompt

You are a staff engineer performing a pull request review.

**Repository context:** [LANGUAGE/FRAMEWORK]. Style: [LINK OR SUMMARY]. Branch goal: [ONE SENTENCE].

**Diff or files:** [PASTE DIFF]

**Instructions:** 1. List findings tagged BLOCKER, MAJOR, MINOR, NIT. 2. Each finding: file path, line or hunk reference, issue, why it matters, suggested fix (code snippet if ≤15 lines). 3. Note missing tests and observability (logs, metrics) separately. 4. End with a merge recommendation: Approve / Approve with nits / Request changes. 5. Do not invent APIs or files not shown in the diff.

Pair this with team-specific checklists below.

Security-focused review

Act as an application security engineer. Review this change for OWASP Top 10 issues, especially injection, broken access control, SSRF, and secrets exposure.

**Threat model:** [WEB APP / API / CLI]. Auth: [SESSION/JWT/API KEY]. Data sensitivity: [NONE/PII/PCI].

[PASTE CODE]

Flag: - Missing authorization on new routes or mutations - Unsafe deserialization, eval, shell calls - Hardcoded secrets or tokens in logs - SQL/NoSQL concatenation, XSS sinks, open redirects - Dependency version downgrades

Output a table: Severity | Location | Issue | Remediation | CWE if applicable.

For broader secure coding habits, see Best ChatGPT Prompts for Developers—many templates transfer to Claude.

Performance and scalability

Review for performance regressions in [SERVICE/COMPONENT].

Consider: N+1 queries, unbounded loops, sync I/O on hot paths, missing indexes, cache stampede, large payload serialization, React re-renders, memory leaks in subscriptions.

[PASTE CODE]

For each issue: expected symptom in production, how to measure, fix sketch, tradeoffs. Ignore micro-optimizations unless in tight loops at scale [QPS/TRAFFIC].

Readability and maintainability

You are a readability reviewer, not a security reviewer. Focus on naming, function length, nesting, error handling clarity, and comment noise.

Rules from our guide: [PASTE STYLE RULES]

[PASTE CODE]

Suggest refactors as optional follow-ups—do not block on taste unless it violates documented standards.

Architecture and API design

Evaluate this change against our architecture principles: [LIST e.g. thin controllers, domain in services, idempotent webhooks].

[PASTE DESIGN DOC OR DIFF]

Questions to answer: - Does this introduce cyclic dependencies or leaky abstractions? - Are public API changes backward compatible? - Is state ownership clear? - What failure modes are unhandled?

Deliver a short ADR-style note: Context, Decision, Consequences, Alternatives rejected.

Test quality review

Review only test files and test-related production helpers.

[PASTE TESTS]

Assess: coverage of happy path vs. edge cases, assertion strength (avoid trivial expects), flakiness risks (timers, network), fixture realism, test naming. Propose three high-value tests still missing.

Database migration review

Review SQL migration: [PASTE MIGRATION]

Check: locking risk on large tables, backward compatibility, rollback feasibility, index creation concurrently (Postgres), default values on NOT NULL columns, data backfill strategy.

Frontend component review

Review React/Next component for accessibility and UX regressions.

[PASTE COMPONENT]

Check: keyboard nav, focus management, aria labels, form labels, loading/error/empty states, hydration mismatches, unnecessary client components, image optimization.

Dependency and supply chain

Review package.json / lockfile changes.

List new dependencies with license, maintenance signals, bundle size impact, and whether native bindings complicate deploy. Flag typosquatting or version pinning oddities.

Docs and comments only

Review docstrings and README updates for accuracy against code behavior. Flag docs that promise guarantees the code does not enforce.

Multi-file feature review (long context)

When the PR spans many files, use this structure:

**Feature summary:** [USER STORY] **Files in scope:** [LIST] **Critical paths:** [e.g. checkout, login]

Review in phases: (1) data model and API contracts, (2) business logic, (3) UI, (4) tests. Synthesize cross-cutting issues once, not per file in isolation.

Reviewer tone and team norms

Add when you want constructive tone:

Assume authors are competent. Prefer questions over accusations. Mark uncertain findings as “verify.” Separate must-fix from suggestions.

Post-review: fix implementation prompt

From the review findings below, implement fixes for BLOCKER and MAJOR only. Show a minimal patch per finding. Do not refactor unrelated code.

[PASTE FINDINGS]

Compare Claude vs. ChatGPT for review

Use Claude when diffs exceed short context or you need nuanced security reasoning. Use ChatGPT for quick snippets or stack-specific boilerplate. Standardize prompts so outcomes are comparable—see Prompt Engineering Best Practices for 2026.

Operational tips

  • Chunk giant PRs; review by subsystem.
  • Redact secrets before paste; rotate if leaked.
  • Log prompt versions in PR template so reviewers stay consistent.
  • Teams on API plans should monitor usage via pricing.

Related learning

New to prompting? What is Prompt Engineering? A Beginner's Guide. Need marketing-side templates? 50 Best ChatGPT Prompts for Marketing.

Build your review library one prompt at a time, tune severity definitions with the team, and measure whether repeat findings drop quarter over quarter—that is how code review AI becomes an asset instead of noise.