code-review

name

code-review

description

Perform a language-agnostic first-pass code review covering logic errors, bad practices, operation ordering, magic strings, pattern improvements, strict type checking, and SQL injection. Use when asked to review code, audit a file, or check a diff. Accepts a single file, a diff with multiple hunks, or can explore the repo when needed to resolve external references. If no input is provided, ask the user to share a file or diff before proceeding.

Code review

Quick start

If the user has not provided a file or diff, ask:

"Please share the code you'd like reviewed — paste it directly, upload a file, or provide a git diff."

This is a first-pass, language-agnostic review. It surfaces issues common across all languages before any in-depth language-specific analysis.

Input handling

Input type	How to handle
Single file	Review the full file
Git diff (one or more hunks)	Review changed lines and their surrounding context
Ambiguous reference (e.g. call to external method)	Explore the repo only as needed to verify whether the reference exists and what it does; do not read files unrelated to the finding

Review passes

Run all six passes on every review. Report findings grouped by severity.

1. Logic errors

• Infinite or unreachable loops (missing exit condition, invariant that never changes)
• Off-by-one errors in indices, ranges, or pagination
• Null / undefined dereferences that can crash at runtime
• Dead code that can never be reached
• Incorrect boolean logic (de Morgan violations, short-circuit mistakes)
• Race conditions or incorrect execution-order assumptions

2. Operation ordering

Flag sequences where order creates correctness or reliability risk:

• Side effects before guards (e.g. sending an email before committing a DB transaction, charging a card before persisting the order)
• Mutations or writes before validation of input
• Resource acquisition without matching release in all exit paths (locks, file handles, connections)
• Audit logging placed after the action it should record

3. Bad practices

• Raw request / user input used without sanitisation or validation
• Overly broad exception handling that silently swallows errors
• Missing error handling on I/O, network, or DB calls
• Unsafe type coercion or implicit casting between incompatible types
• Strict equality not enforced where type safety matters (e.g. == instead of === in JS/TS, or comparing values of different types without explicit conversion)
• Functions with hidden side effects or unclear single responsibility

4. Security

• SQL injection: string concatenation or interpolation used to build queries instead of parameterised queries / prepared statements
• Hardcoded secrets, credentials, tokens, or environment-specific values
• Exposed sensitive data in logs, error messages, or API responses

5. Magic strings and values

• Unnamed string or numeric literals used inline instead of named constants (e.g. status === "active", retries > 3, role === "admin")
• Duplicated literals that should share a single source of truth
• Suggest extracting to a constant, enum, or config value with a descriptive name

6. Pattern improvements

Flag cases where a well-known pattern would meaningfully reduce coupling, improve testability, or clarify intent. Suggest only when the improvement is concrete and actionable — do not suggest patterns for their own sake:

• Hard-coded dependencies that could use dependency injection (class instantiates its own collaborators, making unit testing difficult)
• Repeated conditional chains that could be replaced with a strategy or map lookup
• Procedural code that obscures a domain concept a named abstraction would clarify
• When exploring the repo reveals that a dependency is always used in the same way, note if a factory or builder would remove duplication

Output format

For each finding:

• Location — file name and line number, or function / block if unavailable
• Severity — critical / high / medium / low
• Pass — which of the six passes caught it
• Description — what the problem is and why it matters
• Fix — a corrected snippet or concrete actionable suggestion

Group all findings by severity (critical first). End with a short summary of overall quality and the top 1–3 issues to address immediately.

Severity guide

Severity	Meaning
Critical	Data loss, security breach, or crash in production
High	Likely bug or incorrect behaviour under normal use
Medium	Subtle risk or meaningful maintainability concern
Low	Best-practice deviation with minor practical impact