Building a Claude Skill for Code Review Automation

Code review is one of the most time-consuming parts of engineering work. It's also one of the most valuable: a good review catches bugs before production, spreads knowledge across the team, and maintains code quality over time. The challenge is that good reviews are expensive, experienced reviewers are busy, and the mechanical parts of review (checking for common patterns, security issues, naming conventions) take time away from the architectural judgment that actually requires expertise.

A well-built code review skill handles the mechanical layer reliably and thoroughly. Here's how to build one that actually works.

What a Good Code Review Skill Covers

Think of code review as having four layers. Security is first because security bugs are expensive. Correctness is second because wrong code is worse than slow code. Performance is third because most code doesn't need to be optimized until you know it's the bottleneck. Readability and maintainability is fourth because code is read more often than it's written.

A skill that covers all four layers, with appropriate specificity in each, is genuinely useful. One that's generic ("review this code for issues") is not much better than no skill at all.

The Prompt Structure

Here's a working prompt structure for a multi-language code review skill:

You are a senior software engineer conducting a thorough code review.
Review the provided code for the following, in order of priority:

SECURITY (report all findings)
- SQL injection, command injection, path traversal vulnerabilities
- Hardcoded credentials, API keys, or secrets
- Missing authentication or authorization checks
- User input used without validation or sanitization
- Insecure direct object references

CORRECTNESS (report all findings)
- Logic errors and off-by-one bugs
- Unhandled error cases and exception swallowing
- Race conditions in async or concurrent code
- Null/undefined dereferences
- Integer overflow risks

PERFORMANCE (report high-impact findings only)
- N+1 query patterns
- Inefficient loops with nested O(n²) or worse complexity
- Missing indexes implied by query patterns
- Unnecessary object allocation in hot paths

READABILITY (report medium and high severity only)
- Misleading variable or function names
- Functions exceeding 40 lines without clear justification
- Complex conditionals that could be simplified
- Missing or incorrect comments on non-obvious logic

Output format for each finding:
- SEVERITY: Critical, High, Medium, or Low
- LOCATION: line number or function name
- ISSUE: one sentence description
- EXPLANATION: why this is a problem
- SUGGESTED FIX: concrete code or approach

End with a SUMMARY section: overall assessment, top 3 priorities, and
whether this code is ready to merge with fixes or needs major rework.

Language detected from context. Apply language-specific best practices.

Handling Multiple Languages

The prompt above handles language detection automatically because Claude is capable of identifying the language from the code itself. But language-specific best practices vary significantly. Python has GIL concerns. JavaScript has callback hell and prototype chain issues. Go has goroutine leak patterns. Java has checked exception patterns that affect correctness.

For a general-purpose review skill, instruct Claude to apply language-specific best practices based on what it detects. For a language-specific skill (a Python review skill, a TypeScript review skill), encode the language-specific concerns explicitly. The more specific the skill, the more useful and accurate the output.

A TypeScript-specific review skill that knows about strict null checks, discriminated unions, and the difference between type and interface is more useful than a generic "code review" skill. Specificity is what makes skills worth buying.

Limitations to Be Honest About

The skill doesn't run the code. It can't catch runtime errors that only appear with specific inputs. It can't evaluate whether the overall architecture is correct for the problem. It doesn't know about your codebase's conventions unless you include them. It can produce false positives, flagging things that are intentional. Every output needs human review before acting on it.

These limitations don't make the skill useless. They define where the human reviewer should focus. The skill catches the mechanical issues so the human can focus on the architectural ones.

Real-World Results

Engineering teams using code review skills report catching security issues earlier, more consistent coverage of review criteria across different reviewers, and faster first-pass review cycles. The skill doesn't replace the senior engineer's review. It makes that review more efficient by front-loading the pattern-matching work.

Browse code review skills on SkillsLib to see what's available. If you want to build your own, start with the prompt structure above and tune it to your team's specific language stack and code standards.