Claude Code PR review: pre-review diffs faster and safer

Why PR review time balloons

PR reviews rarely take forever because the code is “hard.” They take forever because the reviewer has to reconstruct intent, risk, and impact from a diff that shows changes, not the whole story.

A small edit can hit hidden dependencies: rename a field and a report breaks, change a default and behavior shifts, tweak a conditional and error handling changes. Review time grows when the reviewer has to click around for context, run the app locally, and ask follow-up questions just to understand what the PR is supposed to do.

There’s also a human pattern problem. People skim diffs in predictable ways: we focus on the “main” change and miss the boring lines where bugs hide (boundary checks, null handling, logging, cleanup). We also tend to read what we expect to see, so copy-paste mistakes and inverted conditions can slip by.

A good pre-review isn’t a verdict. It’s a fast, structured second set of eyes that points to where a human should slow down. The best output is:

• a plain-English summary of what changed
• specific risk points (files, functions, assumptions)
• readability notes (naming, confusing control flow)
• correctness concerns (logic, error handling, data consistency)
• edge cases worth testing (inputs, time, permissions, empty states)

What it should not do: “approve” the PR, invent requirements, or guess runtime behavior without evidence. If the diff doesn’t include enough context (expected inputs, constraints, caller contracts), the pre-review should say so and list exactly what’s missing.

AI help is strongest on medium-sized PRs that touch business logic or refactors where meaning can get lost. It’s weaker when the right answer depends on deep org-specific knowledge (legacy behavior, production performance quirks, internal security rules).

Example: a PR that “just updates pagination” often hides off-by-one pages, empty results, and mismatched sorting between API and UI. A pre-review should surface those questions before a human burns 30 minutes rediscovering them.

What to ask Claude to do in a pre-review

Treat Claude like a fast, picky first-pass reviewer, not the person who decides whether the PR ships. The point is to surface problems early: confusing code, hidden behavior changes, missing tests, and edge cases you forget when you’re close to the change.

Give it what a fair human reviewer would need:

• the goal of the PR (1 to 3 sentences)
• what must not break (API shape, backwards compatibility, performance budget, security rules)
• any special constraints or tradeoffs (deadlines, partial rollout)
• the relevant diff hunks, with enough surrounding code to understand intent

If the PR touches a known high-risk area, say so up front (auth, billing, migrations, concurrency).

Then ask for outputs that you can act on. A strong request looks like:

• Summarize what changed in plain English.
• Flag readability issues (naming, structure, surprises, inconsistent patterns).
• Identify correctness risks (null handling, error paths, off-by-one, data shape mismatches).
• List edge cases and failure modes (timeouts, retries, empty inputs, partial updates).
• Suggest missing tests and what each test proves.
• Produce a short reviewer checklist and 5 to 10 “questions to ask” before merging.

Keep the human in charge by forcing clarity on uncertainty. Ask Claude to label findings as “certain from diff” vs “needs confirmation,” and to quote the exact lines that triggered each concern.

Prep the diff and context before you prompt

Claude is only as good as what you show it. If you paste a giant diff with no goal or constraints, you’ll get generic advice and miss the real risks.

Start with a concrete goal and success criteria. For example: “This PR adds rate limiting to the login endpoint to reduce abuse. It should not change the response shape. It must keep average latency under 50 ms.”

Next, include only what matters. If 20 files changed but only 3 contain the logic, focus on those. Include surrounding context when a snippet would be misleading, like function signatures, key types, or config that changes behavior.

Finally, be explicit about testing expectations. If you want unit tests for edge cases, an integration test for a critical path, or a manual UI run-through, say so. If tests are missing on purpose, state why.

A simple “context pack” that works well:

• PR goal: what changes, what users see, what should improve
• Relevant diff chunks: key files only, with enough surrounding code
• Hard constraints: performance budgets, compatibility requirements, security/privacy rules
• Test expectations: what must be covered, what was added, how to run it
• “Must not change” items: public API contracts, database schema, UX behavior, logging/auditing format

Step by step: a repeatable pre-review flow

A good Claude Code PR review works as a tight loop: provide just enough context, get structured notes back, then turn them into actions. It doesn’t replace humans. It catches easy misses before a teammate spends a long time reading.

The 5-pass flow

Use the same passes each time so results stay predictable:

1. Explain the change in plain language. Ask Claude to summarize what the PR does, what files changed, and the likely reason for the change. If it can’t explain it simply, the PR probably needs a clearer description or smaller scope.
2. Check correctness first. Look for logic errors, broken assumptions, and silent behavior changes (defaults, error handling, permissions, time zones, off-by-one).
3. Scan for missing cases. Think like a user and like production: empty inputs, nulls, retries, partial failures, concurrency, backward compatibility.
4. Review readability and upkeep. Identify confusing names, long functions, duplicated logic, unclear comments, and small refactors that lower future review time.
5. Draft review comments with pointers. Group comments by file and include a function name or quoted snippet so a human can find the spot fast.

After you get notes, turn them into a short merge gate:

Merge checklist (keep it short):

• Tests cover the new behavior and at least one edge case
• Errors are handled consistently (and logged if needed)
• No breaking change without a clear migration path
• Naming and structure match nearby code
• Risky parts have a rollback plan

End by asking for 3 to 5 questions that force clarity, like “What happens if the API returns an empty list?” or “Is this safe under concurrent requests?”

Use a simple rubric (readability, correctness, edge cases)

Claude is most helpful when you give it a fixed lens. Without a rubric, it tends to comment on whatever pops first (often style nits) and can miss the one risky boundary case.

A practical rubric:

• Readability: clear names, simple flow, small functions, comments that explain why, no dead code or leftover debug output.
• Correctness: key invariants are enforced, errors handled consistently, null/empty values safe, boundaries correct (off-by-one, rounding).
• Edge cases: empty/huge inputs, missing optional fields, time zones and daylight savings, retries that risk double-writes, concurrency races.
• Security and privacy: auth checks in the right place, no secrets in code/logs, logs don’t leak tokens or sensitive payloads.
• Compatibility and rollout safety: older clients and stored data won’t break, migrations are safe, rollback plan exists.

When you prompt, ask for one short paragraph per category and request “highest-risk issue first.” That ordering keeps humans focused.

Prompt templates that produce useful review notes

Use a reusable base prompt so results look the same across PRs. Paste the PR description, then the diff. If behavior is user-facing, add expected behavior in 1 to 2 sentences.

You are doing a pre-review of a pull request.

Context
- Repo/service: <name>
- Goal of change: <1-2 sentences>
- Constraints: <perf, security, backward compatibility, etc>

Input
- PR description:
<...>
- Diff (unified diff):
<...>

Output format
1) Summary (max 4 bullets)
2) Readability notes (nits + suggested rewrites)
3) Correctness risks (what could break, and why)
4) Edge cases to test (specific scenarios)
5) Reviewer checklist (5-10 checkboxes)
6) Questions to ask the author before merge (3-7)

Rules
- Cite evidence by quoting the relevant diff lines and naming file + function/class.
- If unsure, say what info you need.

For high-risk changes (auth, payments, permissions, migrations), add explicit failure and rollback thinking:

Extra focus for this review:
- Security/privacy risks, permission bypass, data leaks
- Money/credits/accounting correctness (double-charge, idempotency)
- Migration safety (locks, backfill, down path, runtime compatibility)
- Monitoring/alerts and rollback plan
Return a “stop-ship” section listing issues that should block merge.

For refactors, make “no behavior change” a hard rule:

This PR is a refactor. Assume behavior must be identical.
- Flag any behavior change, even if minor.
- List invariants that must remain true.
- Point to the exact diff hunks that could change behavior.
- Suggest a minimal test plan to confirm equivalence.

If you want a fast skim, add a limit like “Answer in under 200 words.” If you want depth, ask for “up to 10 findings with reasoning.”

Turn the output into a reviewer checklist

Claude’s notes become useful when you convert them into a short checklist a human can close out. Don’t restate the diff. Capture risks and decisions.

Split items into two buckets so the thread doesn’t turn into preference debates:

Must-fix (block merge)

• Correctness: expected outcome is written in one sentence and matches the ticket
• Edge cases: null/empty inputs and error paths are handled (or rejected) clearly
• Data safety: writes and migrations are safe for existing data and old code
• Tests: at least one test covers the main behavior and one covers the riskiest failure
• Observability: logs/metrics are enough to debug quickly (request id, user id, job id)

Nice-to-have (follow-ups)

• Readability: rename the most confusing identifier or add a short “why” comment
• Consistency: match existing patterns for errors, naming, and file layout
• Performance: note hot-path changes and whether they matter at current scale
• Docs: update inline docs if a new option/flag was added

Also capture rollout readiness: safest deploy order, what to watch after release, and how you’d undo the change.

Questions to ask before merging

A pre-review only helps if it ends with a small set of questions that force clarity.

Behavior and correctness

• What user-visible behavior changes, and what must stay the same?
• If this is “no behavior change,” what evidence shows outputs are identical?
• What’s the most likely production failure, and where would it show up (UI, API, data)?
• What assumptions does the code make about inputs, ordering, time, or network calls?
• Are any errors swallowed or turned into silent defaults?

Edge cases, tests, and operations

• What are the worst real inputs (empty, huge, malformed, duplicate), and what should happen?
• What common flow could trigger this twice (retries, double-click, background jobs), and is it safe?
• Which test proves the main behavior, and which test covers the riskiest edge case?
• If a test is missing, is it hard to write, or is the code hard to test?
• What will ops need: useful logs, metrics, alerts, config defaults, and rollback steps?

If you can’t answer these in plain words, pause the merge and tighten the scope or add proof.

Common traps (and how to avoid them)

Most failures are process problems, not model problems.

• Dumping huge diffs with no focus. Ask for review on 1 to 3 risky areas and paste only the related hunks plus the signatures they depend on.
• Skipping intent and expected behavior. Without a goal, the review drifts. Add two lines: what changes, and what must not change.
• Trusting confident guesses. Require quotes back to the diff. If it can’t cite evidence, treat it as a hypothesis to test.
• Letting it bikeshed style. Ask for “Must-fix” vs “Nice-to-have,” and cap style notes.
• Ignoring team standards. If your team has conventions (early returns, error types, logging format), include them.

If a PR adds a new checkout endpoint, don’t paste the whole service. Paste the handler, validation, DB write, and any schema changes. Then state: “Goal: prevent double charges. Non-goals: refactor naming.” You’ll get fewer comments, and the ones you get are easier to verify.

A realistic example: pre-review a small PR

A small, real-feeling PR: add a “display name” field to a settings screen. It touches validation (server) and UI text (client). It’s small enough to reason about, but still full of places where bugs hide.

Here are the kinds of diff snippets you’d paste (plus 2 to 3 sentences of context like expected behavior and any related tickets):

- if len(name) == 0 { return error("name required") }
+ if len(displayName) < 3 { return error("display name too short") }
+ if len(displayName) > 30 { return error("display name too long") }

- <TextInput label="Name" value={name} />
+ <TextInput label="Display name" value={displayName} helperText="Shown on your profile" />

Example findings you’d want back:

• Readability: “displayName” vs “name” is mixed across files. Pick one term so future changes don’t require mental translation.
• Correctness: server validates length, but the client doesn’t. Users can type 1 to 2 characters and only see the error after submit.
• Edge case: spaces-only strings pass len(displayName) but still look empty. Trim before validation.

Turn that into a checklist:

• Naming is consistent across API, database fields, and UI labels.
• Client-side checks match server rules (min/max, required).
• Input is trimmed (and Unicode/emoji behavior is acceptable).
• Error messages are clear and aligned between server and UI.

Quick checks, measurement, and next steps

A Claude Code PR review works best when it ends with a few fast checks:

• Behavior: what changes for a user, and what must not change
• Tests: what’s covered, what’s missing, what could flake
• Logs and errors: failures are clear and messages are usable
• Performance: new loops, N+1 queries, large payloads, extra network calls
• Security: validation, auth checks, secrets, risky defaults

To see if it’s paying off, track two simple metrics for 2 to 4 weeks: review time (opened to first meaningful review, and opened to merged) and rework (follow-up commits after review, or how many comments required code changes).

Standardization beats perfect prompts. Pick one template, require a short context block (what changed, why, how to test), and agree on what “done” means.

If your team builds features through chat-based development, you can apply the same workflow inside Koder.ai: generate changes, export the source code, then attach the pre-review checklist to the PR so the human review stays focused on the highest-risk parts.