An AI-generated pull request is safe to merge only when you can show how its risky behavior is tested, authorized, rolled back, and stopped across every execution path. Fluency, green lint, and a tidy file list are not evidence of production safety.
This review rubric uses eight blockers. A blocker is a defect that can page someone, corrupt data, leak access, create unsafe side effects, or ship a change nobody can undo without a forward-fix. Every finding needs a precise path:line reference.
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The review rule: classify risk, not prose quality
The framework comes from lugy’s first-person DEV Community article, published September 2, 2026. The author describes reviewing code produced with Cursor and Claude Code after deciding that fluent, lint-clean diffs were not enough. It is an engineering rubric, not an incident-rate study or benchmark, so use it as a practical gate rather than as statistical proof that AI code fails more often.
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Use three outcomes for every observation:
- BLOCKER: the author must fix it or explain in the pull-request body why it is being accepted before merge.
- WARNING: record one sentence, but do not delay the merge by itself.
- SILENT: do not mention it. Minor style preferences and harmless refactoring belong here unless they create one of the defined risks.
A passing review has zero blockers. Warnings can remain. A blocker merged without a written acceptance rationale is a process failure, even when the code compiles.
#1 Best Overall
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Require evidence in the form path:line for every finding. “This looks risky” is not a finding. As the rubric puts it, “If you cannot cite it, it is not a finding.”
Eight blockers at a glance
| Blocker | What must be true before merge | Typical proof |
|---|---|---|
| No rollback | The change can be disabled, reverted, or safely forward-fixed. | Feature flag, trivial revert, named rollback owner and procedure. |
| Contract break | Existing clients continue to parse and interpret requests, responses, events, and CLI options. | Additive field or versioned endpoint, with compatibility tests. |
| Data without a backfill | Existing rows remain valid, readable, and semantically correct. | Expand/migrate/contract sequence or linked safe migration. |
| Authz gap | Every sensitive path enforces the resource’s established authorization guard. | Unauthenticated, cross-account, and post-action tests. |
| Tests that do not prove the change | Tests fail when the production hole exists. | Assertions on status, persisted data, denied access, and rollback. |
| Flag gap | Every entry point, worker, and process obeys the rollout control. | Flag-off tests for HTTP, queues, jobs, and scheduled paths. |
| Failure behavior | Retries and errors cannot duplicate side effects or falsely report success. | Idempotency, unique constraints, timeout tests, surfaced failures. |
| Secrets, tokens, or raw PII exposure | Sensitive values never enter logs, bundles, URLs, git, or error output. | Redaction matching production practice and negative logging tests. |
1. No rollback: prove the escape hatch
Block a user-visible or data-writing change when it cannot be turned off or reversed without shipping a forward-fix. The review question is not merely “Can we deploy this?” It is “What can the on-call engineer do when the behavior pages someone or starts damaging data?”
Changes that trigger the blocker
- A risky request path is enabled for everyone with no feature flag.
- A migration permanently changes or removes information.
- An endpoint is deleted without a deprecation or sunset period.
- Old code is removed while the replacement is enabled globally, leaving no easy revert.
What a pass looks like
Accept the change when a flag can disable the behavior, the implementation is trivially revertible, or the pull request names a concrete rollback procedure and the person or team authorized to execute it. The procedure should identify what is switched off, which data is safe to retain or restore, and which deployment or configuration action starts the recovery.
A reviewer should be able to point to the exact flag, migration step, or commit. “We can fix it later” is a forward-fix, not a rollback. The rubric’s memorable test is: “If a review cannot name a rollback, it is a vibe check with extra tokens.”
2. Contract break: protect every current client
Block a change that alters an API, event, or command contract in a way existing consumers can mis-handle. Compatibility includes request and response shapes, status codes, error codes, event payloads, and CLI flags—not only whether the server still returns a response.
Rank #2
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Breaking changes to look for
- Renaming or removing a field that clients still read.
- Adding a newly required request field without a compatibility period.
- Narrowing a type so previously valid values are rejected.
- Changing the meaning of a field while keeping its name.
- Changing status or error codes that client logic branches on.
- Changing event payloads or command-line flags used by automation.
The invoice amount example
The source rubric uses a particularly dangerous shape change: an invoice field named amount moves from integer cents to a decimal string. A client that assumes the old unit or type can silently calculate the wrong total even though deserialization succeeds.
Use an additive field, such as a separately named decimal representation, or publish a versioned endpoint. Keep the old contract until known consumers have migrated, and test both representations where compatibility is required. A passing type check in the producer does not prove that deployed consumers understand the new meaning.
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Block schema or stored-data meaning changes when existing records could become null, invalid, unreadable, or semantically wrong. A migration that works on an empty test database can still fail against years of production data.
Concrete danger signs
- A new
NOT NULLcolumn is added with no default and no update for existing rows. - An existing enum is reused for a new meaning, so old values become ambiguous.
- Timezone, currency unit, measurement unit, or other semantics change silently.
- Only new rows receive a new field while old rows remain impossible to interpret.
- Code starts reading a field before every historical row has a valid value.
Required migration shape
Use an expand, migrate, contract sequence when the data and code need to change safely. First expand the schema in a backward-compatible way. Then backfill or dual-write while old and new readers can coexist. Verify the migrated population before switching reads. Contract—removing old columns or behavior—only after consumers and rollback plans no longer depend on them.
A linked follow-up can be acceptable when the first pull request explicitly preserves compatibility and the follow-up has an owner, ordering, and safe handoff. “The backfill will happen later” without that chain is a blocker because the deployed code already has an unsafe assumption.
Rank #3
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4. Authz gap: test who is allowed to act
Block a new or modified sensitive path when it lacks the authorization guard used elsewhere for the same resource. Authentication alone is not authorization: knowing who made a request does not prove that the person may read, change, or delete the target.
Common authorization holes
- An insecure direct object reference lets a client-supplied ID access another account’s record.
- An endpoint described as “internal” is mounted on the public router.
- The server trusts a role or account identifier supplied by the client.
- A new route bypasses the shared resource guard used by neighboring routes.
Minimum delete-endpoint proof
- Send the request without credentials and assert the established unauthenticated response, such as
401or403. - Authenticate as one account and target a record owned by another account; assert
403(or the service’s documented denial response). - Authenticate as an allowed user, perform the deletion, and verify that the target row is actually gone.
Those checks exercise both the boundary and the intended effect. A test that only calls the route as an administrator does not prove that the guard is present.
5. Tests that do not prove the change: reject coverage theater
Block risky behavior when no test would fail if the production hole existed. The key question to ask aloud is: “Name the test that would fail if this blocker were present.” If nobody can name one, the review has identified a gap even when the test count is high.
Weak tests that should not satisfy the gate
should not throwassertions that never inspect the result.- Snapshots that record implementation details without checking user-visible behavior.
- Mock assertions proving that a function was called, while the response, database, or authorization outcome is unchecked.
expect(status).not.toBe(500)as the only assertion, which allows many incorrect success codes.
Behavioral assertions to require
For a contract change, assert the exact status, shape, type, and error behavior a current client receives. For a data migration, read representative old rows and verify their values and meanings after migration. For authorization, assert denied access across accounts. For rollback, turn the flag off or execute the documented reversal and verify that the old safe behavior returns. For side effects, assert the number and identity of persisted records or external operations, not merely that a mock was invoked.
Tests should fail when the actual hole is reintroduced. That property matters more than the number of test files or lines changed.
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Rank #4
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6. Flag gap: cover every state-changing entry point
Block a risky or large partial rollout when the new path is ungated, or when the flag does not cover every process that can change state. Rollback coverage and flag coverage are separate checks: rollback asks whether the change can be undone; flag coverage asks whether turning the control off actually stops all new behavior.
The HTTP-and-worker failure mode
Suppose an HTTP handler checks flags.newIndexer, but an SQS worker continues writing the new index regardless of that flag. Flipping the flag appears to roll back the feature for requests while the worker keeps mutating state. The system is still advancing into the risky behavior, so the flag is not a real escape hatch.
How to review flag coverage
- List every entry point: HTTP handlers, queue consumers, scheduled jobs, command-line tasks, webhooks, and backfills.
- Trace where each process reads the flag and what it does when the value is false.
- Test flag-off behavior independently for each process that can write data or emit an external side effect.
- Check defaults, configuration propagation, and failure behavior when the flag cannot be loaded.
A single conditional in the most visible handler is not enough when other workers can reach the same state-changing code.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. Failure behavior: make retries safe and errors visible
Block timeouts, retries, and error handling that can duplicate side effects or report success after failure. Distributed systems retry; the question is whether a retry repeats a payment, webhook, write, or job in a harmful way.
Patterns that require a blocker
- Payment or webhook handlers have no idempotency key or equivalent deduplication.
- A timeout causes the caller to retry while the first operation may still have succeeded.
- A catch-all handler swallows an exception and returns
200. - Partial failure leaves one external operation committed and another silently skipped.
Evidence for a pass
Show that side effects are idempotent or protected by a unique constraint, and that failures surface through the documented status, error, retry, or alerting path. Exercise a timeout and repeat the request; the test should prove that the final state contains one intended effect rather than two. Exercise an exception and verify that the caller does not receive a false success.
Best Value
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“It usually succeeds” is not a failure strategy. A safe design makes duplicate execution harmless or impossible and makes unresolved outcomes visible to the system that must recover them.
8. Secrets, tokens, or raw PII exposure: keep sensitive data out of every output
Block secrets and sensitive personal data added to logs, browser or client bundles, URLs, git history, or error messages. Verbose production request-body logging is included: a payload can contain credentials, payment details, identity data, or other information that becomes broadly accessible through log systems.
Places to inspect
- Structured and debug logs, including exception context and request bodies.
- Client bundles, source maps, configuration files, and embedded test fixtures.
- Query strings, path segments, redirect URLs, and webhook URLs.
- Committed files, generated artifacts, and pull-request snippets.
- Error messages returned to users or forwarded to third-party monitoring.
What a pass requires
Redaction should match the project’s existing production practice rather than introducing an ad hoc filter that misses a field or format. Test sensitive values negatively: confirm that the token, secret, or raw personal field does not appear in captured logs, responses, URLs, or built assets. Fake placeholders in an .env.example file are silent by themselves; they are not a blocker unless they expose a real secret or create a misleading production path.
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Use the blockers in a consistent order so a polished diff cannot distract from an operational hole.
- Map the change surface. Identify user-visible paths, data writes, API and event contracts, workers, flags, authorization guards, external calls, and logging changes.
- Find the escape hatch. Locate the flag, revert commit, migration reversal, or forward-fix procedure. Record its
path:lineevidence and owner. - Check compatibility. Compare old and new request, response, event, error, and CLI contracts. Search for current consumers before accepting a rename, removal, required field, type narrowing, or semantic change.
- Trace existing data. Inspect schema changes and old rows. Require defaults, backfills, dual reads or writes, and an expand/migrate/contract sequence where needed.
- Trace authorization. Compare the new route with neighboring guarded resources. Run unauthenticated, cross-account, and authorized behavior tests.
- Trace every process. Follow the feature flag through HTTP, SQS or other workers, scheduled jobs, webhooks, and command-line paths. Test the off state everywhere state can change.
- Exercise failure. Inject timeouts, retries, duplicate deliveries, and exceptions. Verify idempotency, uniqueness, and surfaced errors.
- Inspect outputs. Search logs, URLs, bundles, git changes, and error payloads for secrets, tokens, and raw PII. Confirm redaction with a negative test.
- Demand behavioral tests. For each suspected blocker, name the test that would fail if the defect were present. Reject mock-only evidence.
- Write findings precisely. Classify each item as blocker, warning, or silent; cite
path:line; and require a written acceptance rationale for any blocker the team chooses to merge.
How to keep the review useful instead of noisy
Urgency has to be scarce or it is not urgency. Treat style nits, optional refactors, and harmless naming preferences as warnings or silence so that a real authorization gap or irreversible migration retains attention. The blocker threshold should remain tied to paging, corruption, access leakage, irreversibility, or unsafe side effects.
When comparing an AI-PR review approach, evaluate it on the same axes every time:
| Axis | Question to answer |
|---|---|
| Rollback and forward-fixability | Can operators disable or reverse the behavior without waiting for a new fix? |
| API and event compatibility | Will existing clients interpret every shape, code, flag, and meaning correctly? |
| Data migration safety | Are old rows valid, backfilled, and readable throughout deployment? |
| Authorization coverage | Does every sensitive path enforce the established resource guard? |
| Behavioral-test strength | Would a test fail when the real production hole exists? |
| Rollout and worker coverage | Does every process obey the same flag and off-state behavior? |
| Idempotency and error surfacing | Are retries harmless and failures observable rather than falsely successful? |
| Secret and PII handling | Are sensitive values excluded from logs, bundles, URLs, git, and errors? |
| Evidence granularity | Can each claim be verified at a specific path:line? |
| Reviewer-noise control | Are only production-threatening issues blockers? |
What “safe to merge” means in this rubric
Safe does not mean perfect, elegant, or free of warnings. It means the reviewer found zero unaccepted blockers and can point to evidence for the controls that matter: a rollback, compatible contracts, valid existing data, enforced authorization, behavior-proving tests, complete flag coverage, safe retry behavior, and protected sensitive output.
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