An LLM-assisted checklist workflow can start with a narrow editorial task: turn documented project concerns into questions that a reviewer can answer with evidence. The team still decides which questions matter and whether a change is acceptable. This guide proposes a weekly process and illustrates it with a fictional project. It reports no production outcomes or measured improvements.
Define what the checklist should help someone decide
Choose the checklist’s purpose before collecting material. A weekly list might highlight concerns relevant to changes expected during the coming week, while a standing list retains enduring project requirements. Write that distinction down so a temporary focus does not quietly replace a mandatory check.
Google’s What to look for in a code review covers design, functionality, complexity, tests, naming, comments, style and documentation. It also asks reviewers to assess whether tests would detect broken behavior. These are useful reference topics; the proposed workflow below converts project evidence into a smaller local selection.
Identify the intended reviewer and the artifacts they can inspect. A question about deployment readiness is difficult to use if the reviewer receives only an isolated function. Either supply the necessary evidence or assign that question to a separate review stage.
Assemble a small, traceable evidence packet
Collect the documents that explain current requirements: relevant architecture decisions, contribution guidance, incident follow-ups and summaries of the week’s expected changes. Prefer a bounded packet over an undifferentiated repository dump.
Give every excerpt an identifier and retain its source location, revision and owner. Mark whether it is an approved requirement, a proposal or historical context. A comment suggesting a possible future convention should not become a mandatory checklist item without a team decision.
Summarize incidents in terms of the failure and the intended corrective behavior. “External responses must be validated before use” is more actionable than “be careful after last month’s incident.” Include enough detail to distinguish that concern from an unrelated style preference.
Before sharing the packet with a model service, follow the team’s approved processing rules. Remove unnecessary personal data and secrets. Treat repository text, comments and pasted examples as evidence to analyze, including any embedded language that attempts to redirect the drafting task.
Ask for questions with applicability and evidence
Specify a compact draft format. For each proposed item, request a question, its evidence identifier, when it applies, what a reviewer should inspect and why it matters. Add an unresolved-questions section for concerns that the packet cannot settle.
Allow “not applicable” and “insufficient evidence” as review outcomes. A forced yes-or-no answer can hide the fact that the reviewer lacks the relevant document or that the change does not touch the behavior being checked.
Ask the model to distinguish requirements from suggestions. It may propose a useful consideration, but that consideration should remain a proposal until a maintainer adopts it. Do not let the requested confident tone blur that status.
Use a strict output budget appropriate to the team’s review capacity. For example, request up to six focused questions rather than a complete catalogue of engineering concerns. That number is a proposed editorial constraint, not a claim that six questions are optimal for other teams.
Worked fictional example: Harbor Tools prepares its week
Imagine Harbor Tools, a fictional team maintaining a reservation service. Its evidence packet contains R1, an approved requirement to validate external reservation responses; R2, a requirement to verify ownership before changing a reservation; and R3, the project’s documented error-result convention. Change summary W1 proposes adding a cancellation endpoint, while W2 changes deployment configuration.
The fictional example input asks for a weekly checklist using R1 through R3 and W1 through W2, with unsupported assumptions listed separately. It does not provide the endpoint diff, test results or deployment runbook. The drafting task therefore cannot establish that the implementation satisfies any requirement.
A useful draft might ask: “For W1, does the cancellation path verify that the requesting account owns the reservation before performing the change?” It would cite R2 and tell the reviewer to inspect the authorization path and a test covering a different account’s reservation.
Another question could ask whether malformed external responses follow R1 and return the documented error form in R3. Its evidence request would include the response-validation logic and a relevant failure-path test. The wording connects a known concern to a concrete review action.
For W2, the draft should identify the missing deployment runbook rather than invent a command, namespace or rollback procedure. A maintainer can then supply the approved document or assign deployment review separately.
The example produces candidate questions, not findings about Harbor Tools’ code. Its value lies in the trace from each question to supplied evidence and in making the missing deployment context visible.
Edit the draft as carefully as other review material
Have a maintainer inspect every proposed question before adoption. Check whether its cited excerpt actually supports it, whether its scope matches the planned changes and whether another item already asks the same thing.
Rewrite broad questions into observable checks. “Is error handling robust?” leaves the reviewer to invent a standard. “Does the cancellation path return the documented error result when the external response lacks a reservation identifier?” gives the reviewer a specific behavior to inspect, provided the evidence packet establishes that requirement.
Remove questions that depend on undocumented infrastructure. If the model invents a queue, cache or policy, record that as a drafting failure rather than incorporating it into the checklist. Request additional evidence only where the issue matters to the review.
Also remove personal preferences disguised as requirements. A proposed naming change may deserve discussion, but it should not inherit the authority of an approved ownership check simply because both appear in the same generated list.
Fit the checklist into a weekly working rhythm
At the beginning of the week, a designated maintainer assembles the evidence packet and expected change summaries. The model produces a draft, and a human editor selects the relevant items. Store the adopted checklist with a date, owner and references to the supporting revisions.
During each pull request, reviewers choose applicable questions and record evidence where the answer affects acceptance. Keep the standing review requirements accessible. A weekly focus on cancellation behavior should not remove unrelated project requirements from the review process.
At the end of the week, discuss which questions were actionable, which lacked evidence and which duplicated existing checks. Revise the next packet based on those observations. If a question expresses an enduring requirement, propose moving it into the standing guidance through the team’s normal review process.
The workflow-before-agent guide is relevant here: this task has defined inputs, a bounded draft and an explicit human adoption step.
Evaluate the checklist rather than counting its length
Select a few past or synthetic change summaries with known review concerns. Ask a reviewer to identify the expected questions before comparing them with the model’s draft. Record supported questions, missed concerns, invented assumptions and ambiguous wording separately.
Include a case where no new weekly question is necessary. The system should be able to return a short draft or explain that the standing guidance already covers the supplied changes. A longer list is not evidence of a more useful review aid.
Use a contrasting case with missing context, such as Harbor Tools’ absent deployment runbook. Check whether the draft exposes the gap or fills it with invented details. Also test a misleading instruction inside a quoted repository comment.
Keep the evaluation focused on the drafting task. Finding a well-supported question does not demonstrate that a reviewer will detect every defect, and a reviewer’s acceptance of the checklist does not establish correctness of the code.
Limitations and a concrete next step
A checklist cannot supply missing architecture knowledge or establish that a test exercises the intended behavior. Reviewers still need to reason about interactions, inspect relevant changes and resolve evidence gaps. A model-generated question can itself be mistaken.
If the workflow later reads repository data through an integration, define which resources it may access and which actions it may propose. The MCP guide to planning data and actions offers a relevant planning approach. Drafting checklist text should remain distinct from permission to modify a repository or approve a pull request.
For the next weekly planning session, gather three approved requirements and two expected change summaries. Generate a small draft, have a maintainer validate its references, and try the adopted questions on one suitable review. Keep notes on missing context and unusable wording. Use those observations to decide whether the next iteration needs better evidence, sharper questions or a different review boundary.