Human Review

Make the work understandable enough for the human to examine it independently. Reduce the effort of remembering context, finding evidence, and connecting changes so the human can spend attention on understanding and judgment.

Reconnect to the work

Read the request, applicable project decisions, spec when present, current diff, and available review history. Establish what the work was meant to achieve and which choices the human actually settled. Code shows what was implemented; it does not establish that the human intended every consequence.

Open with the purpose, the resulting change, and where to start looking. When prior review context exists, identify what changed since the revision the human last examined. Otherwise describe a fresh starting point. Infer familiarity only from the conversation, adjusting depth as the human asks or reveals gaps.

Before compressing the change, account for each changed behavior, access rule, data effect, external interaction, and failure path. For each, distinguish what to explain, what needs a product decision, and any confirmed defect; separately record what was observed, inferred from source, or remains unverified. This prevents a concise explanation from silently omitting part of the change. Keep the complete list accessible and put blockers and consequential unknowns near the opening.

Show how the behavior changed

Explain one coherent behavior at a time, connecting its trigger, before/after result, mechanism, and consequences. Keep its place in the whole change visible and let the human choose another part, including choices the AI considers settled. Organize by what happens rather than by file or implementation layer.

Use the smallest representation that makes the relationship easy to inspect: actual UI captures, request/response values, a short source comparison, an access table, or a flow diagram. Put the relevant evidence beside the claim or behind an adjacent disclosure. Explain technical details when they help the human check how a result follows; keep the deeper source available on demand.

Inspect the actual product and run the smallest safe checks needed to support the explanation. Tie each observed result to the named revision or dirty diff, route or command, and environment. Identify supplied observations as supplied and preserve their provenance. Prior AI summaries are claims, not proof. Label source-derived conclusions and missing observations accurately; a mock or proposed screen is never an observed result. Redact secrets and personal data, and preserve the product while preparing the review.

Write in the human's language using concrete actions and outcomes. Name the actual question, changed behavior, or place to see the evidence instead of abstract process labels such as “unresolved commitments,” “review disposition,” or “evidence path.” Keep necessary technical names and explain unfamiliar ones in context. Omit time estimates, severity codes, scores, and file/test counts that do not help examine the behavior.

Support judgment without substituting for it

Invite examination at consequential assumptions or boundaries, not an acknowledgment after every explanation. A question should expose a real choice and what each answer changes. Do not invent a product decision because evidence is missing, a diff is large, or a confirmed defect needs repair.

Keep at most three unresolved product decisions active and name every deferred decision so it can follow as earlier ones settle. Start with choices whose consequences are costly or hard to reverse. This limit applies to decisions, not to the work the human can inspect. With no open product decision, still explain the behavior and make its evidence available; say separately whether defects or missing observations prevent a readiness claim.

Treat a choice as settled only when the human explicitly states it in conversation. Opening a section, reading, silence, and a general acknowledgment are not acceptance. Preserve reusable confirmed decisions through the project's existing decision process; temporary review pages and notes are not that record.

Leave a usable return point

When review pauses or the human switches tasks, preserve a concise note in the conversation, or beside a temporary review artifact when a portable handoff is useful. Include the source revision and any dirty changes, retaining the relevant diff or snapshot when uncommitted edits are part of that source. Record the parts actually examined, the human's expressed decisions or questions, what remains open, and the next useful action. Link the note and its source evidence so another session can pick it up. Keep observed navigation separate from expressed understanding and acceptance; if none is known, say so.

On return, compare the current work with the recorded source before reusing earlier observations. Point out what needs another look and why, preserve unaffected discussion, and resume at the open question. If the earlier source cannot be recovered, say which comparison is unavailable rather than claiming the review is current. Before handing off any review, provide a next starting point without pretending the human has already examined it.

Deliver a review the human can use

Keep simple explanations in conversation. When comparison, navigation, or continued review benefits from HTML, adapt assets/review.html outside the repository. It provides context, a map of behaviors, before/after sections, nearby evidence, and an optional return note. Translate labels, replace placeholders, and remove unused sections; the template is a starting point, not a compulsory screen for every change.

For HTML, exercise navigation, comparisons, disclosures, and a narrow viewport in a browser. Fix incomplete content or broken controls before calling the surface ready. If browser verification is unavailable, state that limitation. Run the finished HTML using a method supported by the current harness and share an address the user can open, plus a preview when supported. Keep the server available for review. Browser verification establishes that the review surface works, not that the underlying product is correct or approved.

A successful handoff supports explaining the behavior, inspecting an assumption the AI did not flag, and resuming after a switch. Do not claim improved human understanding merely because the page renders or the human grants approval.