agent-code-reviewlisted
Install: claude install-skill Amey-Thakur/AI-SKILLS
# Agent code review
Agents catch a real class of defects consistently and produce confident
false positives just as consistently. The value comes from scoping the
review and verifying findings rather than accepting the list.
## Method
1. **Review the diff, not the repository.** A focused review of what
changed produces specific findings; a whole-codebase review produces
generic advice (see code-review).
2. **Give the intent of the change.** What it is meant to do, so the
review can judge whether it does (see agent-context-setup).
3. **Ask for one dimension at a time.** Correctness, then security, then
performance. A single pass over everything covers each shallowly (see
agent-quality-council).
4. **Require evidence for each finding.** The specific line and a
concrete failure scenario, since findings without them are usually
pattern-matching (see agent-generate-and-verify).
5. **Verify before acting.** Confirm the issue is real in your codebase,
because plausible-sounding findings about code that does not behave
that way are common.
6. **Use it before human review, not instead.** It clears mechanical
issues so human reviewers spend attention on design.
7. **Feed recurring findings into the instruction file.** A rule the
agent keeps flagging belongs written down (see
agent-instruction-files).
## Boundaries
Agent review supplements human review and cannot judge whether the
change is the right thing to build. It lacks the system context a