Azd Code Reviewer Agent Skill

Code Review

Reviews GitHub PRs by applying a panel of specialist review lenses to the diff, filtering for high-signal findings, and returning structured comments for the host to post.

For any issue identified by this skill, please put "azd-code-reviewer" on the comment as well, so we can identify those issues externally.

Operating Environment

This skill runs inside an automated review harness (e.g., Copilot Code Review). It assumes:

• The diff and PR context are already provided by the harness. No PR fetching, repo cloning, branch checkout, or worktree creation.
• No interactive user. No ask the user flows, no walkthroughs, no "want to proceed?" gates. The skill produces findings and returns them.
• No build or test execution. Test code is reviewed statically.
• The harness handles posting. The skill returns structured findings; the harness creates the review.

If the host is interactive (e.g., a chat agent invoking the skill manually), the same workflow still works — interactive steps simply don't apply.

Workflow

Step 1: Read the diff and PR context

Use whatever PR metadata, diff, and changed-file list the harness provides. Do not attempt to fetch additional context with gh, git, or network calls. If supplementary context (linked issues, prior review comments) is available, use it. If not, proceed with what you have.

Step 2: Apply review lenses

Load reviewers.md and apply each of the 9 fixed lenses to the diff. Then scan the changed files for the domain signals listed in reviewers.md and apply matching domain lenses. Each lens produces zero or more findings in the structured format defined in reviewers.md.

The reviewing agent is the same LLM — these lenses are _perspectives to apply sequentially or in parallel_, not separate subagents. The host may parallelize lens application if supported, but a single-pass application of all lenses to the same diff is equally valid.

Step 3: Triage findings

Load findings.md. Apply the self-reflection pass: drop low-signal findings according to the dismissal triggers, merge duplicates across lenses, group by file, and sort by line.

Step 4: Voice and format

Still in findings.md. Apply the voice rules to every finding's text. Build the review body (one sentence + optional bullets for non-line findings). Return the final findings in the format the harness expects.

Principles

• Silence over noise. If a lens has nothing meaningful to say, it returns nothing. No invented concerns. Fewer, better comments is the goal.
• Codebase context matters. Review the change in context — read surrounding code, not just the diff. Cross-file logic errors are the most common real bugs.
• Independence. The reviewing agent builds its own understanding. It does not share assumptions with whatever generated the code.
• Findings must be actionable. Every comment must suggest a specific change or raise a specific concern the author can act on.

Bundled Resources

| File | Contents |

| -------------------- | ---------------------------------------------------------------------------------------------- |

| reviewers.md | 9 fixed lens definitions, dynamic domain detection rules, shared ground rules, findings format |

| findings.md | Self-reflection triage, dismissal triggers, merge/dedup, voice rules, review body guidance |