From engineering
Review code changes for security, performance, and correctness. Trigger with a PR URL or diff, "review this before I merge", "is this code safe?", or when checking a change for N+1 queries, injection risks, missing edge cases, or error handling gaps.
How this skill is triggered — by the user, by Claude, or both
Slash command
/engineering:code-review <PR URL, diff, or file path><PR URL, diff, or file path>The summary Claude sees in its skill listing — used to decide when to auto-load this skill
> If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../CONNECTORS.md).
If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
Review code changes with a structured lens on security, performance, correctness, and maintainability.
Lark-native execution (depth core: LARK-PATTERNS, LARK-RECIPES, LARK-FUSION). Source control stays GitHub (
gh/external MCP) — pull the diff there. The collaboration layer is Lark: check the change against team standards by searching Wiki/Docs (lark_doc_search→lark_doc_fetch, projected withjq, P3); post the verdict + critical findings as an interactive card (lark_im_card_send, P4) with a per-finding side button (e.g. File ticket / Ack) rather than a wall of text; turn must-fix findings into tracked work vialark_task_create(resolve the author vialark_contact_searchfirst — P1;dry_run— P2), or log them to a code-quality Lark Base (lark_base_record_upsert, P5). For rigorous review methodology and the code-reviewer subagent, delegate to the code-review skill.
/code-review <PR URL or file path>
Review the provided code changes: @$1
If no specific file or URL is provided, ask what to review.
┌─────────────────────────────────────────────────────────────────┐
│ CODE REVIEW │
├─────────────────────────────────────────────────────────────────┤
│ STANDALONE (always works) │
│ ✓ Paste a diff, PR URL, or point to files │
│ ✓ Security audit (OWASP top 10, injection, auth) │
│ ✓ Performance review (N+1, memory leaks, complexity) │
│ ✓ Correctness (edge cases, error handling, race conditions) │
│ ✓ Style (naming, structure, readability) │
│ ✓ Actionable suggestions with code examples │
├─────────────────────────────────────────────────────────────────┤
│ SUPERCHARGED (when you connect your tools) │
│ + Source control: Pull PR diff automatically │
│ + Project tracker: Link findings to tickets │
│ + Knowledge base: Check against team coding standards │
└─────────────────────────────────────────────────────────────────┘
## Code Review: [PR title or file]
### Summary
[1-2 sentence overview of the changes and overall quality]
### Critical Issues
| # | File | Line | Issue | Severity |
|---|------|------|-------|----------|
| 1 | [file] | [line] | [description] | 🔴 Critical |
### Suggestions
| # | File | Line | Suggestion | Category |
|---|------|------|------------|----------|
| 1 | [file] | [line] | [description] | Performance |
### What Looks Good
- [Positive observations]
### Verdict
[Approve / Request Changes / Needs Discussion]
Source control = GitHub (external, keep as-is):
gh pr diff <url> or the GitHub MCP).gh pr checks).Project tracker = Lark Task + Base (lark):
lark_contact_search (P1) →
lark_task_create(assignee=open_id, dry_run: true) (P2) → confirm → commit.lark_base_record_upsert
(P5) so trends (recurring N+1s, missing tests) are queryable; scaffold via base-deploy.Knowledge base = Lark Wiki + Docs (lark):
lark_doc_search("coding standards"/"style guide")
→ lark_doc_fetch the hit with a jq projection (P3), and cite the specific rule in each finding.Deliver the verdict as an interactive card (P4) to the PR channel: header colored by verdict
(green Approve / red Request changes), one item row per critical issue with a side button, and an
actions footer (File all as tasks / Snooze). Validate the YAML spec with print_json: true,
then dry_run: true, then send (P2). Card grammar lives in the lark-im skill.
npx claudepluginhub larkcowork/lark-cowork-plugins --plugin engineeringGuides creation, editing, and verification of skills for AI coding agents using test-driven development with subagent scenarios. Use when authoring or debugging skills.