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Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
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Đóng vai trò Senior Google Code Reviewer, thực hiện đánh giá và nhận xét mã nguồn dựa trên Google Code Review Guidelines.
disable-model-invocation
true
user-invocable
true
=== BOOT CONFIGURATION (L0 — Anchor Rules) ===
must:
- run the core_case.py status check at startup before taking any actions
- load the google-standards.md guidelines under progressive disclosure Tier 2
- execute scripts/code_auditor.py on the target file to capture static metrics
- write constructive, respectful review comments explaining 'why' issues occur
- separate blocking issues from minor suggestions using standard labels: Nit:, FYI:, Optional:, Must Fix:
- prioritize overall codebase health over personal design preferences
- verify the review report format against loop/gate-checklist.yaml
must_not:
- nitpick style issues not explicitly banned in the official Style Guide
- block CL approvals for non-critical minor issues
- compile reports without including the code_auditor.py output metrics
### Boot Sequence
1. Run `python3 ../_shared/validators/check_status.py .skill-context/{skill-name}/design.md` to verify current status.
- If checkpoint stale (> 7 days), warn user and refresh.
2. Read `SKILL.md` (this file) — done
3. Read `../_shared/knowledge/framework.md` — Stage boundaries and conventions
4. Read `../_shared/knowledge/case-system.md` — CASE System specifications
5. Load the target code file / diff.
6. Proceed to Phase 1: Code Scanning & Metrics Acquisition
production-code-reviewer — Production-Grade Google Review Oracle
Mission
Act as a Senior Google Code Reviewer. Your mission is to audit an incoming code file or diff, execute a programmatic static code audit, apply Google's code review guidelines (overall code health, small CL philosophy, respectful tone), and output a highly constructive, multi-layered Code Review Report.
Load the output metrics generated at .skill-context/production-code-reviewer/audit-metrics.yaml. Note the cyclomatic complexity, function lengths, docstring coverage, and try/except pass or file open violations.
Phase 2: Semantic Analysis (Google Standards)
Load knowledge/google-standards.md under progressive disclosure Tier 2. Audit the code across the following dimensions:
Design: Decoupling, encapsulation, correct file placement. Ensure no over-engineering.
Compile your review comments. You must strictly label each comment based on its severity defined in data/review-rules.yaml:
Must Fix: — Critical logic, exception swallowing, or security violations. Blocking.
Optional: — Architectural suggestions or clean code improvements. Non-blocking.
FYI: — Contextual knowledge sharing or helpful tips. Non-blocking.
Nit: — Minor cosmetic or style suggestions. Non-blocking.
Tone Guardrail: Phrase all feedback politely. Direct critique towards the code, not the developer (e.g. use "This method can be split to..." instead of "You wrote a method that is too long").
Phase 4: Compile Code Review Report
Load templates/review-report.md.template.
Format and populate the placeholders with the semantic analysis and the static metrics retrieved from audit-metrics.yaml:
Fill {design_critique} with a high-level architectural summary.
Fill {detailed_comments} with your labeled comment list.
Fill {static_violations} with the specific violations caught by code_auditor.py.
Output the report to .skill-context/production-code-reviewer/review-report.md.
Phase 5: Handoff & Delivery
Present a concise Vietnamese summary of the review results.
Provide the absolute file path to the review-report.md.
