Borrowing it
Nothing to install: this file belongs to u9401066/asset-aware-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/u9401066/asset-aware-mcp/main/.claude/skills/code-reviewer/SKILL.mdgit clone --depth 1 https://github.com/u9401066/asset-aware-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/u9401066/asset-aware-mcp/code-reviewer)<a href="https://agentmods.dev/skills/u9401066/asset-aware-mcp/code-reviewer"><img src="https://agentmods.dev/badge/skills/u9401066/asset-aware-mcp/code-reviewer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/u9401066/asset-aware-mcp/code-reviewer"><img src="https://agentmods.dev/badge/skills/u9401066/asset-aware-mcp/code-reviewer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00046 | $0.00388 |
| Opus 5 | $0.00023 | $0.00194 |
| Sonnet 5 | $0.00009 | $0.00078 |
| Haiku 4.5 | $0.00005 | $0.00039 |
Grade A, and why
code-reviewer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to code-reviewer — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
程式碼審查技能
描述
對程式碼進行全面審查，檢查品質、安全性和最佳實踐。
觸發條件
- 「review 這段程式碼」
- 「檢查程式碼」
- 「code review」
審查項目
1. 程式碼品質
- 命名是否清晰
- 函數長度是否合理（建議 < 50 行）
- 是否遵循 DRY 原則
- 複雜度是否過高
2. 安全性
- SQL 注入風險
- XSS 漏洞
- 敏感資料暴露
- 權限檢查
3. 效能
- N+1 查詢問題
- 不必要的迴圈
- 記憶體洩漏風險
4. 可維護性
- 註解是否充足
- 錯誤處理是否完整
- 測試覆蓋率
輸出格式
## 審查結果
### ✅ 優點
- ...
### ⚠️ 建議改進
- [嚴重程度] 問題描述
- 位置：第 X 行
- 建議：...
### 📊 評分
- 品質：X/10
- 安全：X/10
- 效能：X/10
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 58 lines · 46 tokens per session scan A f25036098dd5
code-reviewer is a skill published in the GitHub repository u9401066/asset-aware-mcp (0 stars, last pushed 20d ago), licensed Apache-2.0. It adds 46 tokens to every session and 388 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to code-reviewer, differing in 2 lines, and is treated as a copy.
Other skills, from other repositories
understand-diff
Use when you need to analyze git diffs or pull requests to understand what changed, affected components, and risks.
code-knowledge-graph
Codebase'i knowledge graph olarak analiz et. Dependency, call graph, hotspot analizi.
cortex-memory
Persistent cognitive memory for AI agents — query, record, review, and consolidate knowledge across sessions with spreading activation, FSRS scheduling, and NLI contradiction detection.
raytsystem-run-review
Independently review an raytsystem run, diff, contract, test result, or milestone checkpoint and return structured findings. Use for REVIEW, architecture/contracts/data-integrity/test critique, gate verification, or pre-promotion review; remain read-only and separate from the writer context.
raytsystem-security-review
Audit raytsystem changes for prompt injection, provenance bypass, path/symlink/hardlink escape, secret leakage, stale fencing, partial promotion, unsafe parsing, and unapproved side effects. Use for SECURITY REVIEW, adversarial testing, recovery review, or approval-boundary validation; remain independent and read-only.
accurate-code
Use when implementing or modifying code with Kontext Brain so each behavior-bearing logic unit is grounded in current provenance-backed decisions, domain terms, invariants, and Evidence.