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-refactor/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-refactor)<a href="https://agentmods.dev/skills/u9401066/asset-aware-mcp/code-refactor"><img src="https://agentmods.dev/badge/skills/u9401066/asset-aware-mcp/code-refactor/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-refactor"><img src="https://agentmods.dev/badge/skills/u9401066/asset-aware-mcp/code-refactor.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 | $0.00050 | $0.02401 |
| Opus 5.5 | $0.00020 | $0.00960 |
| Sonnet 5.5 | $0.00010 | $0.00480 |
| Haiku 4.5 | $0.00005 | $0.00240 |
Grade A, and why
code-refactor 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 1mo 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-refactor — 10 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.
How it starts
The opening of the file, as written. The whole thing — 352 lines — stays where its author put it; the contents beside it link to each section on GitHub.
程式碼重構技能
描述
主動偵測並執行程式碼重構,維持 DDD 架構和程式碼品質。
觸發條件
- 「重構這段程式碼」、「refactor」
- 「這個檔案太長了」
- 「模組化」、「拆分」
- 主動觸發:偵測到程式碼超過閾值時
核心原則
📜 依據憲法第 7.3 條「主動重構原則」
重構不是改天換地,而是持續的小步快跑
每次提交都應該比上次更乾淨
閾值設定
📏 長度閾值
| 類型 | 警告 | 強制重構 |
|---|---|---|
| 檔案 | > 200 行 | > 400 行 |
| 類別 | > 150 行 | > 300 行 |
| 函數 | > 30 行 | > 50 行 |
| 目錄檔案數 | > 10 個 | > 15 個 |
🔄 複雜度閾值
| 指標 | 警告 | 強制重構 |
|---|---|---|
| 圈複雜度 | > 10 | > 15 |
| 巢狀深度 | > 3 層 | > 4 層 |
| 參數數量 | > 4 個 | > 6 個 |
| 依賴數量 | > 5 個 | > 8 個 |
重構模式庫
1️⃣ Extract Method(提取方法)
觸發條件:函數過長、重複邏輯
# Before
def process_order(order):
# 驗證訂單 (10 行)
if not order.items:
raise ValueError("Empty order")
if order.total < 0:
raise ValueError("Invalid total")
# ... 更多驗證
# 計算價格 (15 行)
subtotal = sum(item.price * item.qty for item in order.items)
tax = subtotal * 0.05
total = subtotal + tax
# ... 更多計算
# 儲存訂單 (10 行)
# ...
# After
def process_order(order):
self._validate_order(order)
total = self._calculate_total(order)
self._save_order(order, total)
def _validate_order(self, order):
"""驗證訂單有效性"""
if not order.items:
raise ValueError("Empty order")
# ...
def _calculate_total(self, order) -> Decimal:
"""計算訂單總金額(含稅)"""
subtotal = sum(item.price * item.qty for item in order.items)
return subtotal * Decimal("1.05")
2️⃣ Extract Class(提取類別)
觸發條件:類別職責過多、超過 150 行
# Before: User 類別包含太多職責
class User:
def __init__(self, name, email, ...):
self.name = name
self.email = email
self.address_line1 = ...
self.address_line2 = ...
self.city = ...
self.postal_code = ...
def validate_email(self): ...
def format_address(self): ...
def calculate_shipping(self): ...
# After: 提取 Address 值物件
@dataclass(frozen=True)
class Address:
"""地址值物件"""
line1: str
line2: str | None
city: str
postal_code: str
def format(self) -> str:
return f"{self.line1}\n{self.city} {self.postal_code}"
class User:
def __init__(self, name: str, email: Email, address: Address):
self.name = name
self.email = email
self.address = address
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.
- 1mo ago First seen · 352 lines · 50 tokens per session scan A fe7f24f62fbc
code-refactor is a skill published in the GitHub repository u9401066/asset-aware-mcp (0 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 50 tokens to every session and 2,401 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to code-refactor, differing in 10 lines, and is treated as a copy.
Other skills, from other repositories
explain-paper
Use EdgeQuake MCP tools as evidence — never invent document lists.
redlines
Use when you need to generate tracked changes in Word documents from diff output, apply redlines from AI agents to DOCX files, or convert text diffs into native Word revisions.
office-word-diff
Use when you need to apply word-level tracked changes to Microsoft Word documents programmatically, preserve formatting through diffs, or integrate with Office.js for document transformation.
superdoc-redlines
Use when you need to apply tracked changes and comments to DOCX files programmatically, merge multi-agent edits with conflict resolution, or produce native Word revisions from AI agent output.
deckprobe
Inspect PDF, Microsoft Office (docx/xlsx/pptx and legacy doc/xls/ppt), and Apple iWork (key/numbers/pages) files without opening or rendering them. Use for page and slide and sheet counts, title/author/created metadata, encryption and macro and digital-signature and JavaScript risk signals, sheet and table structure…
corpus-knowledge-engineering
Turn document corpora (books, merged TXT, PDF/EPUB, OCR text) into structured agent knowledge: normalize → deterministic source segmentation → chapter maps with provenance → book skills → evaluation gates. Use when the user wants to convert books/materials into AI skills, build a knowledge base from a corpus, evaluate…