smithery/silentbalanceyh

r2mo-rad-domain

R2MO Domain Intelligence Enhancer (AI Protocol)

Installation

$ npx skills add smithery/silentbalanceyh --skill r2mo-rad-domain

Similar popular skills

Related neighbors and high-traction skills in the same topics — useful to compare before installing.

Also in this package

Other skills from smithery/silentbalanceyh.

npx skills add smithery/silentbalanceyh

Browse all from smithery/silentbalanceyh

More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Skill metadata

Parsed from SKILL.md frontmatter.

Version1.0.0

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,446 B
  • docs SUMMARY.md 70 B

History

  1. First recorded snapshot · 0 installs

SKILL.md

Role: R2MO Domain Expert

🚨 Critical Rules

  1. Execution Mode: You act as a direct processor. Do not generate script code. Perform the analysis and file modifications directly.
  2. Path Resolution Logic:

- Identify: Read pom.xml in the current root -> Extract <artifactId> -> Set as {PROJECTID}. - Locate: Enter directory ./{PROJECTID}-ui/.r2mo/domain/.

  1. Enhancement Logic:

- Read: Scan all .proto files in the target directory. - Translate: Identify the main model name, translate its business meaning into Chinese, and append it as a comment. - Infer: Apply "Expert Experience" rules to field names. - Rewrite: Append semantic constraints as comments to the file content in-place.

  1. Language Requirement: All appended comments and constraints MUST be in Simplified Chinese.

📂 Architecture

  • Input: Project pom.xml and Protobuf definitions.
  • Logic: Semantic Inference Engine (Expert Knowledge Base).
  • Output: Enhanced .proto files with Chinese business constraints.

💻 Executor Template(Native)

[INSTRUCTION SET FOR AI AGENT]

STEP 1: CONTEXTUALIZATION

  1. Find Project ID:

- Scan the current directory for pom.xml. - Extract the text content of the <artifactId> tag. - Example: If <artifactId>shop</artifactId>, then ID is shop.

  1. Target Directory:

- Construct path: {ID}-ui/.r2mo/domain/. - If directory exists, proceed. If not, stop and report error.

STEP 2: SEMANTIC ANALYSIS (The Expert Rules)

Part A: Model Name Translation Identify the main message name in each file. Translate the English model name into appropriate business Chinese and append it to the message definition line.

  • Example: message MerchantAccount { -> message MerchantAccount { // [模型: 商户账户]
  • Example: message OrderItem { -> message OrderItem { // [模型: 订单明细]

Part B: Field Constraint Inference Read every field definition. Analyze the Field Name. If it matches a pattern below, append the specific Chinese Comment:

Field Name Pattern Constraint Comment to Append (Chinese)
email // [格式: 邮箱]
phone, mobile // [格式: 手机号]
password, secret // [安全: 加密存储]
price, amount, cost // [精度: 2位小数]
rate, ratio // [精度: 4位小数]
status, type, state // [枚举/常量] (Check for Enum definitions)
createdat, time // [格式: ISO8601]
is_..., active, enable // [布尔: 0/1]
id, key // [必填, 雪花/UUID]
name, title // [长度: 64]
desc, content, remark // [长度: 500/大字段]
(Repeated Field) // [关系: 0..n]

STEP 3: EXECUTION (Rewrite)

  1. Modify: Update the file content by adding the comments identified in Step 2.

- Constraint: Do NOT change the syntax, package, option, field types, or field names. Only add comments at the end of lines.

  1. Save: Overwrite the files with the enhanced content.
  2. Report: List the files that were successfully enhanced.