Data Classification

用于数据分类、数据分级、数据分类分级任务。用户要求对单一数据字段名、字段列表、数据库表 SQL/DDL 文件进行数据分类、数据分级或数据分类分级时使用；支持普通数据分类分级、GB/T 43697-2024 通用数据分类分级、金融数据分类分级、JR/T 0197-2020 金融数据安全级别，以及“通用数据标签 + 金融数据标签”的金融双标签体系。

Purpose

Classify user-provided field names or SQL DDL into:

1. 普通/通用数据分类分级: GB/T 43697-2024 style category + level (一般数据 / 重要数据 / 核心数据).
2. 金融数据双标签体系: general label + JR/T 0197-2020 financial label (一级/二级/三级/四级子类 + 最低安全级别1-5).

This skill produces classification suggestions, not final regulatory determinations. Mark uncertain items for business-owner review.

Quick workflow

1. Identify input type:
   • Single field name: classify directly.
   • SQL/DDL file: extract table names, column names, types, and comments.
2. Run the helper when useful:

   bash

   python3 skills/data-classification/scripts/classify_data.py --field "customer_id" --mode finance
   python3 skills/data-classification/scripts/classify_data.py --sql path/to/schema.sql --mode finance --format markdown
   

3. Review financial rows against JR/T 0197-2020 Appendix A before falling back to heuristics:
   • references/jrt0197-appendix-a-full.csv is the machine-readable full Appendix A table.
   • references/jrt0197-appendix-a-compact.md is the human-readable compact Appendix A table.
   • references/financial-dual-label.md contains dual-label workflow and fallback heuristics.
   • references/general-rules.md contains GB/T 43697-2024 logic.
4. Return a field-level result that covers every input field. Do not replace the full field list with a summary.
5. Choose output delivery by field count internally, but do not explain this threshold policy to the user:
   • ≤20 fields: output the complete field-level table inline in chat; do not create/attach files unless the user explicitly asks for an export/file.
   • >20 fields: save the complete field-level result as a CSV file, attach it with a MEDIA: line using the CSV file's absolute filesystem path, show the first 20 classified fields inline, and include a coverage statement in the message. Do not inline rows after the first 20. Never provide only a plain local path as the download method. Do not write the coverage statement into the CSV file itself.
6. Run a coverage check before finalizing: compare parsed/input field count with classified output row count. If any field is missing, fix the output or explicitly mark the field as [blocked: 未解析/缺少字段信息].
7. Ask for business context only if the field name/comment is too ambiguous.

Output requirements

For a single field, include:

• 字段名
• 通用分类：行业领域、描述对象/数据主体、内容类别
• 通用分级：一般/重要/核心 + 理由
• 置信度与需确认点
• 金融标签（仅金融场景输出）：推荐的一级/二级/三级/四级子类 + 最低安全级别
• 候选金融标签（仅金融场景输出）：当字段可落入多个 JR/T 分类时，一并列出候选项并说明推荐依据
• 双标签结果（仅金融场景输出）：通用标签 + 金融标签

For a SQL file/table, classify all parsed columns from all tables. Choose the delivery format internally and do not tell the user the threshold/routing rule.

• ≤20 fields: the complete field-level table inline. Do not create/attach files unless the user explicitly asks for an export/file.
• >20 fields: create a complete CSV result file. Return a concise completion note, attach the CSV with MEDIA:<absolute-csv-path> on its own line so the UI can render a downloadable link, include the first 20 field-level rows inline, and include the coverage statement in the message. Do not inline rows after the first 20. Do not rely on a bare local path as the user's download link. Do not include the coverage statement as a row in the CSV file.

Do not provide only a subset such as “core fields”, “sample rows”, or “summary table” unless the user explicitly asks for a summary.

The following output columns are mandatory for every field and must be non-empty in all scenarios:

1. 字段名
2. 通用分类
3. 通用分级
4. 置信度

For financial data/scenarios only, also include these mandatory non-empty columns:

5. 推荐金融分类标签
6. JR/T最低级别
7. 候选金融标签

For financial fields, match against references/jrt0197-appendix-a-full.csv or references/jrt0197-appendix-a-compact.md first. Use financial-dual-label.md heuristics only when Appendix A has no clear match or when field/table context creates multiple reasonable candidates.

For non-financial data, do not output 推荐金融分类标签、JR/T最低级别、候选金融标签.

Recommended non-financial table shape:

表名	字段名	类型/注释	通用分类	通用分级	置信度	依据/备注

Recommended financial table shape:

表名	字段名	类型/注释	通用分类	通用分级	推荐金融分类标签	JR/T最低级别	候选金融标签	双标签	置信度	依据/备注

After the table, include a coverage line:

覆盖校验：输入/解析字段 N 个，已分类 N 个，遗漏 0 个。

If output is saved to a file, still include the coverage line in the message and an attachment. For CSV outputs, include MEDIA:<absolute-csv-path> on its own line so the user can click/download directly; use the absolute path returned by the file-writing step, not a relative workspace path. File output is allowed for >20 fields as CSV, or whenever the user explicitly requests a file/export. Do not write the coverage line into the CSV file. Do not explain that files are chosen because of the field-count threshold unless the user asks why.

Classification principles

• Coverage is mandatory: every user-provided field/parsed SQL column must receive a classification row.
• Use 就高从严: if multiple rules match, choose the stricter level as the recommendation, list reasonable candidate labels, and explain why.
• Treat field names alone as weak evidence; comments and table names improve confidence.
• Do not infer 核心数据 from a field name alone unless the field clearly describes large-scale national/security/critical-infrastructure data. Usually mark as 需人工确认.
• 重要数据 usually requires scale, coverage, precision, or public/national impact context. For isolated personal or organization fields, default to 一般数据 unless a law/industry rule says otherwise.
• For financial data, Appendix A match takes precedence over broad keyword heuristics. JR/T 0197 level is the minimum security level; business context may raise it.
• For personal financial information, authentication credentials, biometric identifiers, account/payment/transaction data, and credit data should be handled conservatively.

Helper script notes

classify_data.py is deterministic and heuristic. It is designed for first-pass tagging:

• Inputs: --field, --fields, or --sql.
• Modes: general, finance.
• Formats: markdown, json, csv.
• It parses common CREATE TABLE DDL and column comments.
• Low confidence means the assistant should inspect context and possibly ask one focused follow-up.

References

• references/general-rules.md: compact GB/T 43697-2024 classification/grading rules.
• references/financial-dual-label.md: financial dual-label workflow and fallback heuristics.
• references/jrt0197-appendix-a-compact.md: compact human-readable JR/T 0197-2020 Appendix A typical data grading table.
• references/jrt0197-appendix-a-full.csv: full machine-readable JR/T 0197-2020 Appendix A typical data grading table.