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linkedin-content-validation

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English
🇨🇳

Translation

Chinese

LinkedIn Content Validation

LinkedIn 内容验证

Use this skill after any LinkedIn/profile-content drafting skill produces copy or visual specs.
在任意 LinkedIn/个人资料内容起草 skill 生成文案或视觉规格后使用此 skill。

Workflow

工作流程

  1. Save generated text under 
    content/
    as Markdown with YAML frontmatter.
  2. Save generated image specs under 
    content/
    or 
    assets/
    as YAML.
  3. Run the formatter.
  4. Run the validator.
  5. If validation fails, repair the draft using the exact machine-readable error.
  6. Repeat until validation passes.
  1. 将生成的文本以带 YAML frontmatter 的 Markdown 格式保存到 
    content/
    目录。
  2. 将生成的图片规格以 YAML 格式保存到 
    content/
    或 
    assets/
    目录。
  3. 运行格式化器。
  4. 运行验证器。
  5. 如果验证失败，使用确切的机器可读错误修复草稿。
  6. 重复直到验证通过。

Markdown Contract

Markdown 契约

Every LinkedIn Markdown file must use frontmatter:
markdown
---
platform: linkedin
section: about
---

Text body here.
Supported 
section
values:
  • profile_headline
  • about
  • experience_description
  • project_title
  • project_description
  • post
  • comment
  • article_title
  • article_body
每个 LinkedIn Markdown 文件必须使用 frontmatter：
markdown
---
platform: linkedin
section: about
---

Text body here.
支持的 
section
值：
  • profile_headline
  • about
  • experience_description
  • project_title
  • project_description
  • post
  • comment
  • article_title
  • article_body

Commands

命令

bash
python3 scripts/format_linkedin_text.py --write content
python3 scripts/format_linkedin_text.py --check content
python3 scripts/validate_linkedin_assets.py content
python3 scripts/validate_linkedin_assets.py content --config config/linkedin_limits.yaml
bash
python3 scripts/format_linkedin_text.py --write content
python3 scripts/format_linkedin_text.py --check content
python3 scripts/validate_linkedin_assets.py content
python3 scripts/validate_linkedin_assets.py content --config config/linkedin_limits.yaml

Repair Priorities

修复优先级

Preserve:
  1. quantified hook
  2. reader benefit
  3. proof metrics
  4. mechanism/framework
  5. CTA
Remove first:
  1. filler words
  2. repeated qualifiers
  3. duplicated concepts
  4. excessive internal details
  5. weak adjectives
  6. redundant setup
Do not remove blank lines just to fit a character limit. If 
count_mode
is 
non_newline_unicode_codepoints
, shorten actual words instead.
保留：
  1. 量化钩子
  2. 读者收益
  3. 证明指标
  4. 机制/框架
  5. CTA
优先删除：
  1. 填充词
  2. 重复限定词
  3. 重复概念
  4. 过度的内部细节
  5. 虚弱的形容词
  6. 冗余铺垫
不要为了满足字符限制而删除空行。如果 
count_mode
是 
non_newline_unicode_codepoints
，请缩短实际单词。