Generate structured academic CVs from free-form Chinese/English text and export to Word (.docx). Use this skill when you are asked to organize, generate, or optimize an academic CV (e.g., publications/projects/awards) into a consistent, formatted document with uniform-colored section headers and optional bilingual output.
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Use academic-cv-generator to turn [free-form CV draft] into a polished academic CV Word document named [Name]-Academic-CV.docx, with a normalized Markdown outline, consistent section styling, and publication citations preserved exactly as supplied.
Required inputs
[free-form CV draft]
[Name]
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SKILL.md
name: academic-cv-generatordescription: Generate structured academic CVs from free-form Chinese/English text and export to Word (.docx). Use this skill when you are asked to organize, generate, or optimize an academic CV (e.g., publications/projects/awards) into a consistent, formatted document with uniform-colored section headers and optional bilingual output.license: MITauthor: AIPOCH
python scripts/render_cv.py \
--input sample_input_standard.txt \
--output output \
--lang en \
--header-color random
Supported options:
--lang zh|en
--header-color purple|cyan|green|red|random
4) Expected output
Output file: Name-Academic-CV.docx (saved under the --output directory)
Reference sample: sample_output.docx
Implementation Details
1) End-to-end workflow
Parse the input and classify content into:
Personal Information
Educational Background
Project Experience
Publications
Skills
Awards (optional)
Convert the classified result into the required Markdown layout (mandatory intermediate step).
If Awards is empty, omit the section from both Markdown and DOCX.
If Skills is empty, infer skills from project experience and output a minimal skills list.
Render the CV into Word using the same visual structure as output/ZhangWei-Academic-CV.docx.
Ensure section titles are single-line, bold, larger than body text, and share one uniform color.
Save as Name-Academic-CV.docx.
2) Classification rules
Personal Information: name, title, organization, email, phone.
Educational Background: degree (PhD/Master/Bachelor), major, school, year; sort from highest to lowest degree.
Project Experience: project name + time range + responsibilities/description; sort by time (most recent first).
Publications: keep original citation strings exactly as provided (no reformatting).
Skills: use explicit skills if present; otherwise infer from project content.
3) Required Markdown output format (intermediate representation)
The classified content must be normalized into the following Markdown outline:
Personal Information
Name|Title|Organization|Email|Phone
Educational Background
Degree, Major, School, Year
Degree, Major, School, Year
Project Experience
Project Name (Project Period): Key responsibilities or work content
Project Name (Project Period): Key responsibilities or work content
Publications
Original citation entries (keep input format)
Original citation entries (keep input format)
Awards
Year, Award
Year, Award
Skills
Programming Languages: ...
Technical Fields: ...
Tools & Technologies: ...
Rules:
If Awards is empty, omit the entire section.
If Skills is empty, infer and output a minimal skills list (e.g., Technical Fields: Blockchain / Machine Learning / Systems Development).
4) Word rendering rules
Do not print the “Personal Information” section title in the DOCX.
Print the name as the first line in bold.
Print the contact line as: Title | Organization | Email | Phone.