cv-analysis

name	CV Analysis
description	This skill should be used when the user asks to "analyze my CV", "extract CV profile", "review my resume", "parse my CV", or runs /ra:cv-analyze. It reads a CV/resume and produces a structured capability profile stored in .local.md for other skills to reference.

CV File Discovery

To locate the CV file, check in this order:

1. Read ${CLAUDE_PLUGIN_ROOT}/.local.md and check cv_file_path in frontmatter
2. Search current working directory for files matching *CV*, *cv*, *resume*, *Resume* (use Glob)
3. If not found, prompt: "未找到 CV 文件。请提供 CV 文件路径，或将 CV 放在当前工作目录下。其他功能需要先完成 CV 分析。"

CV Content Extraction

After locating the CV file, extract content using the appropriate method based on file type:

For PDF files, use a three-level fallback:

1. Read tool (primary): Use the Read tool directly to read the PDF. This works for most PDFs natively.
2. PyPDF2 (fallback): If Read fails or returns incomplete content, try extracting text via Python:

   python3 -c "import PyPDF2; reader = PyPDF2.PdfReader(open('FILE_PATH','rb')); print('\n'.join([p.extract_text() or '' for p in reader.pages]))"
   

   If PyPDF2 is not installed, skip to next fallback.
3. textutil (last resort): On macOS, use the built-in textutil:

   textutil -convert txt -stdout "FILE_PATH"
   

For DOCX files:

• Use textutil (macOS built-in): textutil -convert txt -stdout "FILE_PATH"
• Or use python3 with python-docx if textutil output is insufficient

CV Analysis Process

After extracting the CV content: 2. Extract and categorize:

• Education: degrees, institutions, dates, grades (note: do NOT judge grades, just record them)
• Technical Skills: programming languages, frameworks, tools
• Project Experience: name, description, technologies used, achievements — flag which projects are most relevant to research applications
• Work Experience: job titles, companies, responsibilities
• Internships: similar to work experience
• Awards & Honors: competitions, scholarships, hackathons
• Teaching Experience: courses taught, role (TA/instructor)
• Publications: papers, venues (if any)

3. Evaluate for research application relevance:
   • Strengths: experiences, skills, and achievements that strengthen a research degree application
   • Areas for Improvement: gaps that could be addressed (no publications, low grades, etc.) — note these objectively without judgment
   • Packaging Opportunities: experiences that could be framed more strongly for applications

Output

Write the structured profile to ${CLAUDE_PLUGIN_ROOT}/.local.md:

• Set cv_profile_analyzed: true in frontmatter
• Set cv_file_path in frontmatter if not already set
• Populate the ## CV Profile markdown section with all extracted information
• Use clear headers and bullet points

After writing, present a summary to the user highlighting:

• Top 3 strengths for research applications
• Key experiences that connect to potential research directions
• Any notable packaging opportunities

Important Notes

• Never judge or comment negatively on grades — they are factual data
• Focus on identifying connections between experience and research potential
• The output must be structured enough for other skills (cold-email, professor-match, etc.) to reference programmatically
• Support both English and Chinese CVs