| name | career-cv |
| description | Generate ATS-optimized CVs in Markdown and PDF format. |
| compatibility | opencode |
| when_to_use | When the user wants to create or update their CV/resume. |
| model | high |
| user-invocable | false |
| hub-skill-ids | ["career-intelligence","generation"] |
| allowed-tools | ["Bash","Read","Write"] |
Skill: Career CV
Generate tailored CVs using career data from the knowledge base.
Prerequisites
Career data must be gathered first. Verify:
from fu7ur3pr00f.utils.data_loader import load_career_data_for_cv
data = load_career_data_for_cv()
If empty, tell the user to run /gather first.
CV Generation
- Load career data via
load_career_data_for_cv()
- Load profile via
load_profile()
- Generate CV content in Markdown using your LLM:
- Professional summary aligned with target role
- Experience (from LinkedIn/CV data)
- Skills (technical + soft)
- Education & certifications
- Projects & achievements
- Save markdown: use
fu7ur3pr00f.generators.save_cv_markdown(content, filename)
- Render PDF:
python scripts/generate/render_cv.py path/to/cv.md
ATS Optimization Rules
- Use standard section headers (Experience, Education, Skills)
- Include keywords from target job descriptions naturally
- No tables, columns, or images
- Clean, simple formatting
- Quantify achievements with numbers
Languages
- English: default
- Spanish:
--language es
Output
~/.fu7ur3pr00f/data/output/cv.md — markdown source
~/.fu7ur3pr00f/data/output/cv.pdf — ATS-optimized PDF
Dependencies
weasyprint for PDF rendering
libpango, libcairo system libs for PDF