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cv:         # Your content: name, contact info, and all sections
design:     # Visual styling: theme, colors, fonts, margins, spacing, layouts
locale:     # Language: month names, phrases, translations
settings:   # Behavior: output paths, bold keywords, current date

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When using separate files, each file contains only its section (e.g., `design.yaml` has `design:` as the top-level key). CLI-loaded files override values in the main YAML file.

The YAML maps directly to Pydantic models. The complete type-safe schema is provided below so you can understand every field, its type, and its default value.

class RenderCVModel(BaseModelWithoutExtraKeys):
    cv: Cv = pydantic.Field(default_factory=Cv, title='CV', description='The content of the CV.')
    design: Design = pydantic.Field(default_factory=ClassicTheme, title='Design')
    locale: Locale = pydantic.Field(default_factory=EnglishLocale, title='Locale Catalog')
    settings: Settings = pydantic.Field(default_factory=Settings, title='RenderCV Settings', description='The settings of the RenderCV.')

class Cv(BaseModelWithoutExtraKeys):
    name: str | None = pydantic.Field(default=None, examples=['John Doe', 'Jane Smith'])
    headline: str | None = pydantic.Field(default=None, examples=['Software Engineer', 'Data Scientist', 'Product Manager'])
    location: str | None = pydantic.Field(default=None, examples=['New York, NY', 'London, UK', 'Istanbul, Türkiye'])
    email: pydantic.EmailStr | list[pydantic.EmailStr] | None = pydantic.Field(default=None, examples=['john.doe@example.com', ['john.doe.1@example.com', 'john.doe.2@example.com']])
    photo: ExistingPathRelativeToInput | pydantic.HttpUrl | None = pydantic.Field(default=None, union_mode='left_to_right', examples=['photo.jpg', 'images/profile.png', 'https://example.com/photo.jpg'])
    phone: pydantic_phone_numbers.PhoneNumber | list[pydantic_phone_numbers.PhoneNumber] | None = pydantic.Field(default=None, examples=['+1-234-567-8900', ['+1-234-567-8900', '+44 20 1234 5678']])
    website: pydantic.HttpUrl | list[pydantic.HttpUrl] | None = pydantic.Field(default=None, examples=['https://johndoe.com', ['https://johndoe.com', 'https://www.janesmith.dev']])
    social_networks: list[SocialNetwork] | None = pydantic.Field(default=None)
    custom_connections: list[CustomConnection] | None = pydantic.Field(default=None, examples=[[{'placeholder': 'Book a call', 'url': 'https://cal.com/johndoe', 'fontawesome_icon': 'calendar-days'}]])
    sections: dict[str, Section] | None = pydantic.Field(default=None, examples=[{'Experience': '...', 'Education': '...', 'Projects': '...', 'Skills': '...'}])

type SocialNetworkName = Literal['LinkedIn', 'GitHub', 'GitLab', 'IMDB', 'Instagram', 'ORCID', 'Mastodon', 'StackOverflow', 'ResearchGate', 'YouTube', 'Google Scholar', 'Telegram', 'WhatsApp', 'Leetcode', 'X', 'Bluesky', 'Reddit']

available_social_networks = get_args(SocialNetworkName.__value__)

class SocialNetwork(BaseModelWithoutExtraKeys):
    network: SocialNetworkName = pydantic.Field()
    username: str = pydantic.Field(examples=['john_doe', '@johndoe@mastodon.social', '12345/john-doe'])

class CustomConnection(BaseModelWithoutExtraKeys):
    fontawesome_icon: str
    placeholder: str
    url: pydantic.HttpUrl | None

cv:
  sections:
    experience:          # list of ExperienceEntry (detected by company + position)
      - company: Google
        position: Engineer
        start_date: 2020-01
        highlights:
          - Did something impactful
    skills:              # list of OneLineEntry (detected by label + details)
      - label: Languages
        details: Python, C++
    about_me:            # list of TextEntry (plain strings)
      - This is a free-form paragraph about me.

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Rule: if a string value contains `:`, it MUST be quoted. When in doubt, quote it.

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If the user provides a local number without country code, ask which country, or omit the phone field.

highlights:
  - Main bullet point
    - Sub-bullet 1
    - Sub-bullet 2

rendercv render CV.yaml --cv.name "Jane Doe" --design.theme "moderncv"
rendercv render CV.yaml --cv.sections.education.0.institution "MIT"

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cv:
  name: John Doe
  headline:
  location: San Francisco, CA
  email: john.doe@email.com
  photo:
  phone:
  website: https://rendercv.com/
  social_networks:
  - network: LinkedIn
    username: rendercv
  - network: GitHub
    username: rendercv
  custom_connections:
  sections:
    Welcome to RenderCV:
    - RenderCV reads a CV written in a YAML file, and generates a PDF with 
      professional typography.
    - Each section title is arbitrary.
    education:
    - institution: Princeton University
      area: Computer Science
      degree: PhD
      date:
      start_date: 2018-09
      end_date: 2023-05
      location: Princeton, NJ
      summary:
      highlights:
      - 'Thesis: Efficient Neural Architecture Search for Resource-Constrained Deployment'
      - 'Advisor: Prof. Sanjeev Arora'
      - NSF Graduate Research Fellowship, Siebel Scholar (Class of 2022)
    - institution: Boğaziçi University
      area: Computer Engineering
      degree: BS
      date:
      start_date: 2014-09
      end_date: 2018-06
      location: Istanbul, Türkiye
      summary:
      highlights:
      - 'GPA: 3.97/4.00, Valedictorian'
      - Fulbright Scholarship recipient for Graduate Studies
    experience:
    - company: Nexus AI
      position: Co-Founder & CTO
      date:
      start_date: 2023-06
      end_date: present
      location: San Francisco, CA
      summary:
      highlights:
      - Built foundation model infrastructure serving 2M+ monthly API requests 
        with 99.97% uptime
      - Raised $18M Series A led by Sequoia Capital, with participation from 
        a16z and Founders Fund
      - Scaled engineering team from 3 to 28 across ML research, platform, and 
        applied AI divisions
      - Developed proprietary inference optimization reducing latency by 73% 
        compared to baseline
    - company: NVIDIA Research
      position: Research Intern
      date:
      start_date: 2022-05
      end_date: 2022-08
      location: Santa Clara, CA
      summary:
      highlights:
      - Designed sparse attention mechanism reducing transformer memory 
        footprint by 4.2x
      - Co-authored paper accepted at NeurIPS 2022 (spotlight presentation, top 
        5% of submissions)
    projects:
    - name: '[FlashInfer](https://github.com/)'
      date:
      start_date: 2023-01
      end_date: present
      location:
      summary: Open-source library for high-performance LLM inference kernels
      highlights:
      - Achieved 2.8x speedup over baseline attention implementations on A100 
        GPUs
      - Adopted by 3 major AI labs, 8,500+ GitHub stars, 200+ contributors
    - name: '[NeuralPrune](https://github.com/)'
      date: '2021'
      start_date:
      end_date:
      location:
      summary: Automated neural network pruning toolkit with differentiable 
        masks
      highlights:
      - Reduced model size by 90% with less than 1% accuracy degradation on 
        ImageNet
      - Featured in PyTorch ecosystem tools, 4,200+ GitHub stars
    publications:
    - title: 'Sparse Mixture-of-Experts at Scale: Efficient Routing for Trillion-Parameter
        Models'
      authors:
      - '*John Doe*'
      - Sarah Williams
      - David Park
      summary:
      doi: 10.1234/neurips.2023.1234
      url:
      journal: NeurIPS 2023
      date: 2023-07
    - title: Neural Architecture Search via Differentiable Pruning
      authors:
      - James Liu
      - '*John Doe*'
      summary:
      doi: 10.1234/neurips.2022.5678
      url:
      journal: NeurIPS 2022, Spotlight
      date: 2022-12
    selected_honors:
    - bullet: MIT Technology Review 35 Under 35 Innovators (2024)
    - bullet: Forbes 30 Under 30 in Enterprise Technology (2024)
    skills:
    - label: Languages
      details: Python, C++, CUDA, Rust, Julia
    - label: ML Frameworks
      details: PyTorch, JAX, TensorFlow, Triton, ONNX
    patents:
    - number: Adaptive Quantization for Neural Network Inference on Edge Devices
        (US Patent 11,234,567)
    - number: Dynamic Sparsity Patterns for Efficient Transformer Attention (US 
        Patent 11,345,678)
    invited_talks:
    - reversed_number: Scaling Laws for Efficient Inference — Stanford HAI 
        Symposium (2024)
    - reversed_number: Building AI Infrastructure for the Next Decade — 
        TechCrunch Disrupt (2024)

design:
  theme: classic
  page:
    size: us-letter
    top_margin: 0.7in
    bottom_margin: 0.7in
    left_margin: 0.7in
    right_margin: 0.7in
    show_footer: true
    show_top_note: true
  colors:
    body: rgb(0, 0, 0)
    name: rgb(0, 79, 144)
    headline: rgb(0, 79, 144)
    connections: rgb(0, 79, 144)
    section_titles: rgb(0, 79, 144)
    links: rgb(0, 79, 144)
    footer: rgb(128, 128, 128)
    top_note: rgb(128, 128, 128)
  typography:
    line_spacing: 0.6em
    alignment: justified
    date_and_location_column_alignment: right
    font_family:
      body: Source Sans 3
      name: Source Sans 3
      headline: Source Sans 3
      connections: Source Sans 3
      section_titles: Source Sans 3
    font_size:
      body: 10pt
      name: 30pt
      headline: 10pt
      connections: 10pt
      section_titles: 1.4em
    small_caps:
      name: false
      headline: false
      connections: false
      section_titles: false
    bold:
      name: true
      headline: false
      connections: false
      section_titles: true
  links:
    underline: false
    show_external_link_icon: false
  header:
    alignment: center
    photo_width: 3.5cm
    photo_position: left
    photo_space_left: 0.4cm
    photo_space_right: 0.4cm
    space_below_name: 0.7cm
    space_below_headline: 0.7cm
    space_below_connections: 0.7cm
    connections:
      phone_number_format: national
      hyperlink: true
      show_icons: true
      display_urls_instead_of_usernames: false
      separator: ''
      space_between_connections: 0.5cm
  section_titles:
    type: with_partial_line
    line_thickness: 0.5pt
    space_above: 0.5cm
    space_below: 0.3cm
  sections:
    allow_page_break: true
    space_between_regular_entries: 1.2em
    space_between_text_based_entries: 0.3em
    show_time_spans_in:
      - experience
  entries:
    date_and_location_width: 4.15cm
    side_space: 0.2cm
    space_between_columns: 0.1cm
    allow_page_break: false
    short_second_row: true
    degree_width: 1cm
    summary:
      space_above: 0cm
      space_left: 0cm
    highlights:
      bullet: •
      nested_bullet: •
      space_left: 0.15cm
      space_above: 0cm
      space_between_items: 0cm
      space_between_bullet_and_text: 0.5em
  templates:
    footer: '*NAME -- PAGE_NUMBER/TOTAL_PAGES*'
    top_note: '*LAST_UPDATED CURRENT_DATE*'
    single_date: MONTH_ABBREVIATION YEAR
    date_range: START_DATE – END_DATE
    time_span: HOW_MANY_YEARS YEARS HOW_MANY_MONTHS MONTHS
    one_line_entry:
      main_column: '**LABEL:** DETAILS'
    education_entry:
      main_column: |-
        **INSTITUTION**, AREA
        SUMMARY
        HIGHLIGHTS
      degree_column: '**DEGREE**'
      date_and_location_column: |-
        LOCATION
        DATE
    normal_entry:
      main_column: |-
        **NAME**
        SUMMARY
        HIGHLIGHTS
      date_and_location_column: |-
        LOCATION
        DATE
    experience_entry:
      main_column: |-
        **COMPANY**, POSITION
        SUMMARY
        HIGHLIGHTS
      date_and_location_column: |-
        LOCATION
        DATE
    publication_entry:
      main_column: |-
        **TITLE**
        SUMMARY
        AUTHORS
        URL (JOURNAL)
      date_and_location_column: DATE

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