rendercv

快速开始

可用主题： classic、harvard、engineeringresumes、engineeringclassic、sb2nov、moderncv 可用语言区域： english、arabic、danish、dutch、french、german、hebrew、hindi、hungarian、indonesian、italian、japanese、korean、mandarin_chinese、norwegian_bokmål、norwegian_nynorsk、persian、portuguese、russian、spanish、turkish、vietnamese

这些只是起点——设计和语言区域的每一个方面都可以在 YAML 文件中完全自定义。

# Install RenderCV
uv tool install "rendercv[full]"

# Create a starter YAML file (you can specify theme and locale)
rendercv new "John Doe"
rendercv new "John Doe" --theme moderncv --locale german

# Render to PDF (also generates Typst, Markdown, HTML, PNG by default)
rendercv render John_Doe_CV.yaml

# Watch mode: auto-re-render whenever the YAML file changes
rendercv render John_Doe_CV.yaml --watch

# Render only PNG (useful for previewing or checking page count)
rendercv render John_Doe_CV.yaml --dont-generate-pdf --dont-generate-html --dont-generate-markdown

# Override fields from the CLI without editing the YAML
rendercv render cv.yaml --cv.name "Jane Doe" --design.theme "moderncv"

YAML 结构

一份 RenderCV 输入包含四个部分。只有 cv 是必需的——其余部分都有合理的默认值。

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

单文件与分离文件： 这四个部分可以放在一个 YAML 文件中，也可以各自作为单独的文件。分离文件适合在多份简历之间复用同一套设计/语言区域：

# Single self-contained file (all sections in one file)
rendercv render John_Doe_CV.yaml

# Separate files: CV content + design + locale loaded independently
rendercv render cv.yaml --design design.yaml --locale-catalog locale.yaml --settings settings.yaml

使用分离文件时，每个文件只包含自己的部分（例如 design.yaml 以 design: 作为顶层键）。通过命令行加载的文件会覆盖主 YAML 文件中的值。

YAML 直接映射到 Pydantic 模型。下面提供了完整的类型安全 schema，以便你了解每个字段、其类型以及默认值。

Pydantic Schema

YAML 输入会根据这些 Pydantic 模型进行校验。

顶层模型

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.')

简历内容（cv）

cv.sections 字段是一个字典，键是章节标题（任意字符串），值是条目列表。每个章节包含的条目类型相同。

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=['[email protected]', ['[email protected]', '[email protected]']])
    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', '@[email protected]', '12345/john-doe'])

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

条目类型

cv.sections 是一个字典：键是章节标题（任意字符串），值是条目列表。每个章节必须使用单一条目类型——不能在同一章节中混用不同的条目类型。条目类型会根据每个条目中出现的字段自动检测。

共享字段——这些字段在支持日期和复杂字段的条目类型上可用（ExperienceEntry、EducationEntry、NormalEntry、PublicationEntry）：

字段	类型	默认值	备注
date	str | int | null	null	自由格式："2020-09"、"Fall 2023" 等。与 start_date/end_date 互斥。
start_date	str | int | null	null	严格格式：YYYY-MM-DD、YYYY-MM 或 YYYY。
end_date	str | int | "present" | null	null	与 start_date 相同的格式，或 "present"。当设置了 start_date 时，省略该字段默认为 "present"。
location	str | null	null
summary	str | null	null
highlights	list[str] | null	null	项目符号要点。

9 种条目类型：

条目类型	必需字段	可选字段	典型用途
ExperienceEntry	company、position	所有共享字段	工作经历、职位
EducationEntry	institution、area	degree + 所有共享字段	学位、学校
PublicationEntry	title、authors	doi、url、journal、summary、date	论文、文章
NormalEntry	name	所有共享字段	项目、奖项
OneLineEntry	label、details	—	技能、语言
BulletEntry	bullet	—	简单的项目符号要点
NumberedEntry	number	—	编号列表项
ReversedNumberedEntry	reversed_number	—	反向编号项（5、4、3……）
TextEntry	（纯字符串）	—	自由格式段落

示例：

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.

条目还接受任意额外的键（渲染时会被静默忽略）。字段名写错不会导致错误。

设计（design）

所有内置主题共用相同的结构——它们只是在默认值上有所不同。请参阅下面的示例设计，了解每个可用字段及其默认值。设置 design.theme 来选择主题，然后覆盖任意字段。

区域设置（locale）

内置区域设置：english、arabic、danish、dutch、french、german、hebrew、hindi、hungarian、indonesian、italian、japanese、korean、mandarin_chinese、norwegian_bokmål、norwegian_nynorsk、persian、portuguese、russian、spanish、turkish、vietnamese

将 locale.language 设置为内置区域设置名称即可使用。可覆盖任意字段以自定义翻译。将 language 设置为任意字符串并提供所有翻译，即可实现完全自定义的区域设置。

设置（settings）

关键字段：bold_keywords（要自动加粗的字符串列表）、current_date（覆盖今天的日期）、render_command.*（输出路径、生成标志）。

重要模式

YAML 引号

包含冒号（:）的字符串值必须加引号。 这是导致 YAML 无效的最常见原因。亮点、标题、摘要以及任何自由形式的文本中常常包含冒号：

# WRONG — colon breaks YAML parsing:
- title: Catalytic Mechanisms: A New Approach
  highlights:
    - Relevant coursework: Distributed Systems, ML

# RIGHT — wrap in double quotes:
- title: "Catalytic Mechanisms: A New Approach"
  highlights:
    - "Relevant coursework: Distributed Systems, ML"

规则：如果字符串值包含 :，则必须加引号。如有疑问，就加引号。

项目符号字符

design.highlights.bullet 字段只接受以下确切字符：●、•、◦、-、◆、★、■、—、○。不要使用短破折号（–）、>、* 或任何其他字符。如有疑问，请省略 bullet 以使用主题默认值。

电话号码

电话号码必须使用带国家代码的国际格式（E.164）。切勿编造电话号码——仅在用户提供时才包含。

# WRONG:
phone: "(555) 123-4567"
phone: "555-123-4567"

# RIGHT:
phone: "+15551234567"

如果用户提供的本地号码没有国家代码，请询问是哪个国家，或省略 phone 字段。

文本格式

所有文本字段都支持内联 Markdown：**bold**、*italic*、[link text](url)。不支持块级 Markdown（标题、列表、引用块、代码块）。原始 Typst 命令和数学公式（$$f(x)$$）也会原样传递。

日期处理

• date 与 start_date/end_date 互斥。如果提供了 date，则忽略 start_date 和 end_date。
• 如果只提供 start_date，则 end_date 默认为 "present"。
• start_date/end_date 要求严格格式：YYYY-MM-DD、YYYY-MM 或 YYYY。
• date 较为灵活：除日期格式外，还接受任何字符串（如 "Fall 2023"）。

章节标题

• snake_case 键会自动首字母大写：work_experience → "Work Experience"
• 包含空格或大写字母的键按原样使用。

出版物作者

使用 *Name*（单个星号，斜体）在作者列表中突出显示简历所有者。

嵌套亮点（子项目符号）

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

命令行参考

rendercv new "Full Name"

生成一个起始 YAML 文件。

选项	短选项	作用
--theme THEME		要使用的主题（默认：classic）
--locale LOCALE		要使用的区域设置（默认：english）
--create-typst-templates		同时创建可编辑的 Typst 模板文件，以便完全控制设计

rendercv render <input.yaml>

从 YAML 文件生成 PDF、Typst、Markdown、HTML 和 PNG。

选项	短选项	作用
--watch	-w	当 YAML 文件更改时自动重新渲染
--quiet	-q	抑制所有输出消息
--design FILE	-d	从单独的 YAML 文件加载 design 部分
--locale-catalog FILE	-lc	从单独的 YAML 文件加载 locale 部分
--settings FILE	-s	从单独的 YAML 文件加载 settings 部分
--output-folder DIR	-o	自定义输出目录

按格式控制：--{format}-path PATH 设置自定义输出路径，--dont-generate-{format} 跳过生成。格式：pdf、typst、markdown、html、png。

使用点号表示法从命令行覆盖任意 YAML 字段（无需编辑文件即可覆盖）：

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

rendercv create-theme "theme-name"

搭建一个自定义主题目录，其中包含可编辑的 Typst 模板，以便完全控制设计。

JSON Schema

用于 YAML 编辑器自动补全和验证：

# yaml-language-server: $schema=https://raw.githubusercontent.com/rendercv/rendercv/refs/tags/v2.8/schema.json

完整示例

示例简历

cv:
  name: John Doe
  headline:
  location: San Francisco, CA
  email: [email protected]
  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)

示例设计（经典 — 完整参考）

此处展示所有可用的设计字段及其默认值。所有主题共享相同的结构。

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

其他主题的覆盖项

其他主题仅覆盖上述经典默认值中的特定字段。要使用某个主题，请设置 design.theme，并可选择覆盖任意字段。每个主题还会自定义 design.templates（条目布局模式）——完整的模板结构请参见上方的经典示例。为简洁起见，下面的覆盖 YAML 省略了模板。

harvard

# yaml-language-server: $schema=../../../../../../schema.json
design:
  theme: harvard
  page:
    top_margin: 0.5in
    bottom_margin: 0.5in
    left_margin: 0.5in
    right_margin: 0.5in
    show_top_note: false
  colors:
    name: rgb(0,0,0)
    headline: rgb(0,0,0)
    connections: rgb(0,0,0)
    section_titles: rgb(0,0,0)
    links: rgb(0,0,0)
  typography:
    font_family:
      body: XCharter
      name: XCharter
      headline: XCharter
      connections: XCharter
      section_titles: XCharter
    font_size:
      name: 25pt
      connections: 9pt
      section_titles: 1.3em
  header:
    space_below_name: 0.5cm
    space_below_headline: 0.5cm
    space_below_connections: 0.5cm
    connections:
      show_icons: false
      separator: •
      space_between_connections: 0.4cm
  section_titles:
    type: centered_with_centered_partial_line
    space_below: 0.2cm
  sections:
    space_between_regular_entries: 1em
    show_time_spans_in: []
  entries:
    short_second_row: false

engineeringresumes

# yaml-language-server: $schema=../../../../../../schema.json
design:
  theme: engineeringresumes
  page:
    show_footer: false
  typography:
    font_family:
      body: XCharter
      name: XCharter
      headline: XCharter
      connections: XCharter
      section_titles: XCharter
    font_size:
      name: 25pt
      section_titles: 1.2em
    bold:
      name: false
  header:
    connections:
      separator: '|'
      show_icons: false
      display_urls_instead_of_usernames: true
  colors:
    name: rgb(0,0,0)
    connections: rgb(0,0,0)
    headline: rgb(0,0,0)
    section_titles: rgb(0,0,0)
    links: rgb(0,0,0)
  links:
    underline: true
    show_external_link_icon: false
  section_titles:
    type: with_full_line
    space_above: 0.5cm
    space_below: 0.3cm
  sections:
    space_between_regular_entries: 0.42cm
    space_between_text_based_entries: 0.15cm
    show_time_spans_in: []
  entries:
    short_second_row: false
    summary:
      space_above: 0.08cm
    side_space: 0cm
    highlights:
      bullet: ●
      nested_bullet: ●
      space_left: 0cm
      space_above: 0.08cm
      space_between_items: 0.08cm
      space_between_bullet_and_text: 0.3em

engineeringclassic

# yaml-language-server: $schema=../../../../../../schema.json
design:
  theme: engineeringclassic
  typography:
    font_family:
      body: Raleway
      name: Raleway
      headline: Raleway
      connections: Raleway
      section_titles: Raleway
    bold:
      name: false
      section_titles: false
  header:
    alignment: left
  links:
    show_external_link_icon: false
  section_titles:
    type: with_full_line
  sections:
    show_time_spans_in: []
  entries:
    short_second_row: false
    summary:
      space_above: 0.12cm
    highlights:
      space_left: 0cm
      space_above: 0.12cm
      space_between_items: 0.12cm

sb2nov

# yaml-language-server: $schema=../../../../../../schema.json
design:
  theme: sb2nov
  typography:
    font_family:
      body: New Computer Modern
      name: New Computer Modern
      headline: New Computer Modern
      connections: New Computer Modern
      section_titles: New Computer Modern
  colors:
    name: rgb(0,0,0)
    connections: rgb(0,0,0)
    section_titles: rgb(0,0,0)
    headline: rgb(0,0,0)
    links: rgb(0,0,0)
  links:
    underline: true
    show_external_link_icon: false
  section_titles:
    type: with_full_line
  sections:
    show_time_spans_in: []
  header:
    connections:
      hyperlink: true
      show_icons: false
      display_urls_instead_of_usernames: true
      separator: •
  entries:
    short_second_row: false
    highlights:
      bullet: ◦
      nested_bullet: ◦

moderncv

# yaml-language-server: $schema=../../../../../../schema.json
design:
  theme: moderncv
  typography:
    line_spacing: 0.6em
    font_family:
      body: Fontin
      name: Fontin
      headline: Fontin
      connections: Fontin
      section_titles: Fontin
    font_size:
      name: 25pt
      section_titles: 1.4em
    bold:
      name: false
      section_titles: false
  header:
    alignment: left
    photo_width: 4.15cm
    photo_space_left: 0cm
    photo_space_right: 0.3cm
  links:
    underline: true
    show_external_link_icon: false
  section_titles:
    type: moderncv
    space_above: 0.55cm
    space_below: 0.3cm
    line_thickness: 0.15cm
  sections:
    show_time_spans_in: []
  entries:
    short_second_row: false
    side_space: 0cm
    space_between_columns: 0.3cm
    summary:
      space_above: 0.1cm
    highlights:
      space_left: 0cm
      space_above: 0.15cm
      space_between_items: 0.1cm
      space_between_bullet_and_text: 0.3em