Career Toolkit

Transform career data into multiple formats - resumes, cover letters, LinkedIn content, vector embeddings, and more.

What is this?

Career Toolkit is a Python library for managing career data. You maintain your work history, education, and skills in one YAML file, then transform it into whatever format you need using pluggable output engines.

# Store once
career-data.yml

# Transform many ways
→ resume.pdf          (PDF engine)
→ resume.docx         (DOCX engine)
→ linkedin.txt        (LinkedIn engine)
→ embeddings.faiss    (Vector index engine)
→ profile.json        (JSON engine)

Installation

Prerequisites

Python 3.9+
PDM
wkhtmltopdf: brew install wkhtmltopdf (macOS) or apt-get install wkhtmltopdf (Ubuntu)

Install

git clone https://github.com/yourusername/career-toolkit.git
cd career-toolkit
pdm install

Quick Start

1. Create your data file (data.yml):

personal:
  name:
    first: Jane
    last: Doe
  email: jane.demo@example.com
  linkedin:
    text: linkedin.com/in/jane-demo-doe
    url: https://www.linkedin.com/in/jane-demo-doe

work:
  - company: Tech Co
    positions:
      - title: Senior Engineer
        dates:
          start: "2020-01"
          end: Present
        description: Led development of AI features...

See examples/data.example.yml for a complete example.

2. Generate outputs:

# PDF resume
career-toolkit generate --data data.yml --output resume.pdf

# DOCX resume
career-toolkit generate --data data.yml --output resume.docx --engine docx-simple

# Public version (masks contact info)
career-toolkit generate --data data.yml --output resume.pdf --no-contact

Output Engines

Each format is handled by an output engine:

Engine	Description	Status
`pdf`	PDF resume via HTML templates	✅ Ready
`docx-simple`	Simple single-column DOCX	✅ Ready
`docx-two-column`	Two-column DOCX layout	✅ Ready
`json-profile`	JSON export (for websites/APIs)	✅ Ready
`cover-letter-docx`	Cover letter generation	✅ Ready
`linkedin`	LinkedIn profile sections	🔲 Planned
`vector-index`	FAISS/ChromaDB embeddings	🔲 Planned
`markdown`	Markdown resume	🔲 Planned

List available engines:

career-toolkit list-engines

Architecture

career-toolkit/
├── src/career_toolkit/
│   ├── core/              # Data loading, YAML processing
│   ├── engines/           # Output engines (PDF, DOCX, etc.)
│   │   ├── base.py        # Base OutputEngine class
│   │   ├── pdf.py
│   │   └── docx.py
│   ├── templates/         # Jinja2 template system
│   └── cli.py             # CLI interface
└── templates/             # Jinja2 templates
    ├── cv/
    └── cover_letter/

Adding a New Engine

Extend OutputEngine and register it:

from career_toolkit.engines.base import OutputEngine

class LinkedInExporter(OutputEngine):
    @property
    def file_extension(self) -> str:
        return "txt"

    @property
    def engine_name(self) -> str:
        return "LinkedIn Profile Export"

    def generate(self, data, output_path):
        # Generate delineated sections for manual copy-paste
        sections = self._format_linkedin_sections(data)
        output_path.write_text(sections)

Register in engines/__init__.py:

ENGINE_REGISTRY["linkedin"] = LinkedInExporter

Data Privacy

Keep sensitive data separate:

# sensitive.yml (gitignored)
personal:
  email: real@email.com
  phone:
    mobile: "+1 555-123-4567"

career-toolkit generate --data data.yml --sensitive sensitive.yml --output resume.pdf

Or mask it for public versions:

career-toolkit generate --data data.yml --output resume.pdf --no-contact

Usage Examples

# Basic PDF
career-toolkit generate -d data.yml -o resume.pdf

# DOCX with custom template
career-toolkit generate -d data.yml -o resume.docx -e docx-simple

# Public version (no contact info)
career-toolkit generate -d data.yml -o resume_public.pdf --no-contact

# With sensitive data merged
career-toolkit generate -d data.yml -s sensitive.yml -o resume.pdf

GitHub Projects Integration

Automatically fetch your GitHub repositories and include them in your profile with custom enrichments.

Quick Start

1. Fetch all your repos (quick list):

export GITHUB_TOKEN="ghp_your_token_here"  # Optional but recommended
career-toolkit github-sync-list --username YOUR_USERNAME
# Creates: .github-cache/github-list.yml

2. Review list, then fetch details for selected repos:

career-toolkit github-sync-details --username YOUR_USERNAME \
  --repos career-toolkit,awesome-project,cool-app
# Creates: .github-cache/github-details.yml

3. Create enrichment file:

cp examples/github-enrichment.example.yml data/github-enrichment.yml
# Edit to add custom descriptions, featured flags, priorities, etc.

4. Export profile with GitHub projects:

career-toolkit export --data data.yml --format json \
  --github-details .github-cache/github-details.yml \
  --github-enrichment data/github-enrichment.yml \
  -o profile.json

Enrichment File Format

projects:
  my-awesome-project:
    featured: true              # Mark as featured
    priority: 1                 # Order (lower = higher priority)
    custom_description: |       # Override GitHub description
      Custom pitch for this project...
    highlights:                 # Key features/achievements
      - Built with Python and FastAPI
      - Deployed on AWS with 99.9% uptime
    demo_url: https://demo.example.com
    image_url: https://example.com/screenshot.png
    category: Web Development
    tags:                       # Merged with GitHub topics
      - python
      - fastapi

  old-experiment:
    hidden: true                # Exclude from profile

GitHub Token Setup

For private repos and higher rate limits (5000/hr vs 60/hr):

Go to https://github.com/settings/tokens
Generate new token (classic) with repo scope
Set environment variable:
```
export GITHUB_TOKEN="ghp_your_token_here"
```
Or pass directly: --token ghp_your_token_here

Data Files Structure

data/
├── data.yml                  # Your career data
├── github-enrichment.yml     # Your manual GitHub project enrichments (checked in)
└── .github-cache/            # Generated GitHub API cache (gitignored)
    ├── github-list.yml       # All repos (from sync-list)
    └── github-details.yml    # Selected repos detailed data (from sync-details)

Updating Dependencies

If you're using career-toolkit as a dependency in another project:

# In your project folder (e.g., my-career-data)
pdm update career-toolkit     # Pull latest changes including GitHub features

Or:

# In career-toolkit folder
pdm install                   # Install/update dependencies

# Then in your project folder
pdm update career-toolkit     # Pick up the changes

PDM Scripts (for your data folder)

Add to your pyproject.toml:

[tool.pdm.scripts]
# GitHub projects integration
github-sync-list = {cmd = "career-toolkit github-sync-list --output data/.github-cache/github-list.yml", help = "Fetch list of all GitHub repos"}
github-sync-details = {cmd = "career-toolkit github-sync-details --output data/.github-cache/github-details.yml", help = "Fetch details for specific repos"}
export-profile-github = "career-toolkit export --data data/data.yml --format json --github-details data/.github-cache/github-details.yml --github-enrichment data/github-enrichment.yml --output exports/profile.json"

Then run:

pdm run github-sync-list --username YOUR_USERNAME
pdm run github-sync-details --username YOUR_USERNAME --repos repo1,repo2
pdm run export-profile-github

Development

pdm run test         # Run tests
pdm run format       # Format code
pdm run lint         # Lint code
pdm run example-pdf  # Generate example PDF

Why This Design?

Single source of truth - Maintain data once, generate many formats Pluggable engines - Easy to add new output formats Privacy control - Separate sensitive data Template-based - Fully customizable output Automation-ready - Foundation for AI-powered tools (vector indexing, job matching, etc.)

Career Toolkit's job is simple: data goes in → formatted output comes out.

License

MIT

Author

Ben Huckvale

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Repository files navigation

Career Toolkit

What is this?

Installation

Prerequisites

Install

Quick Start

Output Engines

Architecture

Adding a New Engine

Data Privacy

Usage Examples

GitHub Projects Integration

Quick Start

Enrichment File Format

GitHub Token Setup

Data Files Structure

Updating Dependencies

PDM Scripts (for your data folder)

Development

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Name		Name	Last commit message	Last commit date
Latest commit History 17 Commits
examples		examples
src/career_toolkit		src/career_toolkit
templates		templates
tests		tests
.gitignore		.gitignore
README.md		README.md
pyproject.toml		pyproject.toml

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Career Toolkit

What is this?

Installation

Prerequisites

Install

Quick Start

Output Engines

Architecture

Adding a New Engine

Data Privacy

Usage Examples

GitHub Projects Integration

Quick Start

Enrichment File Format

GitHub Token Setup

Data Files Structure

Updating Dependencies

PDM Scripts (for your data folder)

Development

Why This Design?

License

Author

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