Build Tools Used for the ai-job-search Project: Python, Bun, and LaTeX Pipeline

The ai-job-search repository combines Python 3.10+ for automation scripting, Bun for TypeScript CLI tooling, and LaTeX (lualatex/xelatex) for document generation, orchestrated through GitHub Actions CI.

The ai-job-search project by MadsLorentzen relies on a heterogeneous build environment that spans multiple runtimes and compilers. Understanding the build tools used for the ai-job-search project requires examining three distinct layers: the Python automation framework, the JavaScript/TypeScript CLI tooling, and the LaTeX document compilation pipeline. Each layer serves a specific purpose in the job application workflow, from scraping job portals to generating tailored CVs and cover letters.

Python 3.10+ Runtime and Automation Scripts

The foundation of the project rests on Python 3.10+, which powers the core automation utilities located in the tools/ directory. These scripts handle framework validation, skill linting, and PDF verification.

Key utilities include:

• tools/lint_skills.py – Validates skill definitions and ensures consistency across job portal integrations
• tools/verify_pdf.py – Verifies compiled PDFs using the pypdf library or falls back to the pdftotext utility from Poppler
• tools/check_framework_version.py – Validates installed runtime versions during CI checks

The /apply, /scrape, and /setup commands invoked by users are Python-driven entry points that orchestrate the broader toolchain.

Bun and TypeScript for Job Portal CLIs

The project uses Bun as a lightweight JavaScript/TypeScript runtime for job-portal CLI tools located under .agents/skills/*. Each skill (such as jobindex-search, linkedin-search, or jobbank-search) contains its own TypeScript CLI package.

Installing CLI Dependencies

According to the README.md "Install job search tools" section, users must run bun install for each skill directory:

# From the repository root

for tool in jobbank-search jobdanmark-search jobindex-search \
            jobnet-search linkedin-search freehire-search; do
  (cd .agents/skills/$tool/cli && bun install)
done

Type Checking with tsc

Each skill's package.json defines a typecheck script that invokes the TypeScript compiler:

cd .agents/skills/jobindex-search/cli
bun run typecheck        # invokes `tsc --noEmit`

This ensures type safety across the JavaScript-based portal integrations without emitting compiled files, maintaining a clean source tree.

LaTeX Document Compilation Pipeline

The document generation layer relies on LaTeX with engine-specific compilation workflows for CVs and cover letters.

CV Generation with lualatex

The CV template at cv/main_example.tex compiles using lualatex to produce a two-page PDF:

lualatex -interaction=nonstopmode cv/main_example.tex

Cover Letter Generation with xelatex

Cover letters use the class file cover_letters/cover.cls and compile with xelatex for proper font rendering:

xelatex -interaction=nonstopmode cover_letters/cover_example.tex

Both engines are required dependencies listed in SETUP.md, as the /apply workflow automatically invokes the appropriate compiler based on document type.

PDF Processing and Verification

After compilation, the system verifies PDF integrity using dual approaches. The tools/verify_pdf.py utility first attempts extraction via the pypdf library, then falls back to the pdftotext command-line utility from Poppler if needed. This verification ensures that generated documents contain searchable text layers and meet page-count requirements (two pages for CVs, one page for cover letters).

from tools.verify_pdf import verify_pdf

# Checks page count and extracts searchable text

verify_pdf("output/cv.pdf")

Continuous Integration with GitHub Actions

The GitHub Actions workflow defined in .github/workflows/ci.yml orchestrates the entire build matrix. The CI pipeline runs:

• LaTeX smoke tests to verify template compilation
• TypeScript type-checking (bun run typecheck) across all skill CLIs
• Python linting via tools/lint_skills.py

This ensures that changes to any layer of the build system—whether Python automation, TypeScript skills, or LaTeX templates—pass validation before merging.

Summary

• Python 3.10+ powers the core automation framework and utility scripts in tools/
• Bun provides the JavaScript runtime for TypeScript-based job portal CLIs under .agents/skills/*/cli
• TypeScript (tsc --noEmit) enforces type safety via npm-style scripts invoked through Bun
• LaTeX engines (lualatex for CVs, xelatex for cover letters) compile application documents from .tex sources
• GitHub Actions validates the entire toolchain through automated CI checks

Frequently Asked Questions

What version of Python is required for ai-job-search?

The project requires Python 3.10 or higher as specified in SETUP.md. This version requirement ensures compatibility with modern type hints and standard library features used in the automation scripts.

Why does the project use Bun instead of Node.js?

The repository uses Bun as a lightweight, fast alternative to Node.js for running the job-portal CLI tools. The README.md specifically instructs users to run bun install for each skill, and the package.json files in skill directories define Bun-compatible scripts for starting services and running tests.

How are the CV and cover letter templates compiled?

The build system uses lualatex to compile cv/main_example.tex into a two-page CV and xelatex to compile cover letters using cover_letters/cover.cls. These commands run with -interaction=nonstopmode for automated pipeline execution, and the engine selection is hardcoded in the /apply workflow logic.

What CI checks run on pull requests?

The GitHub Actions workflow (.github/workflows/ci.yml) executes three main validation layers: LaTeX compilation smoke tests for document templates, TypeScript type-checking via bun run typecheck across all skills, and Python linting using tools/lint_skills.py to validate skill definitions.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}