Quick Start · How It Works · Providers · The 8 Laws

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calibrcv is an open-source CLI resume builder and ATS optimizer. It takes your resume PDF, rewrites it using AI under strict editorial rules, compiles it to LaTeX, and enforces a single-page limit through an agentic trim loop. Runs fully offline with Ollama. No account, no cloud, no data leaves your machine unless you choose a cloud provider.

Most resume tools slap a template on your content and call it optimized. calibrcv runs a 9-stage pipeline with an actual scoring engine. It rewrites every bullet, enforces action verbs, kills filler, compiles to LaTeX, and loops until the result fits on one page. Then it scores the output against 5 ATS categories so you know exactly where you stand.

Quick Start

npm install -g calibrcv

# pull a local model (free, runs on your machine)
ollama pull llama3.1

# optimize your resume
calibrcv build resume.pdf

That's it. Three commands. You get back a PDF, a .tex source file, and a terminal score report.

Just want a score? calibrcv score resume.pdf runs instantly, no AI needed.

What Happens When You Run It

resume.pdf
    |
    v
 1. Parse PDF .............. extract text (or use VLM for image-based PDFs)
    |
    v
 2. Analyze ................ LLM diagnoses weaknesses, generates 3-7 questions
    |
    v
 3. Enrichment Interview ... you answer in the terminal (skippable)
    |
    v
 4. Synthesize ............. LLM rewrites every section under the 8 Laws
    |
    v
 5. Generate LaTeX ......... fills the sb2nov/resume template
    |
    v
 6. Compile + Trim Loop .... compiles to PDF; if >1 page, AI trims
    |    ^                   and recompiles (up to 6 rounds)
    |    |___________________|
    v
 7. ATS Score .............. pure algorithmic, 100-point scale
    |
    v
 optimized.pdf + resume.tex + score report

Usage

Build (full pipeline)

# basic
calibrcv build resume.pdf

# custom output path
calibrcv build resume.pdf -o optimized.pdf

# target a sector
calibrcv build resume.pdf --sector banking

# tailor for a specific job posting
calibrcv build resume.pdf --job-url https://linkedin.com/jobs/view/123456

# tailor from a job description file
calibrcv build resume.pdf --job-desc posting.txt

# paste job description inline
calibrcv build resume.pdf --job-desc "Senior engineer role requiring Python, AWS..."

# auto-detect URL in --job-desc
calibrcv build resume.pdf --job-desc https://linkedin.com/jobs/view/123456

# use a specific cloud provider
calibrcv build resume.pdf --provider openai

# use any model with any provider
calibrcv build resume.pdf --provider anthropic --model claude-sonnet-4-20250514
calibrcv build resume.pdf --provider openai --model gpt-4o
calibrcv build resume.pdf --provider groq --model llama-3.3-70b-versatile

# use a different Ollama model
calibrcv build resume.pdf --model mistral

# skip the enrichment Q&A
calibrcv build resume.pdf --skip-enrich

# use VLM (vision model) for PDF extraction
calibrcv build resume.pdf --vlm

# use a specific vision model with Ollama
calibrcv build resume.pdf --vlm --vlm-model qwen2-vl

Score (no AI needed)

Just want to know where your resume stands? The scoring engine is pure algorithmic: it uses a smart heuristic parser to detect sections, extract bullets, and identify contact info. No LLM calls, runs instantly.

# basic score
calibrcv score resume.pdf

# score against a job description file
calibrcv score resume.pdf --job-desc posting.txt

# score against a live job posting
calibrcv score resume.pdf --job-url "https://linkedin.com/jobs/view/123456"

# score with inline job description text
calibrcv score resume.pdf --job-desc "Looking for a backend engineer with Python, Go..."

LLM Providers

calibrcv tries providers in order and falls back automatically. Ollama is always first.

Provider	Default Model	How to enable
Ollama (default)	llama3.1:8b	ollama serve + ollama pull llama3.1
Groq	llama-3.3-70b	Set GROQ_API_KEY
Google Gemini	gemini-2.5-flash	Set GEMINI_API_KEY
OpenRouter	llama-3.1-8b (free tier)	Set OPENROUTER_API_KEY
OpenAI	gpt-4o-mini	Set OPENAI_API_KEY
Anthropic	claude-sonnet-4	Set ANTHROPIC_API_KEY

The --model flag works with any provider:

calibrcv build resume.pdf --provider openai --model gpt-4o
calibrcv build resume.pdf --provider anthropic --model claude-sonnet-4-20250514
calibrcv build resume.pdf --provider groq --model llama-3.3-70b-versatile

Put your keys in .env in your working directory or at ~/.calibrcv/.env:

GROQ_API_KEY=gsk_...
GEMINI_API_KEY=AI...
OPENROUTER_API_KEY=sk-or-...
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
OLLAMA_MODEL=llama3.1
VLM_MODEL=qwen2-vl

Force a specific provider: calibrcv build resume.pdf --provider groq

VLM-Based PDF Parsing (click to expand)

By default, calibrcv extracts text from PDFs using pdf-parse. For scanned or image-based PDFs (where text extraction returns little to nothing), calibrcv can use a Vision Language Model (VLM) to read the PDF as an image.

# explicitly use VLM extraction
calibrcv build resume.pdf --vlm

# auto-fallback: if pdf-parse detects an image-based PDF, VLM kicks in automatically
calibrcv build resume.pdf

VLM provider waterfall: OpenAI GPT-4o -> Anthropic Claude -> Gemini Flash -> Ollama qwen2-vl

Provider	Model	How to enable	Speed
OpenAI	gpt-4o	Set OPENAI_API_KEY	Fast
Anthropic	claude-sonnet-4	Set ANTHROPIC_API_KEY	Fast
Google Gemini	gemini-2.5-flash	Set GEMINI_API_KEY	Fast
Ollama (local)	qwen2-vl	ollama pull qwen2-vl	Slower, fully offline

Recommended Ollama vision models:

Model	Size	Best for
qwen2-vl	4.4 GB	Best quality, default choice
llava:13b	8 GB	Strong alternative
bakllava	4.7 GB	Faster, good quality
llava	4.7 GB	Lightweight option

The 8 CalibrCV Laws

Every resume produced by calibrcv follows these rules. No exceptions, no overrides.

#	Law	What it means
1	100-Character Bullets	Every bullet fits in 100 characters. Period.
2	HBS Action Verbs	Every bullet opens with an approved verb (Architected, Deployed, Engineered...)
3	Harvard-Style Summary	3-4 sentences, zero pronouns (no I/my/me/we), executive voice
4	Realistic Grounding	No inflated claims. Seniority-appropriate language only.
5	Zero Em Dashes	Replaced with semicolons, colons, or restructured sentences
6	Two-Line Skills	Exactly two rows: "Quantitative Stack" and "Analytic Domain"
7	Strict Bullet Counts	Experience: 2-3 bullets. Projects: exactly 2.
8	Abbreviated Dates	"Jun. 2023" format throughout

ATS Scoring Breakdown (click to expand)

The scoring engine is pure math. No AI calls, no external API. Five categories, 100 points.

Category	Points	What it checks
Structural Integrity	0-20	Required sections present, dates on all entries
Keyword Density	0-30	TF-IDF matching against job description with stopword filtering and stem matching (or lexical richness without JD)
Content Quality	0-25	HBS verb compliance, quantified metrics, bullet length
Parsability	0-15	Box-drawing chars, em dashes, smart quotes, encoding issues
Completeness	0-10	Email, phone, LinkedIn, location, skills breadth

Before & After

Before	After (CalibrCV)

Want the LaTeX source?

• example_before.tex: Colorful template resume (sidebar, skill bars, profile photo)
• example_after.tex: CalibrCV output (clean, quantified, ATS-optimized)

Compile them yourself: pdflatex assets/example_after.tex

Prerequisites

• Node.js 18+
• Ollama for local LLM (or a cloud API key from the table above)
• LaTeX compiler for PDF output: tectonic, pdflatex, or xelatex
  • macOS: brew install tectonic or brew install --cask mactex
  • Ubuntu/Debian: sudo apt install texlive-full
  • Windows: MiKTeX
  • If no compiler is found, calibrcv still outputs the .tex source file

Why This Exists

Everyone deserves a great resume.

I applied to hundreds of internships as an undergrad. Wrote cover letters at 2am, tweaked margins to squeeze in one more bullet, and still got ghosted by ATS systems that never showed my resume to a human.

Career tools that actually work are locked behind paywalls, premium tiers, and monthly subscriptions. The people who need them most, students, career changers, people breaking into tech, are the ones least able to pay.

CalibrCV is the answer: a professional-grade resume pipeline you can install in 10 seconds and run offline. The AI logic, the prompts, the scoring engine, the LaTeX template: all open source. No account, no cloud, no paywall.

The job market is rough enough. Your resume format should not be the thing that stops you.

"The challenge of life, I have found, is to build a resume that doesn't
simply tell a story about what you want to be, but it's a story about who you want to be."

— Oprah Winfrey

Contributing

PRs welcome. If you want to add a new LLM provider, the interface is simple: look at src/providers/ollama.js (55 lines) as a reference.

git clone https://github.com/Coflazo/calibrcv-cli.git
cd calibrcv-cli
npm install
node bin/calibrcv.js --help

Acknowledgments

This project stands on the shoulders of generous tools and teams:

   

Grateful to fal.ai and azerion.ai for the infrastructure and support that helped bring this project to life.

• Jake's Resume: The LaTeX template that powers every CalibrCV output
• Ollama: Making local LLMs accessible to everyone

License

MIT