cvcite

Build your CV from one master YAML file, tailor it for each kind of role with a small overlay, and gate every PDF the way applicant tracking systems (ATS) read it. Every claim on the CV traces to a fact you confirmed, with its source.

It comes in three parts that work together or alone:

• A command line (cvcite) that checks, renders and gates your CV. No AI involved.
• A Claude Code plugin whose skills interview you, one multiple-choice question at a time: they turn an old CV or a LinkedIn export into a fact ledger, check every date and claim, tailor the CV for a job posting, and compare LinkedIn, your website and GitHub against it.
• A GitHub Action that builds and gates the CV on every change, publishes the PDFs as a private release when you ask, and can run an AI claim review on pull requests with your own key.

Your CV lives in your own private repository. This one holds the tool and a made-up sample person.

Why

• Two CVs drift apart. Keep one master with every fact; each variant is an overlay that only changes emphasis (headline, summary, bolded words, skills line, which bullets to drop).
• CVs overclaim. Every number on the CV must appear in the fact ledger (facts.yaml) with its source, or the build fails. Facts marked private can never reach the CV.
• A PDF that looks right can read wrong. Parsers split hyphenated words, lose ligature glyphs, shatter text into one word per line, or read columns in the wrong order. The gate reads each PDF with two to four independent extractors and fails it unless they agree and every bullet survives whole.

Quick start

You need Python 3.12 or newer (RenderCV 2.8 needs it). uv is the easiest way to install; Ghostscript is optional (a third extractor for the gate).

uv tool install git+https://github.com/agusgonzaleznic/cvcite
cvcite init my-cv --sample      # try it with the made-up sample; drop --sample to start blank
cd my-cv
cvcite build                    # check, render and gate every variant

The PDFs land in build/<variant>/, one folder per variant, each holding the file under the same name, so a recruiter cannot tell that variants exist. Keep the repository private:

git init -b main && git add -A && git commit -m "Start my CV"
gh repo create my-cv --private --source . --push

With Claude Code

/plugin install cvcite --marketplace agusgonzaleznic/cvcite

Or in two steps: /plugin marketplace add agusgonzaleznic/cvcite, then /plugin install cvcite@cvcite.

Then ask in your own words, for example "start my CV from inputs/old-cv.pdf" or "tailor my CV for this posting: ". The skills:

The skills follow the open Agent Skills format, so other agents that read SKILL.md files can use them too; cvcite init also writes an AGENTS.md with the rules into your CV repository.

The plugin holds only its skills and the reviewer agent, in plugin/; the skills run the cvcite command line you installed above, and ask before installing it if it is missing.

LinkedIn is read only. The audit works from LinkedIn's own data export or a profile saved as PDF. Nothing signs in to your account or edits it: LinkedIn's terms forbid automated access.

In GitHub Actions

cvcite init writes three workflows into your CV repository:

• cv.yml checks, renders and gates every variant on each pull request and each push to main.
• release.yml (run by hand) publishes the build as a release you can download anywhere. It refuses to run in a public repository, because the PDFs carry your contact details.
• cv-review.yml runs the claim reviewer on pull requests. It does nothing until you add an ANTHROPIC_API_KEY secret, and each run is billed to that key. It sends AGENTS.md, facts.yaml, cv.yaml (contact details included), the overlays and the diff to Anthropic's API; facts in facts.private.yaml are never committed, so never sent. The AI can only read the CV files, and its review is posted as plain text. Use a key made for this alone, with a spending limit.

The action runs cvcite from the release you pin, with every Python package taken from a lock with its hashes and no package built from source. init pins it to that release's full commit SHA. The action refuses to build in a repository that is not private; set allow-public: "true" only for a made-up CV, as this repository does for its sample. These guards stop mistakes; anyone who can push to your CV repository can still change its workflows.

What gets checked

cvcite check (no rendering) and every build:

• dates: format, order, future dates, overlaps, gaps over six months
• every number in a bullet present in a fact of the ledger; none from a private fact, and none of the terms a private fact lists (keep private facts in facts.private.yaml, which git ignores); every fact's visibility one of public, cv-only or private
• every skill on a skills line backed by a bullet of the same variant
• an overlay never adds a skill the master lacks
• only RenderCV's built-in themes, and no render settings that move files: a custom theme runs code from the repository
• no TODO, no banned character (em dashes by default), no RenderCV trap
• privacy: no photo, date of birth, nationality, marital status, street address, identity or bank number, or WhatsApp link

The gate, on every PDF:

• the extractors agree on the words and their order, and every bullet appears whole in each
• no word hyphenated across a line by the typesetter
• no text layer shattered into single words
• every glyph plainly filled, in a colour with at least 4.5:1 contrast on white
• the page count matches the budget in cvcite.toml

Each check has a test that plants the defect and proves the check fails on it.

Commands

How it renders

RenderCV turns the YAML into Typst and then into the PDF. cvcite renders with ligatures and kerning off: with ligatures on, one extractor lost every word containing "ff", "ffi" or "ffl", and with kerning on, another read a bold "Airflow" as two words. It also ships the one Typst package RenderCV's template imports and switches off RenderCV's check for a newer release (a PyPI request made without verifying TLS), so a render makes no network request and writes nothing outside the build.

Development

uv venv --python 3.13 && uv pip install -e ".[test]"
.venv/bin/python -m pytest

Licence

MIT. RenderCV is MIT-licensed; the vendored Typst package fontawesome 0.6.0 is MIT-licensed, with its licence in src/cvcite/vendor/.