resume-tailor

A small Python tool that takes a LaTeX résumé and a job description, and rewrites the bullets, and only the bullets, to read like they were written for that role. The hard line it tries not to cross is inventing anything. If a bullet says I built a forty-tab cash-flow model in March 2025, the tool can re-tense it, re-emphasize it, swap the verb, or surface a more relevant outcome. It can't claim I built fifty tabs, or did it in February.

Why I wrote it

SIPA's on-cycle market is a treadmill. Fifteen banks, ten consultancies, twelve climate funds, all wanting a tailored résumé and a tailored cover letter, all due the same Sunday. The tailoring itself isn't intellectually hard. It's a lot of careful copy-paste and a lot of judgment calls about which bullet leads.

I did the first round by hand, the way one is supposed to. By the seventh résumé I noticed I was making the same five edits over and over: tense changes, lead-bullet reorderings, swapping out "Python" for "Python (pandas, statsmodels)" when the JD asked for it. Short, local rewrites under a clear constraint are something a model is genuinely good at.

So I wrote it.

How it works

A .tex file goes in. The tool parses out each \item{...} bullet, builds a context of your full résumé, the JD, the role's seniority, and any guardrails you set, then asks a model to rewrite each bullet under three rules:

1. Don't add facts. If a bullet doesn't have a number, the rewrite doesn't get to invent one.
2. Keep proper nouns. Wood Mackenzie stays Wood Mackenzie; "a research firm" is not allowed as a substitute.
3. Match the JD's vocabulary. If the JD says "valuation," the bullet doesn't say "modeling."

Output is a new .tex file plus a colored diff so you can scan what changed and decline anything that drifted. There's a flag for cover-letter generation that runs the same constraints over a five-paragraph template.

It works with Claude Code (claude -p as the inference backend) or any LiteLLM-compatible endpoint. I run it most often against Claude Sonnet for tailoring, and Opus when something feels like it needs a second pass.

What it doesn't do

• It doesn't typeset. You still run pdflatex.
• It doesn't track outcomes. There's no "résumé v3 got me an interview at firm Y" loop. I tried wiring one up and decided it was creepy.
• It doesn't tailor to culture, as in "this firm is collegial, write warmer." The model will do that if you ask, but the results were mediocre and I cut the feature.

Caveat

There is a real ethical line at the edge of this kind of tool. Tailoring is acceptable; fabricating is not. The three rules above exist to keep the tool on the right side of that line, though a user can absolutely override them and ask the model to lie. I'd rather you not.

Clone

git clone https://github.com/Waaangjl/resume-tailor
cd resume-tailor
pip install -r requirements.txt
python tailor.py --resume me.tex --jd job.txt --out me-tailored.tex

Repository: github.com/Waaangjl/resume-tailor.