How to Make Claude Code and Cursor Rewrite Your Resume

Recently I caught myself spending more energy on updating my resume than on refactoring a complex legacy codebase. You sit in front of a blank document trying to remember exactly which metric that microservice improved two years ago. The situation is made worse by the fact that modern HR departments run applications through automated ATS (Applicant Tracking System) scanners. If the text doesn't contain the right keywords, a live recruiter won't even open your resume.

Usually for tasks like this, people turn to ChatGPT with lengthy prompts. But prompts get lost quickly, and you have to set the context again for every application. On GitHub, I came across an interesting repository ResumeSkills. The author packaged the entire job search process into a set of 20 ready-made skills for Claude Code, Cursor, Windsurf, and other AI assistants.

What are assistant skills

In modern AI tools for developers, a skill is a Markdown file with structured instructions, rules, and examples. When you add such a file to your working environment, the assistant reads the task context and follows a pre-described algorithm.

Instead of manually explaining to the model "imagine you're a career consultant, analyze the job posting and find discrepancies," you simply invoke the needed scenario. The assistant itself understands what response structure to produce and what to focus on first.

What's inside the repository

The repository contains two dozen files covering virtually all stages of hiring. They can be broken down into several areas:

• Resume text optimization. This includes modules for ATS compatibility checking, rewriting weak points with measurable results (resume-bullet-writer), evaluating achievement digitization (resume-quantifier), and cleaning up document structure (resume-formatter).
• Job posting analysis and tailoring. Module job-description-analyzer breaks down the job posting text, finds gaps in your experience, and calculates a match percentage. And resume-tailor helps adapt phrasing for a specific position without fabrication or embellishing facts.
• Interview preparation. Skill interview-prep-generator takes your real projects and formats them into stories using the STAR methodology (Situation, Task, Action, Result), as well as generates a list of likely technical questions.
• Negotiations and career pivots. The collection includes scenarios for salary negotiation (salary-negotiation-prep), adapting experience for tech lead or manager roles (executive-resume-writer), and translating skills when switching stacks (career-changer-translator).

How it works in practice

The most useful scenario in everyday job searching is the combination of job posting analysis and phrasing improvement.

Let's say you have a typical line: "Developed the backend for a payment gateway in Go." Skill resume-bullet-writer will restructure it into an action format with a measurable result. The model will ask you for approximate numbers (load, latency, failure rate) and turn a dry fact into a strong bullet point: "Designed and implemented a payment processing service in Go, reducing p99 latency from 450ms to 80ms under peak load of 12,000 RPS."

If you don't have exact figures on hand, resume-quantifier comes into play. It will guide you on how to realistically estimate the scope of work through indirect indicators: team size, number of users, or volume of processed data.

The second common case is interview preparation. You feed the model the role description and your experience, launching interview-prep-generator. The output gives you not generic advice, but a detailed breakdown of your own projects. The model outlines which architectural trade-offs you're most likely to be asked about in the system design section and how to best explain the reasons for choosing specific technologies.

Installation and integration

The simplest way to connect all skills at once is to use the package manager via npx.

Installing all 20 modules globally:

npx skills add Paramchoudhary/ResumeSkills -g -y

If you want to connect them only to the current project with your resume in Markdown or LaTeX:

npx skills add Paramchoudhary/ResumeSkills -y

For manual installation in Cursor, simply clone the repository and copy the files to your local configuration directory:

git clone https://github.com/Paramchoudhary/ResumeSkills.git
mkdir -p ~/.cursor/skills
cp -r ResumeSkills/skills/* ~/.cursor/skills/

The set works not only with Claude Code and Cursor. The files are compatible with Windsurf, Gemini CLI, Codex, and a couple dozen other assistants that support the skill format.

After installation, you don't need to enter special commands. Just write a regular phrase in the chat, and the model will activate the appropriate template itself:

"Оптимизируй мое резюме под ATS"
-> активирует resume-ats-optimizer

"Помоги подготовиться к собеседованию в компанию X на позицию Senior Backend"
-> активирует interview-prep-generator

"Сравни офферы от двух компаний с учетом бонусов и опционов"
-> активирует offer-comparison-analyzer

What's the practical value

The main advantage of the repository is that the author spared us from having to reinvent good prompts from scratch. The instructions inside the files are well-written, without unnecessary fluff. They focus the model on specific metrics, action verbs, and structure that automated parsers understand.

The project is distributed under the MIT license, so you can freely modify any files for your own needs, add your own phrasing, or translate the instructions into Russian right in your local folder.

If you actively apply to positions abroad or remote jobs at large companies where ATS filtering happens at the first stage, these skills will save a lot of time. Just keep your resume source in Markdown alongside your codebase and run it through the assistant before each submission.