get-job

Researches target roles, tailors resumes, and generates round-by-round interview preparation files.

567|18|Updated May 29, 2026
One-click install
npx skills add https://github.com/agentenatalie/get-job.skill --skill get-job-agentenatalie
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: get-job
Source: https://github.com/agentenatalie/get-job.skill
Command: npx skills add https://github.com/agentenatalie/get-job.skill --skill get-job-agentenatalie

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires python-docx, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve? Job seekers often have real experience that looks unrelated to their target role, or polished resumes that collapse under interview follow-up questions. This Skill runs the full job-search pipeline—role research, resume tailoring, and interview preparation—so every stage's output feeds the next without fabricating experience. ## Core Features & Use Cases - Role Research via WebSearch: Identifies the target market, searches local job boards and interview-experience sources with A/B/C/D source grading, and produces a role research document covering core capabilities, hidden requirements, and interview stages. - Resume Tailoring with DOCX Generation: Works backward from the JD's 3-5 core capabilities, translates real experience into role-relevant language, and renders a formatted resume via scripts/generate_resume.py. - Round-by-Round Interview Preparation: Generates an interview prep folder with bullet-by-bullet deep dives, self-introduction keyword cards, project walkthrough scripts, risk maps, and a post-interview review question bank—only for confirmed or high-confidence interview rounds. - Use Case: A humanities student applying for an AI product internship pastes their resume and the JD; the Skill researches the role, rewrites the resume around transferable capabilities with honest evidence boundaries, and prepares per-bullet answers with fallback lines for tough follow-ups. ## Quick Start Paste your resume and target job description into the chat and ask the agent to research the role, tailor your resume, and prepare you for the interview.

Frequently Asked Questions about get-job

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I tailor my resume to a specific job description?

Resume tailoring works backward from the JD: extract the 3-5 core capabilities the role requires, then rewrite your real experience bullets in that language. This Skill automates the process and renders the result as a formatted DOCX using python-docx.

How to prepare for interviews based on my resume bullets?

Interview preparation starts by numbering every resume bullet, then writing a 30-second spoken explanation, likely follow-up questions, evidence details, and fallback answers for each. The Skill generates these as structured Markdown files organized by confirmed interview rounds.

Can I use this if my resume is already submitted?

Yes, submitted resumes skip the tailoring stage entirely. The Skill prepares interview answers based on your current resume, including honest fallback phrasing for claims you cannot fully support under follow-up questioning.

What happens if I don't have the job description?

Missing JDs trigger an automatic fallback: the Skill searches for the official careers page or comparable listings first. If nothing reliable is found, it proceeds with a generic role profile clearly marked as low confidence rather than inventing requirements.

Does the resume generator support English resumes?

Yes, the generate_resume.py script accepts a locale field set to "en" for English labels and fallback text. Chinese remains the default, and the script strips internal drafting labels from bullets in both languages.

Will this skill exaggerate or invent experience on my resume?

No, fabrication is a hard red line: education, employment dates, and job titles are never altered, and unsupported claims are downgraded, blurred, or deleted. Numbers you cannot explain are marked as risks with honest fallback phrasing for interviews.