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AI for Job Search

Resume & ATS Optimisation

Your resume has one job: survive the first filter. That filter is usually a parser looking for specific titles, tools, and phrases pulled straight from the job description. It does not know that “owned the migration” and “led platform migration” are the same thing. So a strong candidate gets dropped for a vocabulary mismatch, and you never find out why. Rewriting the document by hand for every role is the obvious fix and the reason most people stop after four applications.

Skynet keeps your career in unified memory — every role, project, number, and tool you have ever listed. When you point it at a posting, it does not start from a blank page. It picks the evidence that actually fits that role, phrases it in the posting’s terms, and shows you what it changed and why.

How it works

step 01

Load your history once

Give Skynet your existing resume, your LinkedIn export, old project notes — whatever you have. It builds a single memory of your career, so you never retype the same job description again.

step 02

Point it at the posting

Paste the job ad. The agent pulls out the required skills, the exact keywords, and the seniority signals, then maps them against what is already in your history.

step 03

Review the rewrite

You get a tailored version with the changes marked: which bullets were reordered, which phrasing was swapped, which claim came from where. Nothing is invented — if the posting wants something you have not done, the agent tells you instead of writing it in.

step 04

Reuse it for the next one

The next posting takes a minute, not an evening. Your history stays in memory; only the targeting changes.

Build it from a prompt

Describe the job once and let the agent handle each new posting.

What you get is a resume per application without an evening per application. Every version is built from things you actually did, worded so the filter can recognise them — and you see each edit before it ships.

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