An AI-powered Job Search System built on Claude Code
Career-Ops turns Claude Code into an AI job search pipeline. 12 modes, A-F scoring across 10 dimensions, ATS-optimized CVs per listing, batch processing of 122 URLs in parallel. 631 evaluations, 68 apps sent. Open source, 9.1K GitHub stars.
@santifer built a multi-agent job search system on Claude Code, ran 631 evaluations, sent 68 applications, and landed a Head of Applied AI role, then open-sourced it.
The problem: searching for senior AI roles is a full-time job. Read the JD, map your skills, rewrite the CV, fill 15-field forms, multiplied by 10 offers a day. 74% of offers are a poor fit. You find out after reading 800 words. The solution: Career-Ops automates the analysis. You make every final call.
What stands out:
🎯 A–F scoring across 10 dimensions with role match gate filters
📄 ATS PDFs per role with keywords, reordered bullets, auto region
🔍 Scanner across 45+ companies like Ashby, Lever, Wellfound
⚡ Batch mode runs 122 URLs in parallel with worker architecture
🧠 Story Bank builds reusable STAR+R interview answers
💰 Negotiation scripts for salary, geo gaps, competing offers
📊 Dashboard with filters, sorting, and lazy previews
🔁 680 URLs deduped with human review before submission
By the numbers:
631 evaluations, 516 unique offers
354 PDFs generated
68 applications sent
9.1K GitHub stars, 1.6K forks
Different because this isn't an auto-apply bot, it's a filter. The design principle is explicit: automate analysis, not decisions. And the system itself is the portfolio, building a multi-agent architecture to land multi-agent roles is the most direct proof of competence.
Perfect for senior engineers, AI practitioners, and anyone running a serious, high-signal job search at scale.
P.S. I hunt the latest and greatest launches in tech, SaaS and AI, follow to be notified →@rohanrecommends
@santifer built a multi-agent job search system on Claude Code, ran 631 evaluations, sent 68 applications, and landed a Head of Applied AI role, then open-sourced it.
The problem: searching for senior AI roles is a full-time job. Read the JD, map your skills, rewrite the CV, fill 15-field forms, multiplied by 10 offers a day. 74% of offers are a poor fit. You find out after reading 800 words. The solution: Career-Ops automates the analysis. You make every final call.
What stands out:
🎯 A–F scoring across 10 dimensions with role match gate filters
📄 ATS PDFs per role with keywords, reordered bullets, auto region
🔍 Scanner across 45+ companies like Ashby, Lever, Wellfound
⚡ Batch mode runs 122 URLs in parallel with worker architecture
🧠 Story Bank builds reusable STAR+R interview answers
💰 Negotiation scripts for salary, geo gaps, competing offers
📊 Dashboard with filters, sorting, and lazy previews
🔁 680 URLs deduped with human review before submission
By the numbers:
631 evaluations, 516 unique offers
354 PDFs generated
68 applications sent
9.1K GitHub stars, 1.6K forks
Different because this isn't an auto-apply bot, it's a filter. The design principle is explicit: automate analysis, not decisions. And the system itself is the portfolio, building a multi-agent architecture to land multi-agent roles is the most direct proof of competence.
Perfect for senior engineers, AI practitioners, and anyone running a serious, high-signal job search at scale.
P.S. I hunt the latest and greatest launches in tech, SaaS and AI, follow to be notified → @rohanrecommends