Browse / Job hunt / ai-job-search-os
ai-job-search-os
Xiao-yun-Hu
A measurable conversion-funnel system for AI/Applied AI/Solutions job search.
Open Xiao-yun-Hu/ai-job-search-os → Checked against the four rules.
Works with any agent
ships AGENTS.md, the cross-tool instruction file.
In its own words
Built for offer conversion rate, not application volume. Match Function v0 + 5-type memory + scheduled tasks for Claude Code. MIT.
An AI-powered operating system for improving job-search conversion rates — from job discovery to fit matching, application strategy, funnel tracking, and weekly optimization.
Most job-search tools help candidates find more jobs or generate more resumes.
AI Job Search OS focuses on a different problem:
How do you systematically increase the probability of getting a high-quality offer?
It supports the full job-search loop:
- Collect job and company data
- Parse the candidate profile and resume
- Match candidate × job × company fit
- Prioritize the best opportunities
- Generate application and outreach strategy
- Track funnel progression
- Learn from outcomes and improve the next round
Job search is not a volume game. It is a conversion optimization problem.
Who Is This For
This system can be used by:
- Fresh graduates trying to identify realistic entry paths
- Career switchers trying to reposition themselves
- Mid-career professionals seeking a better-fit next role
- Senior candidates who want to avoid wasting time on low-fit opportunities
- Anyone who wants to treat job search as a measurable funnel instead of random applications
It is especially useful if you are applying across many roles, companies, or regions and need a structured way to decide:
- Which jobs are worth applying to
- Which companies are worth direct outreach
- Which applications are wasting time
- Where your funnel is breaking
- What to change next week
The repository's own description, then 20% of its README. Read the rest on GitHub ↗
Read from the repository's git tree on 2026-08-17.