Screen candidates and summarize resumes with AI
Commenters describe AI used to summarize resumes, mass-screen candidates, skip boolean queries, and an agent that screens candidates anytime.
Done with a custom-built or unnamed agent
- What they did
- Commenters said the common live use is lighter-weight than the hype: AI summarizes resumes, helps skip or build Boolean sourcing queries, mass-screens obvious low-fit applicants, and in one case ranks CV batches against job descriptions with match analysis. One recruiter described a homegrown setup that grabs screening info from the page, sends it to a local Ollama qwen3:8b model, auto-navigates past poor matches, and leaves the filtered shortlist for manual review.
- How it went
- The thread’s overall verdict is mixed: several recruiters said AI still screens resumes poorly, and one company’s AI phone-screen pilot bombed. The clearest positive result reported was the local-screening workflow, where about 90% of the candidates it separated were said to be good fits.
- Worth knowing
- The main catch is that recruiters in the thread still kept humans in the loop for final review; even the strongest example used AI mostly to clear shortlist noise, not make hiring decisions.
Try it yourself with Claude Cowork
I have resumes in [folder path] for the role of [job title]. Read each one and make a table with candidate name, years of relevant experience, key skills, and a match rating against these requirements: [must-haves and nice-to-haves]. Sort by fit, flag any you are unsure about with a reason, and stop once the table is saved as [spreadsheet name]. Do not email any candidates.
2 more accounts of this
Source: Reddit · Mar 6, 2025
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