I’m building a "Pessimistic" AI Job Evaluator to detect domain mismatches and stealth-startup risk (v0.1.8)

Most "AI Job Matchers" have a major hallucination problem: they are way too optimistic. They see two matching keywords and give you a 95% score, ignoring the fact that a Web Dev probably shouldn't be applying for a Senior Embedded Engineer role.

I’m building Job Bro to act more like a cynical hiring manager. I just pushed v0.1.8, focusing on Domain-Aware Scoring and Risk Detection. ### What’s new in the logic:

• The "Domain Mismatch" Cap: The evaluator now identifies the job's primary technical domain (Fintech infra, ML platform, Hardware, etc.) and compares it against demonstrated experience. If the domain doesn't exist in your resume, the fit score is hard-capped at ≤0.5, regardless of your seniority or titles.
• Stealth & Seed Risk Detection: It now automatically flags stealth_no_diligence (companies with no public footprint) and seed_stage_comp_risk (high equity/low cash alerts).
• Salary-Aware Risk: For senior/exec roles, it flags "Founding" titles with no disclosed comp as a medium risk for high-comp-floor candidates.
• The "Maybe" Verdict: If the skill match is low, the system is now hard-coded to never give a "Strong Apply" verdict. No more false positives.

The Technical Goal:

I’m aiming for sub-50ms feedback loops for the agentic interface because nobody wants to wait for a spinning wheel while job hunting. The goal is to move past "keyword matching" and into "contextual reasoning."

I'd love to get this community's thoughts: What are the "hidden" signals you look for in a JD that most AI tools currently miss? I'm looking to add more risk categories in v0.1.9.

Github: aeroxy/job-bro