AI screens candidates; Career-Ops gives candidates AI to choose companies. Instead of manually tracking dozens of postings, the project uses small agent teams to read JDs, score fit across weighted dimensions, and produce customized, ATS-friendly CVs and tracker entries you can review and send.
What Sets It Apart
- Agent-first pipeline that reads your cv.md and reasons about fit (not keyword matching), then produces a role summary, gap analysis, and a tailored PDF — so each output is specific to the job rather than a generic resume.
- Parallel, batch processing (claude/gemini integrations) plus Playwright portal scanners for Greenhouse/Lever/Ashby, enabling dozens of evaluations in one run — so you can triage many listings without manual copy-paste.
- Built-in negotiation guidance, STAR story bank, and pipeline integrity checks — so the system helps beyond discovery (prep, personalization, and tracking).
- Local, CLI-centered design (Node + Go TUI) that keeps your CV and data on your machine and fits developers who prefer terminal workflows.
Who it's for & tradeoffs
Great fit if you frequently evaluate many engineering/AI roles, want per-role ATS PDFs, and prefer a terminal/CLI workflow you can customize. Look elsewhere if you need a hosted SaaS, a GUI-first applicant tracker, or zero-config, out-of-the-box perfection — initial evaluations require onboarding (your CV, proof points, and preferences) for best results.
Where it fits
Use Career-Ops as a candidate-side filter and automation layer: it replaces spreadsheet triage and repetitive resume edits with an agentic pipeline that narrows hundreds of listings to the handful worth applying to. It complements—rather than replaces—human review and outreach.
