notanATS

Enables AI agents to discover postings from watched company boards ranked by keyword fit, tailor and fact-check resumes against a source-cited profile, render ATS-readable PDFs, submit applications through the user's own browser, and track replies and follow-ups. All ranking, claim checking and email classification run as plain code, so LLM tokens are spent only on writing.

Enables an AI agent to research industries and companies, track job postings and deadlines from recruiting sites and spreadsheets, match roles against a personal profile, manage application stages, and fill in application forms up to but not including submission. All data stays in a local folder on the user's machine, with reversible actions and confirmation cards for consequential steps.

Enables AI agents to search, score, and track job applications from ATS boards via MCP, with explainable matching and an append-only application history.

Exposes the entire job-search workflow as 13 chat-driven tools, letting users refresh a ranked feed of live postings from employers' public hiring APIs, view and score job descriptions, tailor and render truthful one-page LaTeX resumes, review skill gaps and screening answers, and log applications. It keeps a human in the loop, since nothing is ever submitted automatically.