# Glossary Source: https://career-ops.org/docs/reference/glossary (canonical HTML, identical content) > The vocabulary of an AI-powered job search, defined — ATS, the 1.0–5.0 global score, Data Contract, spray-and-pray, liveness check, tailoring, zero-token scan, STAR+R stories, and every term career-ops uses. Every term career-ops uses, defined in one place. Terms link to the deeper reference where one exists. ## ATS (Applicant Tracking System) [#ats-applicant-tracking-system] The software companies use to receive, store, and filter job applications — [Greenhouse](https://career-ops.org/docs/reference/portals/greenhouse.md), [Ashby](https://career-ops.org/docs/reference/portals/ashby.md), and [Lever](https://career-ops.org/docs/reference/portals/lever.md) are common examples. career-ops reads their public job APIs to discover openings and can pre-fill their application forms, but never auto-submits. ## Global score (1.0–5.0) [#global-score-1050] The LLM's holistic judgement of a job listing across five dimensions (match, north-star alignment, comp, cultural signals, red flags), producing a score from 1.0 to 5.0. At 4.5+ the agent recommends applying immediately; below 4.0 it recommends against applying. There is no closed-form weighting formula: the score is the LLM's judgement given the rubric, which is public on the [methodology page](https://career-ops.org/methodology). ## Data Contract [#data-contract] career-ops's core architectural promise: the **system layer** (scripts, modes, templates) is updatable at any time, while the **user layer** — `cv.md`, `config/profile.yml`, `data/`, `reports/`, `output/` — is never touched by an update. Your data outlives every version. ## Spray-and-pray [#spray-and-pray] The mass-application strategy of sending the same CV to hundreds of jobs with no targeting. career-ops explicitly rejects it: the pipeline evaluates and ranks listings first, so effort concentrates on the small set of roles worth pursuing. ## Liveness check [#liveness-check] A heuristic career-ops runs to detect whether a job listing is still open before you spend time or tokens on it. It is a best-effort signal — expired postings sometimes linger on job boards — and listings can be marked manually when the heuristic is wrong. ## Tailoring [#tailoring] Rewriting your CV for one specific job listing — reordering emphasis, surfacing matching skills and metrics, aligning vocabulary with the job description — while keeping every claim truthful to your real experience. career-ops generates a tailored CV per application as Markdown you can edit. ## Mode [#mode] A focused, prompt-defined workflow that career-ops ships as a plain Markdown file your AI CLI executes — [scan, apply, tracker](https://career-ops.org/docs/reference/modes.md), [interview/practice](https://career-ops.org/docs/introduction/guides/interview-modes.md), and a dozen more. Modes are inspectable text, not black-box code. ## Pipeline [#pipeline] The end-to-end career-ops workflow: scan portals → evaluate listings (1.0 to 5.0) → tailor CV → apply → track → prepare interviews. Each stage is a mode you can run independently or as a batch. ## Zero-token scan [#zero-token-scan] A discovery run that consumes no LLM tokens: `scan.mjs` calls ATS APIs (Greenhouse, Ashby, Lever) directly over HTTP, so finding new listings is free regardless of which AI engine you use. ## AI engine [#ai-engine] The AI coding CLI that executes career-ops prompts — Claude Code, OpenCode, Codex, GitHub Copilot CLI, and [other supported CLIs](https://career-ops.org/docs/supported-clis.md) — or any OpenAI-compatible endpoint. career-ops is engine-agnostic and [runs on free engines too](https://career-ops.org/docs/free-ai-engine.md). ## Question bank [#question-bank] A local Markdown file (`interview-prep/question-bank.md`) where career-ops accumulates real interview questions and your performance on each (✅/🟡/🔴). The [interview/debrief mode](https://career-ops.org/docs/introduction/guides/interview-modes.md) updates it after every real interview, so preparation compounds across rounds. ## STAR+R story [#starr-story] An interview story structured as **S**ituation, **T**ask, **A**ction, **R**esult, plus **R**eflection — the format career-ops uses in its story bank so behavioral answers are concrete, quantified, and reusable across interviews. ## Story bank [#story-bank] A local Markdown file (`interview-prep/story-bank.md`) of your prepared STAR+R stories. Practice sessions verify answers against it, and debriefs extract new stories from what you actually said in real interviews. ## Portal [#portal] A company career site backed by an ATS that career-ops can scan. Your tracked portals live in `portals.yml`; the scanner reads their public APIs on every run. ## Batch evaluate [#batch-evaluate] Scoring many saved listings in one run instead of one at a time, with flags to keep control: `--limit` caps the batch size, `--dry-run` previews what would be processed, and `--resume-paused` continues an interrupted run without re-spending tokens. ## Local-first [#local-first] The career-ops architecture principle: everything — your data, the prompts, the scripts, the AI CLI — runs on your machine. There is no hosted backend, no account, and nothing to sign up for. ## CareerOps [#careerops] CareerOps is the practice of running a job search the way engineers run production: with evidence, with discipline, and with tools on the candidate's side of the table. The term names the practice, not a product; career-ops is its first reference implementation. Coined by Santiago Fernández de Valderrama Aparicio in [The CareerOps Manifesto](https://career-ops.org/manifesto) (July 14, 2026).