Glossary

Every term career-ops uses, defined in one place. Terms link to the deeper reference where one exists.

ATS (Applicant Tracking System)

The software companies use to receive, store, and filter job applications — Greenhouse, Ashby, and Lever are common examples. career-ops reads their public job APIs to discover openings and can pre-fill their application forms, but never auto-submits.

A–F score

The grade career-ops assigns a job listing after evaluating it against your CV and profile across multiple dimensions. An A means apply now with high conviction; an F means the listing fails your own criteria. The scoring rubric is public on the methodology page.

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

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

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

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

A focused, prompt-defined workflow that career-ops ships as a plain Markdown file your AI CLI executes — scan, apply, tracker, interview/practice, and a dozen more. Modes are inspectable text, not black-box code.

Pipeline

The end-to-end career-ops workflow: scan portals → evaluate listings A–F → tailor CV → apply → track → prepare interviews. Each stage is a mode you can run independently or as a batch.

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

The AI coding CLI that executes career-ops prompts — Claude Code, Gemini CLI, Codex, Qwen Code, OpenCode, or GitHub Copilot CLI — or any OpenAI-compatible endpoint. career-ops is engine-agnostic and runs on free engines too.

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 updates it after every real interview, so preparation compounds across rounds.

STAR+R story

An interview story structured as Situation, Task, Action, Result, plus Reflection — the format career-ops uses in its story bank so behavioral answers are concrete, quantified, and reusable across interviews.

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

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

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

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.