FAQ
Frequently asked questions about career-ops — cost, privacy, supported AI CLIs, scan vs scan:full, token limits, Windows setup, and what career-ops will never do on your behalf.
Common questions, answered in one place. For anything not covered here, ask in Discord or open a GitHub Discussion.
Is career-ops free?
Yes. career-ops is open source under the MIT license, forever. The only potential cost is the AI engine it runs on — and that can be free too: OpenCode with a free provider, a local model via Ollama, or the built-in npm run or runner on OpenRouter free models. The Set up a free AI engine guide walks through every path.
Where does my data live?
On your machine, in plain files you own — your CV, profile, pipeline and reports are local Markdown/YAML. Nothing runs on career-ops servers. System updates never touch your data layer (cv.md, config/, data/, reports/, output/): that separation is the Data Contract, and every update honors it.
Which AI coding CLIs does career-ops work with?
Claude Code, Gemini CLI, Codex, Qwen Code, OpenCode, and GitHub Copilot CLI. career-ops is AI-agnostic: it ships prompt files that the CLI executes, so you can also point it at any OpenAI-compatible endpoint or a local model with zero code changes.
What is the difference between scan and scan:full?
npm run scan reads the companies you configured in portals.yml and hits their ATS APIs (Greenhouse, Ashby, Lever) directly, consuming zero LLM tokens — that's your regular daily or weekly discovery run. npm run scan:full inverts the direction: it walks public ATS company directories and surfaces fresh postings that match your title_filter / location_filter, so you catch roles from companies you haven't manually added.
How do I avoid hitting token or rate limits during a batch run?
Cap the run with ./batch/batch-runner.sh --limit 5 to inspect output quality before committing to a larger batch. If a run gets interrupted by a rate limit or network error, don't restart from scratch — use --resume-paused to skip already-completed jobs so no tokens are wasted on work that finished.
Skills aren't loading on Windows — symlink error on install
Windows does not create symlinks by default, so Git checks out the CLI skill entrypoints as plain pointer files. The installer and updater detect this automatically: run node update-system.mjs apply (or npx @santifer/career-ops init on a fresh install) and the materialize step replaces the pointer files with the full skill content. No manual mklink or Developer Mode changes needed.
Can I run career-ops on a cheaper or local model?
Yes — career-ops is fully AI-agnostic. The core repo's Running on a Budget guide covers OpenCode, Qwen CLI, DeepSeek, OpenRouter, Ollama and other low-cost or local providers, with recommended model sizes (32B+ for reliable scoring) and token-saving practices.
Does career-ops auto-submit applications?
No. career-ops prepares — it scans, scores, tailors your CV, and can pre-fill ATS application forms — but submitting is always your explicit click. The project explicitly rejects spray-and-pray automation: the point is choosing companies well, not applying blindly at volume.
Prepare for Interviews
Use the interview modes — plan, practice, debrief — to prepare for a specific interview round, rehearse it with an AI interviewer, and turn every real interview into intelligence for the next one.
Modes
The 14 user-invocable career-ops modes — evaluate, tailor, apply, track, and prep — each a markdown skill that runs in any supported AI coding CLI.