AIArticle Career-ops runs inside your coding CLI and bets that ruthless filtering beats spraying applications.
Mariana Souza Every few months a job-search automation tool goes viral on GitHub, and the pitch is usually the same: apply to a thousand jobs while you sleep. Career-ops, which has piled up 66,000 stars since its April launch, is interesting because it makes the opposite bet. It's an AI job-search pipeline that refuses to submit anything on your behalf — and its README spends real estate telling you not to apply to most of what it finds.
The other reason it's worth your attention has little to do with job hunting. Career-ops isn't an app. It's a repo of markdown "modes," YAML config, and Node scripts that you drop into whatever AI coding CLI you already run — Claude Code, Codex, OpenCode, Antigravity, Qwen, Grok's CLI. That distribution model is quietly becoming a category, and career-ops is the clearest evidence yet that it works at scale.
A filter, not a cannon #
The design center is a structured evaluation: blocks A through F score a listing across five weighted dimensions — role fit against your actual CV, level strategy, comp research, and so on — into a 1.0–5.0 score. A separate block G assesses posting legitimacy (scams, ghost jobs, suspicious reposts) without touching the numeric score, and an explicit no-sponsorship line in a JD gets flagged as a hard blocker. The system's stated policy: don't apply below 4.0 out of 5.
Creator Santiago Fernández de Valderrama Aparicio built it during his own search and published the funnel: 740 listings evaluated, 68 applications sent, 12 interview processes, one signed offer — a Head of Applied AI role. The story got picked up by Business Insider and WIRED's Greek edition, which explains some of the star velocity. One person's funnel isn't a benchmark, but the ratio is the argument: the tool exists to collapse 740 into 68, not to inflate 68 into 740.
That's a direct repudiation of the previous generation. Auto-apply tools — AIHawk went viral in 2024 doing exactly this for LinkedIn — fed an arms race where candidates spray AI-written applications and companies deploy AI screeners to cope, degrading the signal for everyone. Career-ops drafts cover letters, application emails, and even LinkedIn outreach messages, but every one dead-ends at a human approval gate. It never sends, submits, or clicks. Whether that's principle or liability management, it's the right call: the spray-and-pray tools are why hiring pipelines are drowning, and a filter only delivers value while it stays a filter.
What running it actually looks like #
Adoption is one command — npx @santifer/career-ops init
— which scaffolds a project directory. You cd
in, open your coding CLI, and the system onboards you conversationally: CV, target roles, preferences. From there you paste a job URL to trigger the full pipeline (evaluation, tailored ATS-friendly PDF via Playwright, tracker entry), or run /career-ops scan
to sweep portals. Batch mode fans out headless workers with claude -p
or opencode run
to evaluate ten-plus listings in parallel. A Go/Bubble Tea TUI gives you a dashboard over the pipeline, and analysis scripts surface rejection patterns and per-ATS advance rates from your own data.
One architectural choice deserves credit: instead of scraping LinkedIn (fragile, ToS-hostile), the scanner leans on the ATS layer — Greenhouse, Ashby, Lever, Wellfound — where job data is served from stable, public endpoints, with 55-plus provider modules and 100-plus companies preconfigured. That's the difference between a tool that breaks monthly and one that's shipped 20 npm releases since April and was pushed to again today.
The trade-offs are real, though. Deep-evaluating hundreds of listings through a frontier model isn't free; the docs acknowledge this with a whole running-on-a-budget guide covering OpenRouter free tiers, Ollama, and any OpenAI-compatible endpoint. Privacy cuts the same way: your CV, salary expectations, and interview history flow through whatever model API you've wired up — local-first architecture, but only as private as your model endpoint. And the quality of block-A-through-F judgments is bounded by how much context you feed it; the README is refreshingly honest that "the first evaluations won't be great."
The real story is the runtime #
Here's the part that outlasts the job-search niche. Career-ops has no backend, no auth, no SaaS tier. Its "runtime" is your coding agent: the markdown modes are prompts, the scripts are tools, Playwright is the effector, and Claude Code (or any agent-skill-compatible CLI) supplies the reasoning loop, the sub-agent orchestration, and the billing relationship. The maintainer wrote a domain expert system; Anthropic, OpenAI, and Google operate the infrastructure. Sixty-six thousand stars says developers will happily adopt serious software shipped this way.
Expect this shape everywhere: agent-native skill packs for personal finance, immigration paperwork, freelance ops — verticals where the logic is judgment-heavy, the data is personal, and a hosted service would be a privacy and liability nightmare. The losers are the VC-backed "AI job copilot" SaaS products, which now compete with a free MIT-licensed tool that's more capable and keeps your data on your disk.
The catch is who gets to use it. Career-ops assumes a terminal, Node, and a paid coding-CLI subscription (or the patience to wire up local models) — which means the candidates best equipped to out-filter AI screeners are the ones who least need help. And its core advantage is self-limiting: the 4.0-threshold discipline works because most applicants haven't got it. If tools like this become as common as their auto-apply predecessors, hiring signal degrades again and the arms race resumes one level up. For now, though, if you're a developer job-hunting in this market, this is the rare viral repo where the hype and the utility actually line up. Just read the drafts before you hit send — that gate is the whole point.
Sources & further reading #
[santifer/career-ops](https://github.com/santifer/career-ops)— github.com -
[career-ops: Open-source AI job search agent, local-first](https://career-ops.org/)— career-ops.org -
[career-ops: How I Built My Own AI Job Search Tool](https://santifer.io/career-ops-system)— santifer.io -
[How I built a tool to filter job listings and landed a Head of AI role](https://www.businessinsider.com/how-i-built-tool-filter-job-listings-landed-head-ai-2026-4)— businessinsider.com -
[@santifer/career-ops](https://www.npmjs.com/package/@santifer/career-ops)— npmjs.com
[Mariana Souza](https://sourcefeed.dev/u/mariana_souza)· Senior Editor
Mariana covers the fast-moving world of machine learning and generative AI, with a particular focus on how these technologies are reshaping development workflows. When she isn't stress-testing the latest foundation models, she's usually at a local hackathon.
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