A Redditor going by u/OminousLatinWord posted this one under the title “ChatGPT Work can job search now,” and it racked up 62 upvotes fast. The idea is simple: stop scrolling job boards yourself and let ChatGPT run the whole search chain instead. Anyone who’s spent a Sunday afternoon opening forty browser tabs to compare listings will get why this struck a nerve.
What The Prompt Actually Does
The prompt tells ChatGPT to clone a specific open-source project, open-jobs by developer elliottdehn, and use its built-in job-search tool chain. From there, the model pulls the top 200 listings you’re eligible for and builds a shortlist. No manual sorting required.
The twist is the JD embedding step. The prompt authorizes a remote service to embed your “ideal job description” so the tool can match listings against what you actually want. If ChatGPT doesn’t know enough about your background to write that JD, it’s told to ask you questions first. That back and forth matters more than it sounds like on paper. If you’ve never sat down and written out what your ideal role actually looks like, in terms of seniority, industry, team size, remote policy, the JD step forces that clarity before a single listing gets pulled. Skip that step or answer vaguely, and the shortlist that comes back will be just as vague.
Why This Prompt Works
Three techniques are stacked here, and each one matters.
- Tool assignment: instead of asking ChatGPT to “help me find a job” in the abstract, the prompt points it at a specific repo and tells it exactly which tool chain to run.
- Explicit constraints: “skip sorting” and “top 200” keep the model from wandering into low-value busywork like ranking every listing by hand.
- Permission handling: the line about authorizing JD embedding heads off a common failure mode where agents stall out asking for consent mid-task.
That last part is the one most people skip when writing agent prompts. If your prompt doesn’t pre-authorize the steps that need outside access, the model either refuses or keeps interrupting you to ask. Think about how often you’ve watched an agent grind to a halt because it wanted a “yes” before touching a file, an API, or a third-party service. Baking the permission into the prompt itself removes that friction entirely, and it’s a technique worth stealing for other agent workflows too, not just job hunting. Anywhere you’re asking a model to chain multiple tools together, front-loading the approvals saves you from babysitting the run.
The Catch
The original poster is upfront about the cost. Cloning a repo, running a full tool chain, and processing 200 listings burns through tokens fast. Their advice: use Luna instead of a lighter model, or you’ll hit your limit in about 20 minutes. That’s not a small warning. Running a repo clone plus a multi-step tool chain against 200 listings is closer to a heavyweight coding task than a quick chat, so treat it like one.
They also warn you might need to hit “retry” a few times mid-run. Don’t panic when that happens. According to the poster, no progress gets lost when you retry, the task just picks back up. If you’re the type who watches a long-running process and assumes the worst the second it stalls, this is worth remembering: a stall here is a checkpoint, not a crash.
One commenter, u/AutoModerator, asked people to share what they used the prompt for and to post any tweaks that improved results. That’s worth watching if you try this yourself. Small changes to the eligibility criteria or the JD questions could shift the shortlist quality quite a bit, and early threads like this tend to accumulate the best variations within a few days as more people test edge cases.
Use Cases
- 🎯 Job seekers applying to dozens of roles who want a first-pass filter instead of reading every listing themselves.
- 🔄 Career changers who need the model to ask clarifying questions before it can even define what “ideal” looks like.
- ⏱️ Anyone tired of manually cross-referencing job boards against their own resume every single week.
If you’re only casually browsing, this prompt is overkill. But if you’re in an active search and applying at volume, automating the first filter pass saves real hours, hours you can put toward tailoring cover letters or prepping for interviews instead.
Prompt of the Day
Clone https://github.com/elliottdehn/open-jobs and use the job searching tool chain to generate a shortlist of jobs for me to apply to. Skip sorting. Just read the top 200 that I’m eligible for and give me the shortlist. I authorize my ideal JD to be embedded by a remote service. Ask me questions if you don’t know enough about me to craft a JD.
A quick variation worth trying: add a line capping the shortlist at a specific number, like 15 or 20 jobs, so you’re not staring at 200 results after all that processing. You could also specify “remote only” or a location filter directly in the prompt instead of letting the JD carry all the weight. If you’re targeting a specific salary band or industry, spell that out too rather than hoping the JD questions cover it.
Before you run this yourself, budget your tokens and pick a capable model. This isn’t a lightweight ask, it’s closer to handing ChatGPT a part-time research job. Head over to the original thread on r/ChatGPTPromptGenius to see what other users found when they ran it, and drop your own tweaks if you improve on it.
ChatGPT Work can job search now.
by u/OminousLatinWord in ChatGPTPromptGenius