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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →The workflow described by Shubham Kumbhalkar scans seven job platforms, scores openings against his resume, drafts application materials for promising matches, and sends them to Slack for review. It automates discovery and preparation—not applications: Kumbhalkar says a person decides whether to apply and completes each application.
What the seven-board workflow does
In his September 16, 2026 account, Kumbhalkar describes a daily scan across Lever, Greenhouse, Ashby, SmartRecruiters, Workday, Indeed and Google via JobSpy, and Hiring.cafe. The system scores openings against his resume on a 0–100 scale using a language model. When a role clears a threshold he selects, it drafts a tailored cover letter and a response to “why this company,” then sends a Slack card containing the application link. Read Kumbhalkar’s account.
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The Slack workflow also lets him mark a role as applied or request interview preparation, including STAR stories mapped to likely questions. A weekly report summarizes jobs seen, scored and applied to. These are features he reports for his own setup, not independently verified performance results.
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The system does not submit applications. Kumbhalkar writes, “The tool does not auto-submit applications. That was deliberate.” He says the person chooses whether a role is worth pursuing and completes the application. In practice, that keeps the workflow focused on surfacing and preparing opportunities while leaving decisions and final submission to the job seeker.
How the author says it is put together
The implementation is described as Python, with a thin client for each platform and a pluggable model backend. It deduplicates listings by job ID and by title plus company, applies a per-company cap, balances results across platforms, and runs a scheduled daily scan. A Slack bot watches reactions to support status tracking and interview-prep requests.
The public repository’s README describes the project as a Python CLI and documents profile configuration, optional JobSpy installation for Indeed and Google scraping, daily scans, weekly reporting, and the other features above. It lists an MIT license. That documentation describes the repository; it does not establish that setup or compatibility has been independently tested or remains unchanged.
Choosing a model backend
The author names a local model and the Anthropic API as options. He characterizes the local approach as free and the API as faster; those are his descriptions, not a current price comparison or measured speed test. The sources do not provide benchmark results for latency, cost, or matching quality.
For someone adapting the approach, relevant trade-offs include local setup and computing requirements, recurring API usage costs, response latency, and how resume and job-description data are handled under the chosen model’s privacy terms. Check the current provider documentation and terms before sending personal information; the project description does not establish one backend as best for every user.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the author says worked for him—and what remains unmeasured
Kumbhalkar reports that comparing openings with his actual resume reduced noise for him compared with relying only on keywords. He also says the Slack card-and-reaction loop helped him keep up with the workflow, and that retaining a human decision step made the tool trustworthy to him. These are personal observations, not controlled findings.
His account and the repository documentation do not report time saved, application totals, callback or interview rates, job offers, or a comparison with manual searching. The workflow therefore illustrates one way to organize a job search; it does not show that automation improves hiring outcomes.
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